COVID-19: Early Evening Curfews and Mobility — Velias, Georganas and Vandoros, Social Science & Medicine
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A research paper, COVID-19: Early evening curfews and mobility, by Alina Velias, Sotiris Georganas and Sotiris Vandoros, published in Social Science & Medicine (2022) and reproduced from King's Research Portal. The paper studies the shift from a 9pm to a 6pm weekend curfew introduced on 6 February 2021 in the Attica region of Greece, using Google COVID-19 Community Mobility Reports data. It applies a difference-in-differences approach comparing Attica with the Aegean Region, with Epirus & Western Macedonia and Thessaly & Central Greece as robustness controls. The abstract reports little or no evidence of an effect on grocery and pharmacy mobility, and states that an 18.75% reduction in allowed hours led to a relatively small increase in time spent in residential spaces. The document ends with an appendix on the data period, data accuracy checks and curfew data sources.
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“COVID-19: Early Evening Curfews and Mobility — Velias, Georganas and Vandoros, Social Science & Medicine.”
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10.1016/j.socscimed.2021.114538
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Velias, A., Georganas, S., & Vandoros, S. (2022). COVID-19: Early evening curfews and mobility. Social
Science & Medicine, 292(114538), Article 114538. https://doi.org/10.1016/j.socscimed.2021.114538
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Download date: 27. Jun. 2026
COVID-19: Early evening curfews and mobility
Alina Velias,a Sotiris Georganasa and Sotiris Vandorosb
a City, University of London
b King’s College London and Harvard T.H. Chan School of Public Health
Abstract
During the COVID-19 pandemic, some countries introduced early evening curfews.
Several studies try to measure the effectiveness of such measures across different
countries, but disentangling competing effects can be elusive. We examined the impact of
an early evening curfew on mobility by studying a shift in curfews from 9pm to 6pm in
Greece using Google mobility data. We followed a difference-in-difference (DiD)
econometric approach, where we compared trends in mobility in residential spaces as
well as groceries and pharmacies, before and after the introduction of the 6pm curfew in
Attica with trends in three other comparable Regions. We found little or no evidence of
an effect of the early curfew on daily mobility relating to groceries and pharmacies, and
that a 18.75% reduction in hours where people were allowed to leave home led to a
relatively small increase in time spent in residential spaces. This less-than-proportionate
reduction in mobility outside the household suggests a possibility that the curfew led to
more people coinciding in indoor public spaces, such as grocery shops – which constitutes
a contagion risk factor. Results should be treated with caution, especially with regards to
the magnitude of any effect, as Google mobility data do not report the time of the day, so
the time density of activities cannot be estimated. Lockdowns and other measures are
necessary to tackle Covid-19, but it is important to avoid substitution by activities that
contribute further to spreading the virus. Interventions should therefore be based on a
thorough analysis of human behaviour.
Keywords: Covid-19; mobility; curfews; non-pharmaceutical interventions; substitution
1
1. Background
There is an ongoing debate about non-pharmaceutical interventions and how effective
they are in tackling the COVID-19 pandemic (Brauner et al 2021). Such measures are
most often introduced jointly, so disentangling the competing effects of individual
measures is challenging. Common measures that are introduced jointly include
restrictions of movement, congregation and closures, for example of (a) schools and
shops at the same time, (b) bars and restaurants (often closing both outdoors and
indoors), or both (a) and (b). Governments normally treat these measures as substitutes
(e.g. stating that other measures such as curfews or restaurant closures would remain in
place to allow re-opening of schools, which was seen as a priority), although potential
complementarities between some of them cannot be ruled out. Finding appropriate
control groups to disentangle the effects of individual measures is not straightforward.
An increasing number of studies suggest that the stringency of the lockdown
measures does not always make a difference in infection prevalence or related deaths
(e.g. Bonardi et al 2020 argue that that partial lockdowns were as effective in reducing
the number of infections and deaths as stricter measures). Of course, such studies are
often challenged by measurement and identification issues (Goodman-Bacon and Marcus
2020). Also, there are examples of evidence that the ‘signal’ (inducing voluntary behavior
changes) is important in contrast to the actual regulation (mandated behavior changes)
(Herby 2021). In general though, restricting one human activity often leads to
substitution by others, as humans seek alternatives, and there may be a strictness level
beyond which extra measures can actually backfire. In this paper we suggest that early
curfews may be one of these cases, where excessive strictness might potentially lead to
the opposite of the intended epidemiological result.
2
Why would reducing the time window during which people are allowed to leave
their homes either fail to achieve the desired greater reduction in virus spread or, even,
backfire by contributing towards the spread? One straightforward reason is that people
do not fully reduce the activity proportionately to the strictness of measures – they
reallocate part of it towards options that are still allowed. For example, mobile tracker
data in the US shows a large reallocation of consumer activity from “nonessential” to
“essential” businesses (note that definition of essential varies by country) as well as from
restaurants and bars toward groceries and other food sellers (Goolsbee and Syverson
2021). Whilst overall the pandemic appears to have caused a change in online
consumption patterns (Alvarez et al 2021) and the response of consumption to
government stimuli (Chetty et al 2020), it would seem that essential consumption
persisted at reasonable levels. Supermarkets, for instance, have been linked to higher
likelihood of spreading the disease (Shao et al 2021). These studies do not measure the
resulting congestion in the essential businesses, but this is likely to be high, resulting in
greater risk of virus spread. Importantly, recent studies show that early evening curfews
backfired in Toulouse (Dimeglio et al 2021), had no effect in Hesse (Haas et al 2021) and
may elicit reactance (Sprengholz et al 2021).
In our study, we took advantage of within-country heterogeneity in the timing of
the introduction of an early evening curfew to evaluate this measure in tackling the
Covid-19 pandemic.
Our study focuses on the impact on mobility rather than disease outcomes. In
other words, we study how curfews affect a Covid-19 risk factor (mobility, which may be
associated with crowding) instead of Covid-19 cases or deaths. We followed this
approach for a number of reasons. Linking COVID-19 cases with a particular intervention
is particularly challenging and may be misleading due to the presence of different
3
variants; there is dispersion in the time lag between infection and death; second-hand
transmission may occur via asymptomatic people; and there is a strong time-varying bias
in disease measurement, whether it is done by recorded cases or by test positivity rates
(Georganas et al 2021).
While cross-country studies are very useful, they suffer from several drawbacks
absent in our method. On the one hand countries differ in important characteristics that
affect the performance of measures (availability of ICUs, the state of the health system),
but crucially they also differ in the way they measure the pandemic itself. Recorded cases
are biased and since testing methods are not homogeneous across countries, the bias is
heterogeneous, differing greatly across countries (Georganas et al 2021). On the other
hand, the measures are not the same across countries, and are seldom enacted alone
within a specific country. Usually, a complicated bundle of measures is enacted on the
same day (and some measures almost always come together, such as closing several
levels of schooling along with other face-to-face activities) which greatly complicates
isolating the effect of a single measure. In this paper we use evidence from a single
European country to examine the effect of early curfews, using a difference-in-differences
approach, comparing a region affected by the curfew to other regions.
2. Data and Methods
While a 9pm-5am curfew applied in Greece since November 2020, a 6pm-5am weekend
curfew was introduced on 6 February 2021 in the Attica region (which includes the
capital city of Athens) as a response to increasing Covid-19 cases. The reason for the
stricter curfew time was related to rising COVID-19 cases and hospitalisations, despite
the existing social distancing measures and the 9pm curfew. These were attributed to too
much movement in public areas in Athens, with concerns that people were meeting
4
outdoors or paying a visit family and friends, and hanging out around coffee shops and
bars offering drinks to go. Banning movement after 6pm on weekends, when many
people don’t work, directly affected their options to meet with others – but it also reduced
the hours of the day that they could engage in activities, such as visiting grocery stores.
Commuting to and from work was exempt from the curfew, but businesses that would
normally have customers after 6pm would be directly affected (the government took
separate financial measures for businesses and individuals affected by Covid-19 non-
pharmaceutical interventions). We studied the impact of the 6pm curfew on human
activity using mobility data from Google COVID-19 Community Mobility Reports (2021)
over the period of 2 January – 28 February 2021.
2.1 COVID-19 Community Mobility Reports Data
Google mobility reports show daily-level movement trends by region, across
different categories. These reports use aggregated, anonymized data from users who
have turned on the Location History setting, The same kind of data are used to
show popular times for places in Google Maps (Google COVID-19 Community Mobility
Reports 2021). The category labels are: retail and recreation, groceries and pharmacies,
parks, transit stations, workplaces, and residential. The residential category is measured
in amount of time spent, while the other four categories are measured in number of
visitors.
The data show the percentage change in visitor numbers to (or time spent in)
categorized places compared to the baseline. According to the data source, each
day’s baseline is the median day-value for the corresponding day of the week, from the 5-
week period Jan 2 – Feb 6, 2020 (before COVID-19 measures were introduced).
5
We use the raw daily percentage change from the baseline as our outcome
variable. In particular, we focused on time spent at residential spaces, and time spent at
groceries/pharmacies. Staying at home is considered a goal of lockdown measures, to
limit the spread of the novel coronavirus; and it has been shown that indoor spaces such
as supermarkets may facilitate transmission (Shao et al 2021). We considered the
weekend mobility figures, since this is when the curfew policy differed between regions.
Further details on the specifics of the dataset can be found in the Online Appendix.
Apple mobility trends reports are also available. However, these only cover
driving, walking and commuting, and capture the volume of requests, rather than actual
mobility. Furthermore, Apple makes data available for Attica and Greece as a whole only,
so the control group would include the treatment area. Despite these issues, any evidence
we could get out of the Apple data points at exactly the same direction as the results using
the Google data.
2.2 Difference-in-Differences Analysis
A simple before-after analysis to evaluate the impact of the policy on mobility in
Attica may not be reliable as other factors affecting mobility may change, which is why
we used control groups: one in the main analysis (the Aegean Region) and two for
additional robustness checks (the Epirus & Western Macedonia Region; and the Thessaly
& Central Greece Region). Figure 1 provides a map of the Regions of Greece.
We studied the difference in the differences in mobility between Attica and the
control regions in the five weekends before (all weekends in January) and the four
weekends after (all weekends in February) the introduction of the 6pm curfew in Attica
using a difference-in-differences (DiD) ordinary least squares econometric estimator. A
DiD model compares trends in the outcome in the treatment group and a control group
6
before and after a particular intervention, and is used extensively in the literature for
causal inference (Kavetsos et al 2021; Autor 2003). In such empirical models, there is a
treatment group dummy, which takes the value of 1 for the group that underwent a
treatment; and a treatment period dummy, which takes the value of 1 for the time after
the intervention. The interaction of the treatment group dummy and the treatment
period dummy gives us the main variable of interest.
[Insert Figure 1 here]
Finding an appropriate control group can be challenging. Some localised 6pm
curfews did apply to small parts of the population in sub-areas of these control regions
depending on local increases in COVID-19 cases in villages or towns, so our control
groups are not absolutely perfect. However, we argue that these can still serve as
appropriate control groups (see Figure 2 for visual inspection of trends). In the Aegean
region, 13.2 % of the population on average was subject to a local 6pm curfew before this
applied to the whole Attica Region, and 12.3% afterwards. As this applied to a small part
of the population, and the percentage remained about the same, we argue that this can
be used as an appropriate control group for our study.
[Insert Figure 2 here]
We repeated the analysis using two additional control groups (separately, and
within a single regression), which are perhaps not as suitable as the Aegean region, but
still useful as a robustness check. 15.7% of the population on average in Epirus & Western
Macedonia was subject to local 6pm curfews before the introduction of the measure in
7
Attica, which decreased to 6.7% afterwards. This reduction in proportion of local
populations under curfew could lead to an overestimate of any reduction in mobility in
Attica, or of time spent in residential places. The corresponding figures in Thessaly &
Central Greece were 6.8% on average before and 19.9% after, demonstrating a relative
increase in proportion of local population under lockdown. Using this region as a control
group may lead to an underestimate of any reduction in mobility in stores in Attica or of
time spent in residential places.
In the DiD model, the dependent variable is the percentage change in time spent
in a particular type of location compared to the baseline. In one model we study groceries
and pharmacies, and in the other we study residential spaces. We included a dummy
variable for the Attica region which is the treatment group (1 for observations on Attica
and 0 for the control group), and a dummy variable which takes the value of one in the
post-treatment period (from 6 February onwards) and zero otherwise. The interaction
between the two shows whether the intervention had an effect on relative trends. We
used robust standard errors in regressions. Summary statistics are presented in Table 1.
[Insert Table 1 here]
Figure 2 shows the trends in mobility in the treatment and control regions before
and after the 6pm curfew intervention. What matters in a DiD model is that the control
and treatment groups demonstrate common trends. A visual observation of the graphs
suggests the presence of similar trends in mobility with regards to groceries and
pharmacies (Panel A), and residential spaces (panel B) – although this is not directly
testable. The trends in other related variables such as daily case counts and deaths over
the period of study can be found in the Appendix (Figures A1 and A2). Of course, Attica
8
includes the capital city of Athens, and in that sense is more urban than the control
groups. Nevertheless, control groups are often different than treatment groups, and what
matters is the relative change rather than the absolute characteristics. For example,
different countries or US States with different characteristics have been used in the
literature when performing DiD analyses (Kavetsos et al 2021; Kim and Albert Kim 2018;
Card and Krueger 1994). In any case, an assumption of this analysis is that in the absence
of the 6pm curfew in Attica, the trends between Attica and control regions would remain
similar (and we have no reason to believe that something other than the 6pm
intervention coincided with the treatment and would have distorted the common pre-
treatment trends).
On top of the traditional DiD which basically averages between treated and non-
treated periods and regions, we take advantage of the recent advancements in
econometrics, applying synthetic DiD (SDiD) to our data (Arkhangelsky et al 2018). The
primary difference is that it allows to construct the control by putting differential weights
on both control units (regions) and pre-treatment time periods – rather than averaging
over them as the standard DiD model would. The goal of the differential weighting is to
match the pre-treatment trend of the treated region as closely as possible using the
weighted combination of controls – and then use the same weights to extend the trend
into the treatment period. The literature offers an a-theoretic selection tool for such
weight selection, where the model automatically selects the combination of control
regions and periods that best matches the pre-treatment trend in Attica.
3. Results
The results of the DiD regressions using the Aegean region as a control are presented in
Table 2. When considering the effect on time spent in groceries and pharmacies (column
9
1), the coefficient of the DiD interaction term, that shows the difference in the difference
between the two regions, is statistically insignificant [coeff: -13.55; 95%CI -42.723 to
15.623]. This suggests that there was no change in the relative trends in visits to groceries
and pharmacies in Attica compared to the control group after the intervention. Column 2
shows the results of the model with time spent at residential spaces as outcome. The DiD
interaction term is positive and statistically significant [coeff: 4.4; 95%CI 1.688 to 7.112],
suggesting that the relative increase in time spent at residential spaces after the 6pm
curfew was only 4.4 percentage points. The results of the econometric analysis show that
a reduction in the time when people were allowed to go outside by 3 hours (a 18.75%
decrease) led to a 4.4 percentage point increase in time spent at home and had no effect
on time spent in groceries or pharmacies, in relative terms.
[Insert Table 2 here]
Three robustness checks using two more Regions as control groups (separately,
and in the same regression with the Aegean) confirm the results of the main analysis. We
get similar results when using the Epirus & Western Macedonia Region as control group
(Table A1, Online Appendix). The model examining mobility in groceries and pharmacies
yields statistically insignificant results, as in the main model, and in the model examining
time spent residential spaces, the DiD coefficient is positive and statistically significant
[coeff: 4.625; 95%CI 1.412 to 7.838], but slightly larger than in the main model, as
expected. Table A2 in the Online Appendix presents the DiD results of the model using
Thessaly as a control group, as well as all three regions as controls (Table A3). When
studying the effect on mobility relating to groceries and pharmacies, the coefficient of the
DiD interaction term remains statistically insignificant, as in the main analysis. In the
10
model studying residential spaces, the DiD interaction term is positive but statistically
significant at the 10% level only, with a smaller magnitude than in the main analysis, as
expected [coeff: 3.025; 95%CI -0.275 to 6.325]. As discussed above, this can be expected
given that Thessaly had a larger proportion of population under curfew than the other
control groups. The regression including all three regions as control gives consistent
results (Table A3 in the Appendix). Additionally, we conduct a robustness check with the
inverse hyperbolic sine transformation applied to the dependent variables, obtaining
estimates highly consistent in direction and significance with those reported above
(Tables A4 and A5 in the Appendix). Results using synthetic controls (SDiD), which are in
the same direction as the results of the baseline model, are presented in the Online
Appendix (Section A1 and Figures A3 and A4).
4. Discussion
We found that the 6pm instead of 9pm curfew in Athens appears to have led to a
4.4 percentage point relative increase in time spent at home and had no effect on time
spent in groceries and pharmacies. Considering that this was a result of a 18.75%
reduction in hours where people were allowed to leave home, and the percentage change
in mobility seems to be smaller than the percentage change in time, the early curfew may
have led to greater crowding. Especially with regards to grocery stores, the same level of
mobility appears to be concentrated in fewer hours of the day. If more people were
present simultaneously in high-risk places such as supermarkets (instead of being spread
over more hours during the day), the curfew may have led to greater disease
transmission. The percentage increase in time spent at residential spaces was lower than
the percentage change in time permitted to leave home due to the curfew. Apparently,
11
following the introduction of the 6pm curfew, people did not reduce their activities
proportionately to the time that they were allowed to leave home.
Finding the exact impact is not straightforward, as Google mobility data do not
show at what time of the day these activities took place, or the density of activities during
the day. Of course, not all hours in the day demonstrate the same level of mobility, so this
18.75% reduction in hours when people were free to leave their homes does not reflect
differences in density.
Our findings add to important existing evidence from Toulouse (Dimeglio et al
2021) that suggests that a 6pm curfew backfired, and to a paper on the German Land of
Hesse that found no evidence that night curfews had an effect on disease transmission
(Haas et al 2021). Another recent study argues evening curfews may elicit reactance
(Sprengholz et al 2021). Interestingly, we find little or no evidence of a decrease in
mobility at essential businesses such as groceries and pharmacies despite the lower
number of hours that people could spend in stores. This is broadly in line with Chetty et
al (2020) finding of the lower income households increasing consumption in response to
government stimulus.
The outcome variable in this study is mobility rather than infections. Although
certain environments such as supermarkets have a higher likelihood of spreading the
disease (Shao et al 2021), our data do not show the actual impact on Covid-19. However,
such an effect would be extremely challenging to disentangle, even with clinical data for
the following reasons: (a) Other factors such as variants that may be more transmissible
may apply, distorting the effect on actual health outcomes; (b) there is dispersion in the
time lag between infection and symptoms or hospitalisation or death; (c) the effect might
show via second-hand transmission (for example, individuals who first contract SARS-
CoV-2 may be younger people who are often asymptomatic (Kelvin and Halperin 2020),
12
and may pass the virus on to others with a longer lag); (d) due to bias in disease
prevalence measurement (Georganas et al 2021). Future research can examine the
complicated relationship between curfews, mobility and COVID-19 cases at the regional
level, taking into account the aforementioned empirical challenges.
Aside of challenges to measurement, evaluating effect of counter-Covid-19
measures on cases is sensitive to factors undermining the validity of estimates, such as
effects driven by anticipation of measures, reverse causality, and spillovers (Goodman-
Bacon and Marcus 2020). Indeed, it is very plausible that people may change behaviour
in response to factors other than governmental restrictions, such as case counts. Note
that in our case this would lead to an underestimate of the effect of the curfew, as follows.
If rising cases in Attica (treated) region led people to staying at home more and avoiding
indoor public spaces (such as grocery shops and pharmacies) – what we are estimating
is a joint effect of the curfew (law) and rising cases (fear). Should the curfew be effective,
we would see reduction in mobility outside the residence greater than proportionate to
the decrease in time allowed. We also consider an alternative possibility, where rising
cases in the control regions would motivate people to stay at home more despite the lack
of governmental intervention in form of curfew. This would lead to a smaller difference
in mobility between treated and control regions, but attributable to fear, rather than to
the effect of the curfew.
This study is subject to limitations. We cannot directly calculate the impact on
crowding as Google mobility data are not available by time of the day – so the magnitude
of any reported effect should be interpreted with caution. For example, while we do not
find a sufficient overall decrease in mobility, it may have decreased more in some parts
of the day (e.g. early mornings) with majority of outdoors mobility (and the resulting
crowding) concentrating in later part of the day (e.g. just before grocery stores close).
13
Nevertheless, daily averages are still informative. Furthermore, as discussed, small parts
of the population in control groups lived in sub-regions that were subject to local
lockdowns. It is also worth mentioning that many stores are closed on Sundays. Finally,
our study focuses on mobility rather than infections.
We were not able to consider smaller geographic areas due to data availability
constraints, and we also do not have any information on purchases made – which may
have been affected by the pandemic (Alvarez et al 2021). Future research can take
purchasing behaviour into account, if such data become available.
Whilst Google mobility data have been used by multiple studies (including
comparison of mobility trends between and within countries, Chan, 2020; evaluation of
social distancing on regional levels within countries, Cot et al 2021; Wielechowski et al
2020; nowcasting economic activity, Sampi Bravo and Jooste, 2020) - the absence of
disclosure by dataset owners of the proportion of smartphone users whose location
history is recorded, is a weak point of this literature. As a consequence, our ability to
assess the representativeness of this subset of population is limited. However, note that
Google’s policy for anonymising the data results in gaps on the days which lack “enough
data to confidently and anonymously estimate the change from the baseline” – which is
also a median value over a 5-week period (see Appendix for details on the dataset). Since
there are no such gaps for any of the regions of Greece over the period of interest, we are
confident that the dataset captures a substantial proportion of the population.
Our findings are relevant to areas that are still fighting COVID-19 but also to the
next pandemic or any contagious disease (e.g. Ebola). Overall, non-pharmaceutical
interventions were necessary to tackle Covid-19, but any measures should be carefully
designed and should be based on a thorough analysis of human behaviour, that
anticipates substitution of activities. Decisions on what sort of interventions are
14
introduced should be based on empirical evidence, and constantly re-evaluated and
adjusted when necessary to prevent backfiring. It seems that some measures can
occasionally be too strict, even if containing the disease is the only goal.
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Online Appendix
Table A1 – Robustness Check: Using Epirus & Western Macedonia Region as control group
grocery & pharmacy residential
DiD interaction term (Attica*week 6 onwards) -9.95 4.625***
[-44.358 - 24.458] [1.412 - 7.838]
Week 6 onwards (treatment period) 1.725 -4.20***
[-23.336 - 26.786] [-6.668 - -1.732]
Attica dummy variable (treatment group) -2.30 1.00
-66.904 [-1.086 - 3.086]
Constant term 3.90 11.2***
[-20.211 - 28.011] [9.948 - 12.452]
Observations 36 36
R-squared 0.0282 0.4825
F-statistic 3.37 7.71
The dependent variable is the change in time spent in the two types of locations compared to the
baseline. Robust 95% confidence intervals in brackets. *** p<0.01, ** p<0.05, * p<0.1.
Table A2 – Robustness Check: Using Thessaly Region as control group
grocery & pharmacy residential
DiD interaction term (Attica*week 6
onwards) -5.75 3.025*
[-39.015 to 27.515] [-0.275 to 6.325]
Week 6 onwards (treatment period) -2.475 -2.6**
[-25.942 to 20.992] [-5.180 to -0.020]
Attica dummy variable (treatment group) 0.5 1.60
[-30.627 to 31.627] [-0.517 to 3.717]
Constant term 1.1 10.6***
[-19.667 to 21.867] [9.297 to 11.904]
Observations 36 36
R-squared 0.016 0.372
F-statistic 0.56 5.49
The dependent variable is the change in time spent in the two types of locations compared to the
baseline. Robust 95% confidence intervals in brackets. *** p<0.01, ** p<0.05, * p<0.1.
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Table A3 – Robustness Check: Results of the Difference-in-Differences regressions, using
three regions (Aegean; Thessaly & Central Greece; Epirus & Western Macedonia) as a
control group
grocery & pharmacy residential
DiD interaction term (Attica*week 6 onwards) -9.75 4.017***
[-35.301 to 15.801] [1.614 to 6.420]
Week 6 onwards (treatment period) 1.525 -3.592***
[-10.753 to 13.803] [-4.989 to -2.195]
Attica dummy variable (treatment group) 1.167 1.900**
[-23.609 to 25.943] [0.149 to 3.651]
Constant term 0.433 10.300***
[-10.890 to 11.757] [9.556 to 11.044]
Observations 72 72
R-squared 0.011 0.478
F-statistic 2.67 19.64
The dependent variable is the change in time spent in the two types of locations compared to the baseline.
Robust 95% confidence intervals in brackets. *** p<0.01, ** p<0.05, * p<0.1.
Table A4 – Robustness Check: Applying the inverse hyperbolic sine (IHS) transformation to
the dependent variable (residential)
Dependent variable:
Time spent in residential locations (IHS)
Epirus and Western Thessaly and Central
Aegean
Macedonia Greece
(1) (2) (3)
DiD interaction term (Attica*week 6
0.65*** 0.56*** 0.37**
onwards)
(0.36, 0.95) (0.26, 0.87) (0.06, 0.69)
Week 6 onwards (treatment period) -0.61*** -0.52*** -0.33***
(-0.81, -
(-0.74, -0.30) (-0.55, -0.10)
0.40)
Attica dummy variable (treatment group) 0.29*** 0.08 0.14
(0.09, 0.48) (-0.13, 0.28) (-0.08, 0.35)
Constant term 2.89*** 3.10*** 3.04***
(2.75, 3.03) (2.95, 3.24) (2.89, 3.19)
Observations 36 36 36
R2 0.75 0.56 0.41
Adjusted R2 0.72 0.52 0.36
F Statistic (df = 3; 32) 31.22*** 13.40*** 7.51***
Note: *
p<0.1; **p<0.05; ***p<0.01
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Table A5 – Robustness Check: Applying the inverse hyperbolic sine (IHS) transformation to
the dependent variable (groceries)
Dependent variable:
Time spent in groceries and pharmacies (IHS)
Epirus and Western Thessaly and Central
Aegean
Macedonia Greece
(1) (2) (3)
DiD interaction term (Attica*week 6
-2.36 -2.67 -1.48
onwards)
(-6.14, 1.42) (-6.55, 1.20) (-5.67, 2.72)
Week 6 onwards (treatment period) 0.68 0.99 -0.21
(-1.99, 3.35) (-1.75, 3.73) (-3.17, 2.76)
Attica dummy variable (treatment group) -0.13 -0.67 -0.40
(-2.66, 2.39) (-3.25, 1.91) (-3.20, 2.39)
Constant term -0.22 0.31 0.04
(-2.01, 1.56) (-1.52, 2.13) (-1.93, 2.02)
Observations 36 36 36
R2 0.09 0.15 0.07
Adjusted R2 0.01 0.07 -0.02
F Statistic (df = 3; 32) 1.10 1.84 0.75
Note: *
p<0.1; **p<0.05; ***p<0.01
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Figure A1 – Trends for Covid-19 case counts (total cases divided by region’s population) in
the regions of Attica (treated) and (1) Aegean, (2) Epirus and Western Macedonia, and (3)
Thessaly and Central Greece (controls).
Figure A2 – Trends for Covid-19 death counts in the regions of Attica (treated) and (1)
Aegean, (2) Epirus and Western Macedonia, and (3) Thessaly and Central Greece (controls).
Source: iMEdD (a non-profit journalism organization): https://lab.imedd.org/Direct data link:
https://github.com/iMEdD-Lab/open-data/tree/master/COVID-19
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A1. Synthetic Controls
Applying the synthetic controls DiD, we find a significant positive effect [coeff:
4.55; 95%CI 3.91 to 5.20] on the relative time spent at home (Figure A3 in the Online
Appendix), compared to the control group after the intervention. This means that people
stayed at home 4.55% more once the time they were allowed to spend outside was
reduced by 18.75% - strikingly similar to the baseline DiD estimate above. For the time
spent in groceries or pharmacies on weekends (Figure A4 in the Online Appendix), the
SDiD estimate showed a significant negative effect [coeff: -14.62; 95%CI -14.95 to 14.28]
of the intervention on relative time spent. In practice, this suggests a 14.6% decrease in
time people spent in groceries or pharmacies once the time allowed to be spent there was
reduced by 3 hours (20-25% reduction in given that earliest opening time for groceries
and pharmacies is 7am, and most open after 9am on the weekends). However it is worth
noting that this empirical approach may practically ignore certain observations in order
to match trends.
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Controls Proportional Weight
Thessaly and Central Greece 0.348
Region Epirus and Western Macedonia 0.330
Aegean 0.323
Time period 2021-01-17 0.955
Notes: Coeff 4.55; 95%CI [3.91, 5.20]. Trends in mobility over time for Attica (blue) and the relevant weighted average of control regions
(red), with the weights used to average pre-treatment time periods in the table below. The dotted line shows the counterfactual estimate of
the parallel trend. The estimated effect is indicated by an arrow.
Figure A3 – Synthetic DiD estimate for the relative mobility in residential locations over
weekends - in the regions of Attica (treated) and (1) Aegean, (2) Epirus and Western
Macedonia, and (3) Thessaly and Central Greece (controls).
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Controls Proportional Weight
Thessaly and Central Greece 0.340
Region Epirus and Western Macedonia 0.333
Aegean 0.327
2021-01-31 0.632
Time period 2021-01-03 0.180
2021-01-23 0.170
Notes: Coeff -14.62; 95%CI [-14.95, -14.28]. Trends in mobility over time for Attica (blue) and the relevant weighted average of control
regions (red), with the weights used to average pre-treatment time periods in the table below. The dotted line shows the counterfactual
estimate of the parallel trend. The estimated effect is indicated by an arrow.
Figure A4 – Synthetic DiD estimate for the relative mobility in groceries or pharmacies over
weekends - in the regions of Attica (treated) and (1) Aegean, (2) Epirus and Western
Macedonia, and (3) Thessaly and Central Greece (controls).
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A2. Data Sources (as reported by iMEdD):
• The Hellenic National Public Health Organisation (EODY) and the Ministry of
Health are the main sources of data that have been released through:
o Announcements and transcripts of the press briefings published by EODY
and the Ministry of Health.
o Reports published in PDF files by EODY.
• Deaths data have been retrieved from fact-checked news reports, published in the
Greek Press, since neither EODY nor the Ministry of Health announce official data
about the geographical location of each death.
A2.1 Replicating the dataset: Mobility data
Google Mobility data are publicly available at https://support.google.com/covid19-
mobility. We use the following variables: residential_percent_change_from_baseline,
grocery_and_pharmacy_percent_change_from_baseline, country_region, and date. We
select the period over 2 January-26 February 2021. This period comprises:
i. Five weekends of observations prior to the introduction of the 6pm weekend curfew in
Attica. In this period the whole country under the daily 9pm-5am curfew unless specified
otherwise (some local 6pm-5am curfews applied at the village/town level due to a rise in
local cases).
ii. Four weekends after the introduction of the 6pm-5am curfew in Attica region but not
in Aegean, Epirus & Western Macedonia or Thessaly regions (with local exceptions as
explained in the main text).
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A2.2 Data accuracy checks
Note that for days with low number of observations, Google records percentage change
as unknown. This is due to not having “enough data to confidently and anonymously
estimate the change from the baseline”, Our dataset does not have such gaps in the data
for any of the regions or dates, suggesting that each day had sufficient number of
observations.
Google also recommends calibration of the data for regions. For example, major national
holidays falling on different days of the week may affect the baseline. Greek Orthodox
Christmas falls on 7th January, with 6th January thus being the Christmas Eve. For both
2020 (the baseline) and 2021 these two days fall on weekdays, thus not affecting the
weekends data.
A2.3 Curfew data
Non-pharmaceutical measures related to Covid-19 are publicly available for most
countries. For Greece, we used the following website:
https://www.taxheaven.gr/widgets/covid19. The data were used to establish the date of
the major change in curfew restrictions (6 Feb 2021) and to verify that the 6pm
restrictions predominantly applied to the Attica region whilst Aegean, Epirus & Western
Macedonia and Thessaly regions remained largely under the original 9pm curfew. For the
robustness checks, we used the following regions as alternative controls: (i) Epirus &
Western Macedonia, and (ii) Thessaly and Central Greece.
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