# To vote, or not to vote? On the epidemiological impact of electoral
campaigns at the time of COVID-19

Davide Cipullo,1∗ Marco Le Moglie2

1Department of Economics, Uppsala University
2Department of Economics and Finance, Catholic University of the Sacred Heart

∗Correspondence to<EMAIL_ADDRESS>
> Elections are crucial for legitimating modern democracies, and giving all
> candidates the possibility to run a proper electoral campaign is necessary
> for elections’ success in providing such legitimization. Yet, during a
> pandemic, the risk that electoral campaigns would enhance the spread of the
> disease exists and is substantive. In this work, we estimate the causal
> impact of electoral campaigns on the spread of COVID-19. Exploiting
> plausibly exogenous variation in the schedule of local elections across
> Italy, we show that the electoral campaign preceding this latter led to a
> significant worsening of the epidemiological situation related to the
> disease. Our results strongly highlight the importance of undertaking
> stringent measures along the entire electoral process to minimize its
> epidemiological consequences.

##  1 Introduction

Elections represent the primary tool for legitimating governments and their
actions. This legitimization is crucial for the correct functioning of any
democracy and becomes even more needed during a pandemic when decisions that
limit individual rights substantially are taken. Yet, holding elections during
a pandemic might enhance the spread of the disease due to different aspects of
the electoral process, among which the candidates’ campaign preceding the vote
arguably represents the most problematic one. On the one hand, the possibility
for all candidates to run a proper electoral campaign for correctly informing
voters about their platform is necessary for the success of elections in
legitimating the winner (?). On the other hand, the candidates’ campaign
induces a high frequency of in-person meetings where social distancing is
challenging to enforce.

In many countries, election schedules concur with the spread of the infection
from SARS-CoV-2 (?), and the related illness known as COVID-19 (?), thus
imposing a trade-off between the accountability of elected officials and
diffusion of the disease. Countries with elections planned in the year 2020
debated on whether to organize or postpone elections (?, ?). Where elections
occurred, a wide range of country-specific precautionary policies was adopted
to limit the contagion and ensure their smooth running (?). Despite the
importance of such a public debate, empirical evidence on the impact of
electoral campaigns on the diffusion of COVID-19 is very scarce and incomplete
(?). One prominent reason is that experimental variation is lacking: countries
where elections did not occur, are not necessarily a reliable comparison group
for countries where elections occurred (?).

In this work, we identify the causal impact of electoral campaigns on the
diffusion of COVID-19 by exploiting a natural experiment taking place in Italy
at the beginning of the second wave of the pandemic. More specifically, on the
same days as a national referendum to amend the constitution, regional
elections took place in seven out of twenty regions. This plausibly exogenous
variation to the intensity of the electoral campaign allows us to compare the
epidemiological outcomes in areas with and without regional elections before
and after the campaign’s start.

We find that the electoral campaign preceding the regional elections did lead
to a significant increase in new infections (7%), percentage of positive tests
(15%), ordinary hospitalizations (24%), entries in intensive care units
(5.3%), and deaths (0.6%) related to the disease. Taken together, our results
inform the debate on how managing elections at the time of COVID-19 in two
critical manners. First, they point towards the existence of a substantive
impact of elections on the spread of COVID-19, thus proving the necessity to
enforce a strict safety protocol to minimize such impact. Second, they show
that a large portion of the epidemiological risk connected to elections is
concentrated during the electoral campaign preceding the vote, when sanitary
precautions are more difficult to enforce than at the polling station.

##  2 Background

Since the first documented case of local transmission on February
20${}^{\text{th}}$, 2020, the ascending number of infected individuals and
that of hospitalizations and deaths related to COVID-19 induced the Italian
government to enforce a strict lockdown over the entire national territory.
The lockdown lasted more than two months (i.e., from March 11${}^{\text{th}}$
to May $17^{\text{th}}$). By the end of May, the number of new daily cases
stabilized around a few hundred. This level remained almost unaltered until
the beginning of August when the number of new infections started to rise
again. By the beginning of October, Italy was again experiencing a substantive
increase in the daily number of new infections that led to a new set of public
interventions to slow down the spread. Figure S.1 in the Supplementary
Material shows a graphical overview of the evolution of daily infections,
deaths, and hospitalized individuals.

On September 20${}^{\text{st}}$ and 21${}^{\text{st}}$, at the dawn of the
second wave of the pandemic, Italy went to the polls. All Italians were asked
in a referendum whether they intended to confirm a constitutional reform to
reduce the number of members of the national parliament. The referendum was
initially scheduled on March 29${}^{\text{th}}$ and then postponed due to the
epidemic.

In the same days, seven out of twenty regions also went to the polls for
renewing their regional president and council because of the regular
expiration of the legislative term, according to an electoral calendar
strictly disciplined by the law (?). Panel (a) of Figure 1 shows that such
regions’ geographical distribution is homogeneous across the country. Similar
to the referendum, regional elections were postponed from their original
schedule dates in the Spring.

Regardless of the type of voting held in each region, the national government
implemented a strict protocol to guarantee safety at the polling stations.
This protocol included physical distancing between different voting booths,
compulsory masks for all voters and workers, and the use of hand sanitizers
before touching the ballot or the voting pencil.

The referendum did not have to achieve any participation quorum to be valid,
thus ultimately narrowing citizens’ incentives to go to the polling station.
Moreover, since the constitutional reform underneath the referendum had the
support of most parties represented in the parliament, the political campaign
carried on in favor and against the confirmation was almost absent, or at
least very mild (?).

Regional elections, instead, were an important battleground. First, many
candidates competed for a regional council seat, which is a powerful and well-
rewarded position. Second, regions are assigned by the constitution the
authority on healthcare policies – which is a vital issue at stake during a
pandemic. Third, the opposition motivated their voters to signal their dissent
to the national government, and, by contrast, members of the government
coalition campaigned in support of their candidates. As a result, candidates
and party leaders run an intense campaign both on the media and in-person. For
instance, panel (b) of Figure 1 reports the average number of national
political leaders’ mentions on regional TV news. The figure shows that, in the
period between the official start of the electoral campaign and the election
days, such number was significantly higher in regions voting for renewing
their government than in the others. Moreover, panel (c) of Figure 1 shows
that the opposition leader sen. Matteo Salvini (Lega) participated in the same
period to a much greater number of public events in the former regions. In
turn, as shown in panel (d) of Figure 1, voter turnout was approximately 20
percentage points higher in regions where both the referendum and the
electoral round took place than in those voting only for the former (?).

##  3 Data

We rely on the daily epidemiological bulletin on the spread of COVID-19 in
Italy, compiled by the Department of Civil Protection (DPC), the governmental
agency responsible for monitoring the disease’s evolution across the country.
From the information contained in the bulletin, we calculate six
epidemiological outcomes measuring the spread of COVID-19, all normalized per
100,000 inhabitants. Specifically, we focus on i) the daily number of new
infections, ii) the daily number of tests performed, iii) the daily percentage
of positive tests, iv) the daily number of individuals currently undertaking
ordinary care in hospitals, v) the daily number of patients currently in
intensive care units (ICUs), and vi) the daily number of deaths.

To interpret coefficients as percentage changes, we perform a logarithmic
transformation of all outcomes. To limit the noise due to delay in reporting,
we use the seven-day moving average within each region. We focus on bulletins
published between June 29${}^{\text{th}}$ and October 15${}^{\text{th}}$ to
keep the comparison across regions as reliable as possible. Indeed, the first
day corresponds to the day recording the minimum number of cases between the
first and the second Italian wave of the pandemic at the national level. In
contrast, regions started to diverge in their adoption of specific
restrictions to limit the second wave on the last day included in the sample.

To ensure that different regions were experiencing similar risks of spread in
the period under consideration, we also employ regional data from the Google
COVID-19 Community Mobility Reports. This data report daily changes in
individual mobility among workplaces, retailing stores, stations, and
residential areas compared to the beginning of the epidemic.

In the Supplementary Material, we assess the robustness of our findings by
employing information on the number of municipal elections held in each region
on September 20-21${}^{\text{st}}$ as well as several geographic, socio-
economic, and demographic characteristics from the Italian National Institute
of Statistics (ISTAT). Appendix S.2 in the Supplementary Material provides a
detailed list and description of all variables employed in our analysis, while
Table S.1, still in the Supplementary Material, shows the summary statistics
for all of them.

##  4 The electoral campaign and the spread of COVID-19

We compare, in a Difference-in-Differences setting (DiD), regions that held
both the referendum and the regional election on September
20-21${}^{\text{st}}$ – the treatment group – with regions only voting for the
referendum – the control group – before and after the official start of the
electoral campaign – 30 days before the vote.

Treatment assignment is plausibly exogenous because different regions vote in
different years depending on their electoral cycle timing that usually repeats
every five years and law disciplines calendars of voting and campaign dates.
Moreover, the national government announced the new election calendar in the
mid of July when the diffusion of COVID-19 was at its minimum level.

In particular we estimate the following linear regression model

$y_{r,t}=\gamma_{0}+\gamma_{1}Campaign_{r}\times
Post_{t}+\zeta^{{}^{\prime}}\textbf{X}_{r,t}+\epsilon_{r,t},\,$ (1)

where $y_{r,t}$ is one of the epidemiological outcomes measured in the region
$r$ at date $t$, $Campaign_{r}$ is a dummy equal to one for treatment regions,
$Post_{t}$ is a dummy equal to one for all dates after the official starting
of the electoral campaign. $\textbf{X}_{r,t}$ is a set of control variables
which include: i) region fixed effects aimed at capturing all the time-
invariant heterogeneity across regions; ii) regional-by-week linear pre-
treatment trends allowing to control for the linear effect of weekly shocks
preceding the treatment and that are specific to each region; iii) adjacent
regions-date fixed effects that control for daily shocks common to adjacent
treatment and control regions. We account for serial and spatial correlations
in the error terms $\epsilon_{r,t}$ using Conley-HAC standard errors (?) with
maximum cutoffs for both spatial and time lags.

The coefficient of interest is $\gamma_{1}$, representing the differential
effect of the electoral campaign between treatment and control regions. Yet,
its causal interpretation relies on the actual comparability between the two
groups of regions, particularly on the assumption that, in the absence of the
electoral campaign, the dynamics of the outcomes would have been the same
between the two groups. Our analysis deals with the validity of this
assumption in different manners. First, in Figure 2 we exploit Google’s data
on individual mobility to show the absence of significant differences in
mobility patterns between treated and control regions over the entire
observational window. This implies that the two groups of regions were indeed
experiencing similar risks of spread in the period under consideration.
Second, by including in our regression adjacent regions-date fixed effects, we
are constantly comparing changes in the outcome of interest between regions
that share part of their border and are thus more comparable. Third, the
inclusion of adjacent region-date fixed effects also helps to rule out the
impact of weekly trends among adjacent regions, such as those related to
summer holidays. Fourth, the inclusion of regional-by-week linear pre-
treatment trends should capture several potential region-specific differences
in the dynamics of the outcomes before the electoral campaign, making
treatment and control regions strongly comparable once the campaign starts.

Lastly, we propose a dynamic specification in which the dummy for the post-
treatment period ($Post_{t}$) is replaced with a set of week dummies to
empirically exclude that the dynamics of the six epidemiological outcomes were
significantly different between treatment and control regions also before the
campaign. This specification also allows us to get further insights on the
dynamics of the effect after the beginning of the campaign. More specifically,
we estimate the following regression model:

$y_{r,t}=\gamma_{0}+\underset{w\in{[-6,8],w\neq{-1}}}{\sum}\gamma_{w}{Campaign_{r}}\times\textbf{1}(Week_{t}=w)+\zeta^{{}^{\prime}}\textbf{X}_{ij}+\epsilon_{r,t}\,,$
(2)

Table 1 presents the estimates for Equation 1. The daily number of new
infections rose by about 7% (p-value=0.037) due to the electoral campaign.
Even if significantly different from zero, this coefficient probably reflects
a lower bound of the electoral campaign’s impact on the number of new
infections, given that the campaign also led to a reduction in the daily
number of tests performed equal to 8.3% (p-value$<$0.001). Since the Italian
health system is under the authority of the regional governments, the
reduction in the number of tests might reflect a simple increase in
uncertainty among regional bureaucracies induced by the electoral process, a
strategical behavior by incumbent governments to secure their re-election, or
rather a combination of the two. Regardless of its actual origins, once we
account for the reduction in testing, the electoral campaign’s epidemiological
impact appears to be sizeable. In particular, the percentage of positive
COVID-19 tests rose by 15% (p-value$<$0.001). The substantive impact further
reflects into a significant increase in the daily number of ordinary
hospitalizations, the number of people hospitalized in ICUs, and deaths
related to the epidemic, which since the start of the former rose by 24%
(p-value$<$0.001), 5.3% (p-value=0.007) and 0.6% (p-value=0.011),
respectively.

Figure 3 plots the estimated coefficients for the interaction terms of
Equation 2. First, we notice the absence of differential pre-treatment
dynamics in all epidemiological outcomes between treatment and control regions
before the campaign’s official start. The absence of pre-treatment dynamics is
crucial to supporting the actual comparability between the two groups of
regions, and ultimately the possible causal interpretation of our results.
Second, the treatment effect dynamics reveal both similarities and differences
across outcomes. For all outcomes, treatment effects become visible with a
certain lag from the beginning of the campaign and become statistically
significant at the 5% critical level only after some weeks. These patterns are
consistent with a cumulative epidemiological effect of the electoral campaign
and the 7-14 days’ average incubation period of the disease (?).

While most of the outcomes stabilize after the fourth post-treatment week, few
others still increase, like the percentage of positive tests and the number of
hospitalizations in ICUs. Concerning the former, its increase after the fourth
week is entirely due to the further reduction in the testing capacity.
Regarding the latter, hospitalizations in ICUs still increase to the sixth
week after the campaign’s start and then finally stabilized. The fourth post-
treatment week corresponds to the week in which the campaign ends, and the
vote occurs. Therefore, this study’s results suggest that elections enhanced
the spread of COVID-19 primarily through the campaign preceding the vote.

In the Supplementary Material, we assess the robustness of the estimates
presented in Table 1 with respect to i) the inclusion of controls for linear
pre-treatment trends in several demographic, social, and economic
characteristics at the regional level (Table S.2); ii) changes in the starting
week of the electoral campaign (Figure S.2); iii) the exclusion from our
sample of one region at the time (Figure S.3); iv) the use of different
spatial and time cutoffs for calculating the Conley HAC standard errors
(Figure S.4). The estimates survive all these tests, reassuring us about the
empirical design’s validity and the causal interpretation of the findings.

##  5 Conclusions

In this study, we have estimated the causal impact of electoral campaigns on
the spread of COVID-19 in Italy. By exploiting variation in the elections
schedule across Italian regions in a Difference-in-Differences setting, we
have found that the electoral campaign increased the spread of COVID-19
significantly. More specifically, our results show that the incidence of the
disease, the number of patients seeking care in hospitals, and the number of
deceased individuals increased more steeply in the treated than in the control
regions at the dawn of the pandemic’s second wave in Italy.

Our study’s findings lead to a number of concluding remarks and policy
recommendations. First, our findings show that the electoral campaign also
negatively impacted testing capacity, which in Italy is under the authority of
regional governments. Our study is agnostic on whether such reduction was due
to the temporary uncertainty or intentional. However, reduced testing capacity
can contribute to the spread of the disease because infected asymptomatic
individuals are not isolated promptly, and their close contacts are not traced
(?, ?). Reduced testing capacity eventually represents an additional channel
through which electoral campaigns can affect the spread of the disease, and
thus to eventually consider for minimizing their epidemiological impact.

Second, the comparison of the estimates’ magnitudes suggests that the
electoral campaign contributed to the spread of the disease more in low-risk
groups than among high-risk individuals. In particular, the number of
hospitalized patients in ordinary units increased by 24%, while the number of
individuals in ICUs and deceased increased by 5.3% and 0.6%, respectively. One
possible interpretation is that individuals belonging to risk groups are less
likely to spend time outside their home location (?) and participate in large
meetings.

Finally, our findings shed light on the negative epidemiological impact of
gatherings during which the enforcement of prevention policies is limited.
Authorities should bear the epidemiological costs of electoral campaigns when
deciding whether to organize elections and deciding which measures to take.
While several prevention measures can be successfully implemented to limit the
spread of the disease at the polling station – distancing, face masks, hand
sanitizers, and postal voting – it remains an open question of how to conduct
electoral campaigns safely.

Figure 1: The Elections of Mid-September

(a) * (b) *

Notes: Red-colored areas and bars refer to regions voting for both the
referendum and the renewal of their president and council, while blue-colored
areas and bars refer to regions voting only for the referendum. Political
leaders included in panel (b) are Di Maio, Meloni, Renzi, Salvini, and
Zingaretti. Turnout measures in panel (d) are computed on September
20${}^{\text{th}}$ at 12pm, 7pm, 11 pm and September 21st at 3pm and refer to
participation in the referendum. In panels (b)-(d), bars represent sample
averages by treatment status while vertical short line over each bar
represents 95% confidence intervals. In panels (b) and (c), the vertical
dashed lines represent the date in which the electoral campaign officially
started (August 21${}^{\text{st}}$, lines on the left) and the election date
(September 21${}^{\text{st}}$, lines on the right).

Figure 2: The Dynamics of Individual Mobility Through Google’s Data

(a) : Workplaces (b) : Retailing (c) : Stations (d) : Residential

Notes: The figures report the average and 95% confidence intervals of the
daily changes in individual mobility compared to February 2020, in treatment
(red line) and control (blue line) regions. In all panels, the vertical dashed
lines represent the date in which the electoral campaign officially started
(i.e. August 21st, lines on the left) and the week in which the election took
place (i.e., September 20-21st, lines on the right).

| (1) | (2) | (3) | (4) | (5) | (6)
---|---|---|---|---|---|---
| Infections | Tests | % Posit. | Hospit. | ICUs | Deaths
Campaign $\times$ Post | 0.070* | -0.083*** | 0.146*** | 0.240*** | 0.053** | 0.006*
| (0.033) | (0.019) | (0.031) | (0.052) | (0.020) | (0.002)
Observations | 2,060 | 2,060 | 2,060 | 2,060 | 2,060 | 2,060
Baseline (value) | 0.315 | 83.578 | 0.404 | 0.904 | 0.0032 | 0.012
Region FE | ✓ | ✓ | ✓ | ✓ | ✓ | ✓
Regional pre-trends | ✓ | ✓ | ✓ | ✓ | ✓ | ✓
Date $\times$ Adjacent reg. FE | ✓ | ✓ | ✓ | ✓ | ✓ | ✓

Table 1: The Impact of the Electoral Campaign on the Spread of COVID-19 Notes:
The table shows the differential impact of electoral campaign between regions
voting both for the referendum and the regional election (treatment) and those
only voting for the referendum (control), as obtained through the estimation
of Equation 1. All dependent variables are the natural logarithm of the seven-
day moving average of the daily number at the regional level. See Section 3
for details. $Campaign$ is a dummy equal to 1 for treatment regions, and 0
otherwise. $Post$ is a dummy equal to 1 for all the dates since August 21st,
and 0 otherwise. Standard errors are robust to serial and spatial correlation
and computed imposing maximum cutoff for both spatial and time lags.
$\dagger$, *,**,*** represent the 10%, 5%, 1%, 0.1% significance levels,
respectively.

Figure 3: The Impact of Electoral Campaign on the Spread of COVID-19: Dynamic
Specification

(a) : New infections (b) : Tests

(c) : % Positives (d) : Hospitalized

(e) : ICUs (f) : Deaths

Notes: The figures report estimates and 95% confidence intervals for the
coefficients of the interactions included in Equation 2, between the dummy
$Campaign$, which is equal to one for treatment regions, and the week dummies.
$week=-1$ is the reference week (i.e., week 33 of 2020). All dependent
variables are the natural logarithm of the seven-day moving average of the
daily number at the regional level. See Section 3 for details. $Campaign$ is a
dummy equal to 1 for treatment regions, and 0 otherwise. Confidence intervals
are based on standard errors that are robust to serial and spatial correlation
and computed imposing maximum cutoff for both spatial and time lags. In all
panels the vertical dashed line represents the date in which the electoral
campaign officially started (i.e. August 21st).

## References and Notes:

## References

  * 1. J. Lake, J. Nie, Did Covid-19 Cost Trump the Election? CESifo Working Paper No. 8856, (2021).
  * 2. World Health Organization, Modes of transmission of virus causing COVID-19: implications for IPC precaution recommendations. Scientific brief 27 March 2020, (2020).
  * 3. International Institute for Democracy and Electoral assistance (IDEA), Global overview of COVID-19: Impact on elections, available at International IDEA’s web page, (2021).
  * 4. International Institute for Democracy and Electoral Assistance (IDEA), Elections and COVID-19, International IDEA Technical Paper 1/2020, (2020).
  * 5. A. Radjenovic, R. Mańko, G. Eckert, Coronavirus and elections in selected Member States. European Parliamentary Research Service Briefings PE651.969, (2020).
  * 6. International Institute for Democracy and Electoral assistance (IDEA), Risk mitigation measures for national elections during the COVID-19 crisis, available at International IDEA’s web page, (2020).
  * 7. B.D. Bernheim, N. Buchmann, Z. Freitas-Groff, S. Otero, The Effects of Large Group Meetings on the Spread of COVID-19: The Case of Trump Rallies. Stanford, Institute for Economic Policy Research (SIEPR) Working Papers 20(43), (2020).
  * 8. G. Imbens, D. Rubin, Causal Inference for Statistics, Social, and Biomedical Sciences: An Introduction. Cambridge: Cambridge University Press, (2015).
  * 9. These regions are Valle d’Aosta, Veneto, Liguria, Toscana, Marche, Campania, and Puglia.
  * 10. The reform was approved by the Italian parliament in October 2019, with 97% of deputies and 80% of senators in its favor, and confirmed by 70% of the voters at the referendum.
  * 11. We calculate the average turnout at the regional level by using the information provided by Italian Ministry of the Interior, which surveyed it at 12pm, 7pm and 11pm of September 20${}^{\text{th}}$, and at the polling stations’ closing at 3pm of September 21${}^{\text{st}}$.
  * 12. T.G. Conley, GMM estimation with cross sectional dependence. Journal of Econometrics 92(1), 1-45 (1999).
  * 13. J. Qin, et al., Estimation of incubation period distribution of COVID-19 using disease onset forward time: a novel cross-sectional and forward follow-up study. Science advances 6(33), eabc1202 (2020).
  * 14. L. Ferretti, et al., Quantifying SARS-CoV-2 transmission suggests epidemic control with digital contact tracing. Science 368(6491), (2020).
  * 15. T. Fetzer, T. Graeber, Does Contact Tracing Work? Quasi-Experimental Evidence from an Excel Error in England. medRxiv, (2020).
  * 16. M. Monod, et al., Age groups that sustain resurging COVID-19 epidemics in the United States. Science eabe8372, (2021).

## Supplementary material

##  S.1 Supporting qualitative evidence

Figure S.1: An Overview of COVID-19 Dynamics in Italy Notes: The figure
reports information on the aggregate number of individuals who tested positive
to COVID-19 in Italy between February 2020 and January 2021. The dashed
vertical lines represent the first and the last day included in our estimation
sample. The solid vertical line represents the beginning of the campaign
period used in the main specifications.

##  S.2 Data Appendix

### List of data sources (Article)

#### Daily epidemiological bulletin on the spread of COVID-19 in Italy.

(available at https://github.com/pcm-dpc/COVID-19/tree/master/dati-regioni).

We access this publicly available source to retrieve information on the daily
number of infections for each region and the daily stock of individuals
seeking care in ordinary hospitals and ICUs. We also retrieve the daily number
of deceased individuals and the daily number of tests conducted in each region
by subtracting the stock at date $t-1$ from the stock at date $t$. Lastly, we
retrieve the percentage of positive tests dividing the daily number of
positive tests by the daily number of tests conducted and multiplying it by
100. We restrict our attention to bulletins published between June
29${}^{\textit{th}}$, 2020 and October 15${}^{\textit{th}}$, 2020. For the
Trentino-Alto Adige Region, we manually compute the region-level aggregate
figures because raw data contain separate information for the province of
Trento and the province of Bolzano/Bozen.

We then apply the following transormations to all variables to achieve a
better comparability across regions. First,

$\widehat{y}_{r,t}=\log\Big{(}\frac{\tilde{y}_{r,t}\times
100,000}{Population_{r}}\Big{)},$ (S.1)

where $\tilde{y}_{r,t}$ is each of the six epidemiological outcomes of
interest as originally included in the data source or as described in the
previous paragraph, $Population_{r}$ is the region’s population according to
ISTAT data (see below) as of January 1${}^{\textit{st}}$, 2020, and
$\widehat{y}_{r,t}$ is the transformed indicator. Second, we compute a seven-
day moving average as follow to get to the six outcomes $y_{r,t}$ estimated
using equation 1 and equation 2. Formally,

$y_{r,t}=\frac{\sum_{s\in[-6,0]}\widehat{y}_{r,t-s}}{7}.$ (S.2)

In turn, our final sample observes 103 daily seven-day moving averages for
each of the twenty Italian regions, for a total of 2,060 observations.

#### Voter turnout on September 20${}^{\textit{th}}$ and 21${}^{\textit{st}}$,
2020 referendum.

(available at https://dait.interno.gov.it/elezioni/open-data/dati-
referendum-20-21-settembre-2020).

This data contains information on the number of individuals eligible to vote
for the constitutional referendum in each municipality, as well as the number
of individuals who decided to vote by 12 pm, 7 pm, 11 pm on September
20${}^{\textit{th}}$, and by the closing time at 3 pm on September
21${}^{\textit{st}}$.

We aggregate data from all municipalities belonging to the same region to get
such measures at the regional level. We then define turnout as follow,

$Turnout_{r,h}=\frac{Voters_{r,h}}{Eligible_{r}}.$ (S.3)

#### Mentions of political leaders on regional TV news.

(data are available upon request).

We accessed this data by visiting the Italian public TV provider (RAI) and
collect information on all the times each of the prominent political leaders
in Italy have been mentioned on public TV. We search for Luigi di Maio, leader
de facto of the 5-Star Movement; Giorgia Meloni, president of Fratelli
d’Italia; Matteo Renzi, leader de facto of Italia Viva; Matteo Salvini,
secretary of Lega; Nicola Zingaretti, secretary of Partito Democratico.

We then restricted our attention to mentions during the editions of local TV
news (Tg Regionale), which go on air on the third-main channel of the
broadcaster daily at 2 pm; 7.30 pm; 0.10 am (on weekends at 2 pm; 7.30 pm; 11
pm).

Lastly, we summed the number of mentions of all leaders for each date and
region.

#### Public events with Senator Matteo Salvini

(available at https://legaonline.it/t_tour19.asp?l2=1992&archivio=1).

We manually counted the number of events reported in the list for each day and
region.

#### Google COVID-19 Community Mobility Reports

(available at https://www.google.com/covid19/mobility/).

We use daily information from each of the twenty regions relative to the
variables labeled as Retail and recreation percent change from baseline;
Grocery and Pharmacy percent change from baseline; Workplaces percent change
from baseline; Residential change from baseline. We exclude the remaining two
measures (i. e., Parks percent changes from baseline and Transit Stations
percent changes from baseline) because of several missing observations in the
period under consideration.

Retail and recreation percent change from baseline, Grocery and Pharmacy
percent change from baseline, and Workplaces percent change from baseline
measure the percentage change in the number of Google customers who are
located by their smartphone in each of those areas. Residential change from
baseline, instead, measures the percentage change in the average number of
hours that Google customers are located by their smartphone in their home.

We restrict our focus to data covering mobility between June
29${}^{\textit{th}}$ and October 15${}^{\textit{th}}$, 2020.

### List of data sources (Supplementary Material)

#### Municipality elections held on September 20${}^{\textit{th}}$ and
21${}^{\textit{st}}$, 2020

We retrieve the list of all municipality elections that took place on the same
day as the constitutional referendum and the seven regions’ regional elections
to calculate the share of each region’s voters subject to these elections. We
use data on the population eligible to vote at the municipality level
described in the previous sub-section to calculate the shares.

#### Geographic, socio-economic, and demographic characteristics from ISTAT

In Table S.2, we propose a specification in which regional-by-week linear pre-
treatment trends are replaced by weekly pre-treatment trends in observable
characteristics that might explain the spread of COVID-19. These
characteristics include (a) the share of voters subject to municipality
elections just described; (b) the region’s surface; (c) population density;
(d) percentage of hospitalized individuals coming from another region; (e)
unemployment rate; (f) employment rate; (g) employment rate among senior
($>$54 years old) individuals; (h) percentage of elderly in care; (i)
percentage of population covered by ultra-broadband internet connection; (j)
average travel times from central urban areas; (k) the average number of daily
tourists per 100 residents; (l) percent of municipalities providing childcare
services; (m) mountainous surface; coastal surface; (n) number of museums
visitors per km${}^{\textit{2}}$; (o) per capita export volume; (p) an
indicator equal to 1 if the region is located in Southern Italy. We collected
all the latter variables from the Italian National Institute of Statistics
(ISTAT). Table S.1 reports the descriptive statistics of these and all other
variables employed in this analysis.

| (1) | (2) | (3)
---|---|---|---
| Mean | St. Dev. | Obs.
(a) Measures of diffusion
Cases | 0.60 | 0.45 | 2,060
Tests | 4.44 | 0.52 | 2,060
% Positive | 0.594 | 0.350 | 2,060
Hospitalized | 0.85 | 0.49 | 2,060
ICU | 0.12 | 0.12 | 2,060
Deaths | 0.01 | 0.02 | 2,060
(b) Regional characteristics
Population ($\times$ 100K) | 30.12 | 25.40 | 20
Surface (km${}^{\textit{2}}$) | 17,398 | 9,712 | 20
Density (inh./km${}^{\textit{2}}$) | 317 | 368 | 20
% Healthcare regional migration | 8.92 | 6.20 | 20
Unemployment rate (%) | 10.47 | 5.19 | 20
Employment rate (%) | 59.38 | 10.20 | 20
Senior Employment rate (%) | 55.15 | 6.64 | 20
Elderly care patients (%) | 1.24 | 0.92 | 20
Fiber Internet connection (%) | 14.50 | 2.97 | 20
Travel times from urban areas (mins.) | 51.45 | 10.60 | 20
Daily tourists (per 100 inh.) | 9.59 | 10.83 | 20
Childcare provision (%) | 58.20 | 23.91 | 20
Mountainous surface (km${}^{\textit{2}}$) | 2.59 | 1.23 | 20
Coastal surface (km${}^{\textit{2}}$) | 0.18 | 0.22 | 20
No. Museums (per inh.) | 0.01 | 0.01 | 20
Export volume (per inh.) | 44,066 | 45,580 | 20
South | 0.45 | 0.51 | 20
(c) Election characteristics
Campaign | 0.35 | 0.49 | 20
Share municipality election | 0.08 | 0.11 | 20
Voter turnout (Sunday at 12PM) | 0.12 | 0.03 | 20
Voter turnout (Sunday at 19PM) | 0.30 | 0.08 | 20
Voter turnout (Sunday at 11PM) | 0.40 | 0.09 | 20
Voter turnout (Overall) | 0.55 | 0.11 | 20

Table S.1: Descriptive Statistics Notes: The table show the mean, the standard
deviation and the number of observations for each vaiable employed in the
analysis. The summary statistics in panel (a) are calculated considering the
entire estimation sample, while those showed in panels (b) and (c) refer to
the regional value of the characteristics as in 2019 and during the election
days, respectively.

##  S.3 Robustness tests

Besides showing that the six epidemiological outcomes of interest were
following the same dynamics in treated and control regions before the start of
the campaign (see Figure 3), we performed various robustness tests to validate
our estimates and research design further.

First, in Table S.2, we replace the linear pre-treatment weekly regional
trends with weekly trends in several observable characteristics that can
affect the spread of COVID-19. The variables included in the analysis are
defined and reported in Table S.1. On the one hand, this exercise allows
establishing that this paper’s main results do not rely on the control for
pre-trends in the outcomes. On the other hand, the exercise is beneficial to
exclude that observed patterns are due to differences in other dimensions,
which can confound our results. In particular, this robustness check excludes
the possibility that our results depend – among others – on differences in
touristic activities, availability of smart-working solutions, the share of
elderly individuals who are in care, diffusion of pre-school services, and
commuting times.

Second, in Figure S.2, we show that our main results do not depend on the
specific choice of considering all weeks after week 34 as post-treatment and
the weeks before as pre-treatment. In each iteration, we estimate a version of
equation 1 in which the $Post_{t}$ indicator is equal to 1 starting from the
week specified on the horizontal axis and 0 otherwise. Week 30 was the week
when the national government announced the election dates, while week 38 is
the week during which the election took place. This exercise allows us to
establish that our results do not depend on our definition of the campaign
period. More specifically, it might be the case that party leaders and
candidates start a campaign before the official start reported in the law,
which is 30 days before the election. Our results show that the choice to
consider week 34 – as in the law – in the main specification is not driving
our results. Indeed, the estimates are qualitatively equal if we allow the
campaign period to begin during week 33 or 32 (i.e., up to 45 days before the
election).

Third, in Figure S.3, we show that our results do not rely on a specific
region’s presence in the sample. In each iteration, we estimate equation 1 by
removing from the sample the region specified on the horizontal axis. We find
that the estimated coefficients are very stable, excluding the possibility
that our estimates depend on one outlier region, which either has low
diffusion of COVID-19 and no regional election, or high spread of the
infection and the presence of regional elections.

Lastly, we assess the robustness of our statistical inference. The Conley HAC
standard errors (19) account both for spatial correlation of units that are
geographically close and for auto-correlation over time. However, they come
with the limitation that the choice of cutoffs is arbitrary. In Figure S.4, we
show that our analysis’s statistical inference bases on very conservative
cutoffs. More specifically, at each iteration, we estimate equation 1 by
imposing a non-zero correlation among observations within the distance cutoff
specified on the horizontal axis and the lag cutoff specified on the vertical
axis, and we report the p-values for the $Post_{t}\times Campaign_{r}$
indicator. For all epidemiological outcomes, we find that our main results
draw statistical inference using the largest feasible p-values.

| (1) | (2) | (3) | (4) | (5) | (6)
---|---|---|---|---|---|---
| Infections | Tests | % Posit | Hospit. | ICUs | Deaths
Campaign $\times$ Post | 0.070* | -0.083*** | 0.146*** | 0.240*** | 0.053** | 0.006*
| (0.033) | (0.019) | (0.031) | (0.052) | (0.020) | (0.002)
Observations | 2,060 | 2,060 | 2,060 | 2,060 | 2,060 | 2,060
Baseline (value) | 0.315 | 83.578 | 0.404 | 0.904 | 0.032 | 0.012
Region FE | ✓ | ✓ | ✓ | ✓ | ✓ | ✓
Reg. charact. pre-trends | ✓ | ✓ | ✓ | ✓ | ✓ | ✓
Date $\times$ Adjacent reg. FE | ✓ | ✓ | ✓ | ✓ | ✓ | ✓

Table S.2: Robustness Test: Controlling for Trends in Observables Notes: The
table shows the differential impact of electoral campaign between regions
voting both for the referendum and the regional election (treatment) and those
only voting for the referendum (control), as obtained through the estimation
of Equation 1 showed in the article. All dependent variables are the natural
logarithm of the seven-day moving average of the daily number at the regional
level. See Section 3 for details. $Campaign$ is a dummy equal to 1 for
treatment regions, and 0 otherwise. $Post$ is a dummy equal to 1 for all the
dates since August 21st, and 0 otherwise. All columns include region fixed
effects, pre-treatment trends in region-specific characteristics and date-
adjacent regions fixed effects. Standard errors are robust to serial and
spatial correlation and computed imposing maximum cutoff for both spatial and
time lags. $\dagger$, *,**,*** represent the 10%, 5%, 1%, 0.1% significance
levels, respectively.

Figure S.2: Robustness Test: Start of the Electoral Campaign

(a) : New infections (b) : Tests

(c) : % Positives (d) : Hospitalized

(e) : ICUs (f) : Deaths

Notes: The figures shows the differential impact of electoral campaign between
regions voting both for the referendum and the regional election (treatment)
and those only voting for the referendum (control), as obtained through the
estimation of Equation 1 showed in the article, for different definitions of
the starting week of the electoral campaign (horizontal axis), and together
with the 95% confidence intervals (dashed lines). All dependent variables are
the natural logarithm of the seven-day moving average of the daily number at
the regional level. See Section 3 of the article for details. $Campaign$ is a
dummy equal to 1 for treatment regions, and 0 otherwise. Confidence intervals
are based on standard errors that are robust to serial and spatial correlation
and computed imposing maximum cutoff for both spatial and time lags. Figure
S.3: Robustness Test: Dropping one Region at the Time

(a) : New infections (b) : Tests

(c) : % Positives (d) : Hospitalized

(e) : ICUs (f) : Deaths

Notes: The figures shows the differential impact of electoral campaign between
regions voting both for the referendum and the regional election (treatment)
and those only voting for the referendum (control), as obtained through the
estimation of Equation 1 showed in the article, for different sub-samples of
observations from which the region specified on the horizontal axis has been
removed, and together with the 95% confidence intervals (dashed lines). All
dependent variables are the natural logarithm of the seven-day moving average
of the daily number at the regional level. See Section 3 of the article for
details. $Campaign$ is a dummy equal to 1 for treatment regions, and 0
otherwise. Confidence intervals are based on standard errors that are robust
to serial and spatial correlation and computed imposing maximum cutoff for
both spatial and time lags. Figure S.4: Robustness Test: Conley HAC Standard
Errors

(a) : New infections (b) : Tests

(c) : % Positives (d) : Hospitalized

(e) : ICUs (f) : Deaths

Notes: The figure reports p-values from estimating Equation 1 showed in the
article in multiple regressions using Conley HAC standard errors (19) with
time and spatial lags vary from their minimum to their maximum feasible value.
Each iteration represents and increase to the distance cutoff equal to 25 km.
or an increase in the time cutoff equal to 3 days. All dependent variables are
the natural logarithm of the seven-day moving average of the daily number at
the regional level. See Section 3 of the article for details. $Campaign$ is a
dummy equal to 1 for treatment regions, and 0 otherwise. Confidence intervals
are based on standard errors that are robust to serial and spatial correlation
and computed imposing maximum cutoff for both spatial and time lags.