Electoral Studies 81 (2023) 102573
Available online 28 December 2022
0261-3794/© 2022 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).
When the election rains out and how bad weather excludes marginal voters
from turning out
Søren Damsbo-Svendsen, Kasper M. Hansen *
Department of Political Science, University of Copenhagen, Denmark
A R T I C L E I N F O
Keywords:
Electoral turnout
Individual-level voter panel
Local weather
Climate
Marginal voter
Cost of voting
Participation
A B S T R A C T
Ostensibly random and trivial experiences of everyday life, e.g., local weather, can have significant political
consequences. First, we present a comprehensive meta-analysis of 34 studies of electoral turnout and rainfall –
the vast majority demonstrating a negative association. Secondly, we present a new analysis of a voter panel with
validated turnout for a complete electorate merged with fine-grained meteorological observations to show that
Election Day rainfall reduces turnout by 0.95 percentage points per centimeter, while more sunshine increases
turnout. Marginal voters (young voters) are up to six times more susceptible to bad weather and respond more
positively to pleasant weather. Thus, bad weather exacerbates unequal democratic participation by pushing lowpropensity
voters to abstain. Efforts to include marginal voters therefore ought to be intensified during poor
weather, and elections could even be moved to seasons with more pleasant weather to improve participatory
equality.
Bad weather can depress voter turnout in democratic elections and
with more intense and frequent bad weather expected in the future due
to global warming, local Election Day weather will become increasingly
consequential. Extreme weather at elections is thus one important way
through which global climate change directly and tangibly interferes in
politics. The basic mechanism behind the negative effect of bad weather
on electoral turnout is simple and well-known: it increases the cost of
voting and decreases the net utility of voting (Downs, 1957; Riker and
Ordeshook, 1968). As weather largely is exogenous to human actions,
when measured objectively, experiences with poor Election Day weather
have provided a very concrete way of testing the causal effect of
increasing voting costs. Multiple studies have showed that bad weather,
particularly rainfall, depresses turnout in many different electoral contexts
and climates around the globe. However, notable exceptions exist,
e.g., a null effect in Sweden (Persson et al., 2014) and miniscule positive
effects of bad weather in Norway (Lind, 2020) and South Korea (Kang,
2019).
We have located 34 studies of the rainfall-turnout effect conducted
across high and low salience elections, at different points in time, and
with different types of data and research designs.1
In Table 1, we present
an overview of the studies, key design characteristics, context, and data,
and provide a meta-analysis of weighted and unweighted average effects
in Fig. 1. Most of the identified studies take highly comparable approaches,
typically employing linear models of turnout on a continuous
measure of rainfall at an aggregate level, such as polling districts. Most
of the studies also demonstrate a negative association between turnout
and rainfall, which reflects in a simple, unweighted average effect of
− 0.816 percentage points per centimeter of rainfall (2.073 per inch). A
more appropriate inverse variance-weighting results in a positive
weighted average of 0.003 per centimeter (0.008 per inch), which is
brought about by two recent outlier studies that exhibit very small
positive effects with extremely low standard errors (Gavazza et al.,
2019; Lind, 2020). This reveals the limitation of a generic inverse
variance-weighting approach to meta-analysis; inevitably, it is very
* Corresponding author.
E-mail addresses: sdas@ifs.ku.dk (S. Damsbo-Svendsen), kmh@ifs.ku.dk, kmh@kaspermhansen.eu (K.M. Hansen).
URL: https://www.kaspermhansen.eu (K.M. Hansen).
1
This includes the present study. We have primarily searched Web of Science core collection, Google Scholar, and Google (completed September 2022). The searches
have both been on keywords (e.g. AB=(turnout AND (rain OR rainfall OR precipitation)) and among references within the identified studies. We include peer
reviewed academic articles (31), conference papers (1) and working papers (3) to be as inclusive as possible and increase the likelihood of incorporating, e.g.,
unpublished null results. In the meta-analysis below, we have included one estimate per paper, except for studies that analyze multiple data sets (Knack, 1994;
Persson et al., 2014). We also include studies that revisit previously published data, e.g., the frequently used Gomez et al. (2007) panel dataset, but apply a new
research design or estimation approach.
Contents lists available at ScienceDirect
Electoral Studies
journal homepage: www.elsevier.com/locate/electstud
https://doi.org/10.1016/j.electstud.2022.102573
Received 12 May 2022; Received in revised form 14 December 2022; Accepted 15 December 2022
Electoral Studies 81 (2023) 102573
2
sensitive to studies that identify effects with extremely high certainty.
We therefore present several alternative averages: (a) SE-weighted averages
excluding the two outlier studies that are weighted higher than
all other studies combined, (b) SE-weighted averages excluding
aggregate-level studies, and (c) N-weighted averages. Overall, we
believe (a) − 0.417 is the best of the meta-estimates, which range from
− 0.915 to 0.003.
The 34 rainfall-turnout studies are generally quite comparable in
their empirical approach with some relevant variations, e.g., in the
institutional setup, electoral systems, between high and low salience
elections, and between regions with higher or lower normal precipitation.
For example, the norm of voting as a citizen’s duty should arguably
be higher in high salience elections and in proportional electoral systems,
where all votes count, thus reducing the expected weather effect in
such contexts (Blais, 2000). Moreover, rainfall as a cost of voting should
arguably have stronger effects in normally dry regions. But no such
patterns are apparent. We encourage other researchers to use the metadata,
we have collected, to further pursue these and other questions
(see more study details in Table A12). The strongest rainfall effects are
clearly found in aggregate-level studies of turnout, e.g., municipalities
or constituencies, whereas estimates tend to be non-significant in the
few existing individual-level studies.
The most cited study is Gomez et al. (2007) and the follow-up study
by Hansford and Gomez (2010). Both analyze the same aggregate-level
panel dataset of Election Day weather and turnout at US presidential
elections, and they have influenced subsequent work greatly in terms of
research design and data. The designs used in weather-turnout studies
have evolved tremendously over the past decades from aggregate-level
studies, sometimes based on self-reported turnout, to sophisticated
individual-level designs based on validated voter records and objective
weather observations from nearby weather stations. We take another
step in this direction with a first-of-its-kind study of superior
high-quality data comprising an electorate-wide panel of validated
registry-based voter records. Individual-level panel data allows us to not
just revisit the weather-turnout thesis but contribute with much stronger
evidence of the causal effects of Election Day weather on turnout as well
as the heterogeneity of weather effects.
Electoral turnout is the key health indicator of modern democracy.
When poor weather depresses turnout, it therefore indirectly weakens
democratic legitimacy (Lijphart, 1997; Beetham, 1991). More
Table 1
Overview of 34 studies on the effect of rainfall on voter turnout.
Source Country Election(s) Study level Details
Merrifield (1993) US General (1982) Aggregate (state) AA,–,I
Knack (1994), I US Presidential (1984–1988) Individual (survey, validated turnout) AA,0,I
Knack (1994), II US House (1986) Individual (survey, validated turnout) AA,0,I
Shachar and Nalebuff (1999) US Presidential (1948–1988) Aggregate (state) AA,–,I
Gatrell and Bierly (2002) US (Kentucky) Presidential, state, gubernatorial
(1990–2000)
Aggregate (county) AA,–,T
Lakhdar and Dubois (2006) France Parliamentary (1986–2002) Aggregate (d´epartements) AA,–,I
Gomez et al. (2007) US Presidential (1948–2000) Aggregate (county) AA,–,I
Horiuchi and Saito (2009) Japan Parliamentary (1990–2000) Aggregate (municipality) WP,–,
D
Fraga and Hersch (2010) US Presidential (1948–2000) Aggregate (county) AA,–,I
Hansford and Gomez (2010) US Presidential (1948–2000) Aggregate (county) AA,–,I
Eisinga et al. (2012) The Netherlands Parliamentary (1971–2010) Aggregate (municipality) AA,–,I
Steinbrecher (2013) Germany Parliamentary (1994–2009) Individual (survey) CP,0,L
Art´es (2014) Spain Parliamentary (1986–2011) Aggregate (municipality) AA,–,I
Lo Prete and Revelli (2014) Italy Multiple (2001–2010) Aggregate (city) WP,–,
D
Persson et al. (2014), I Sweden Parliamentary (1976–2010) Aggregate (municipality) AA,0,I
Persson et al. (2014), II Sweden Parliamentary (1991–2006) Individual (survey, validated turnout) AA,0,I
Persson et al. (2014), III Sweden Parliamentary (2002–2010) Individual (survey, validated turnout) AA,0,I
Sforza (2014) Italy Parliamentary (2008–2013) Aggregate (municipality) WP,–,
D
Arnold and Freier (2016) Germany (North-Rhine
Westphalia)
Municipal and state (1975–2010) Aggregate (municipality) AA,–,I
Fujiwara et al. (2016) US Presidential (1952–2012) Aggregate (county) AA,–,I
Chen (2020) Taiwan Parliamentary (1998–2012) Aggregate (county) AA,–,I
Cooperman (2017) US Presidential (1948–2000) Aggregate (county) AA,–,T
Lee and Hwang (2017) South Korea Parliamentary and municipal (1995–1999) Aggregate (municipality) AA,–,I
Arnold (2018) Germany (Bavaria) Municipal (1946–2009) Aggregate (municipality) AA,–,I
Horiuchi and Kang (2018) US Presidential (1948–2000) Aggregate (county) AA,+,I
Stockemer and Wigginton (2018) Canada Parliamentary (2004–2015) Aggregate (districts) AA,–,I
Leslie and Arı (2018) UK Referendum (2016) Aggregate (constituency) AA,–,I
Gavazza et al. (2019) UK Municipal (2006–2010) Aggregate (districts) AA,+,I
Kang (2019) South Korea Parliamentary (2000–2012) Aggregate (districts) AA,–,I
Meier et al. (2019) Switzerland Direct democratic votes (1958–2014) Aggregate (municipality) AA,–,D
Rudolph (2020) UK Brexit referendum (2016) Aggregate (districts) AA,–,I
Garcia-Rodriguez and Redmond
(2020)
Ireland Parliamentary (1989–2016) Aggregate (constituency) AA,–,I
Lind (2020) Norway Municipal (1972–2010) Aggregate (municipality) AA,+,I
Our study Denmark Municipal (2013–2017) Individual (registry, validated
turnout)
AA,–,I
Notes: Abbreviations include AA = academic article (peer reviewed), CP = conference paper, WP = working paper. + = significant positive rainfall effect, 0 =
insignificant effect, – = significant negative rainfall effect, I = included in Fig. 1, D = uses a rainfall dummy variable, L = applies logit regression, T = otherwise
incalculable (comparable estimates are incalculable in six studies). The 27 studies used to calculate the average statistics (Fig. 1) adopt comparable linear regression
approaches. All estimates from these studies have been recalculated to centimeters of rainfall (from inches, meter, or millimeter), and t-statistics have been transformed
into standard errors. For IV-studies, we report the linear first-stage effect of rainfall on turnout. Studies are sorted by year of publication (oldest first). See appendix for
more details (Table A12).
S. Damsbo-Svendsen and K.M. Hansen
Electoral Studies 81 (2023) 102573
3
importantly, bad weather can exacerbate inequalities in electoral
participation because obstacles to voting affect marginal voters more
than core voters (Gomez et al., 2007; Bhatti et al., 2020). Whereas core
voters with a robust voting habit and a strong sense of civic duty are
largely immune to the costs of bad weather, marginal voter groups may
be highly susceptible (Knack, 1994).
Our new study of the weather-turnout effect features several novelties
and advantages. First, it is the first to use validated turnout data at
the individual level with repeated measurements of an (almost) complete
electorate.
Secondly, these unique data allow us to estimate individual-level
panel models, which rule out certain types of omitted variable bias
that previous research had to rule out by assumption. We thus contribute
to the turnout literature with more credible results.
Thirdly, we investigate how obstacles to voting affects marginal
voters (i.e., young voters) to a higher degree than core voters, which is a
question with major democratic implications, but also one that requires
a very large number of observations.
Fourthly, previous work has focused almost exclusively on rainfall
with the occasional addition of another weather variable, for example
temperature (Gatrell and Bierly, 2002; Stockemer and Wigginton,
2018). However, weather – poor or pleasant – is a compound phenomenon
determined by precipitation, temperatures, cloud cover, humidity,
wind speed, etc. Previous work that only includes rainfall is therefore
essentially underspecified. We take a first step toward a more comprehensive
approach by analyzing rainfall, solar irradiation (sunshine), and
temperature together, thus allowing us to explore if nice Election Day
weather also shapes turnout. We also explore nonlinear weather effects.
Finally, the setting is new. Danish media routinely make anecdotal
references to the relationship between turnout and weather, but the
relationship has yet to be systematically examined in the case of
Denmark. So far, researchers have not found evidence of detrimental
weather effects in any of the five Nordic countries (Bengtsson et al.,
2014; Lind, 2020; Persson et al., 2014). Like the other Nordics, Denmark
is a highly cooperative multiparty system with automatic voter registration
and a high average local election turnout of around 70% (Hansen,
2020). Unlike the other Nordics, the geography is highly
homogeneous, small, and flat with a uniform temperate and predominantly
coastal climate.
Our results show that local rainfall does in fact cause a reduction in
the probability of voting. On average, the probability of turning out for
election decreases by 0.95 percentage point when rainfall increases by 1
cm (2.41 per inch). This estimate is close to the simple, unweighted
average effect in the meta-analysis (Fig. 1). The negative effect of
rainfall is nonlinear in the sense that small amounts of rainfall are
inconsequential, whereas heavy rain makes a substantial difference. We
also find positive, mostly linear turnout effects of higher levels of sunshine,
i.e., nice Election Day weather. Crucially, young voters (except for
first-time voters) are affected up to six times more by rainfall, and are
also affected more by sunshine, suggesting that marginal voters are
much more susceptible to weather-induced costs of voting and that bad
weather can exacerbate inequality in democratic participation.
Fig. 1. Meta-analysis of 27 studies on the effect of rainfall on voter turnout. N = 27. The meta-analysis calculates weighted average effects using generic inverse
variance-weighting of the unstandardized linear regression coefficients for rainfall (in cm) and turnout (percent) using the formula: SE–weighted average =
∑N
1
estimatei × 1
standard error2
i
∑N
1
1
standard error2
i
. N-weighted averages substitute the number of observations N for the 1
standard error2
i
weight. Prior averages exclude our study, posteriors include it.
*Estimates excluding Lind (2020) and Gavazza et al. (2019) with higher inverse variance-weights than all other studies combined. The dashed line marks the unweighted
average.
S. Damsbo-Svendsen and K.M. Hansen
Electoral Studies 81 (2023) 102573
4
1. Theory: how bad weather on Election Day increases the cost
of voting
A growing body of research shows how weather affects political
opinions and behavior in a multitude of ways, both through dramatic
extreme weather events and more subtle variations in personal weather
experiences. For instance, individuals use the seasonality or normality of
recent temperatures as a heuristic when expressing their opinions on
climate change (Damsbo-Svendsen, 2021; Howe et al., 2019), while
flooding experience strengthens perceptions and concern of climate
change (Ogunbode et al., 2020; Rüttenauer, 2021). The effect of local
weather on turnout is another prime example of how local weather,
increasingly because of global climate change, interferes directly in
politics.
The mechanism through which bad weather depresses turnout is
simple and highly intuitive: it increases the cost of voting in a very
tangible sense. In bad weather, getting to and from the polling place and
standing in line outside is often unpleasant and involves more logistic
consideration and planning. Campaigners also experience a higher cost
of canvassing in bad weather, which can dampen their level of activity
and reduce mobilization effects (e.g., Eisinga et al., 2012; Gomez et al.,
2007). In addition, bad weather can induce a bad mood, or even a state
of light depression, which may also help explaining why both voters
(Howarth and Hoffman, 1984; Meier et al., 2019) and campaigners are
more likely stay at home in bad weather (Lamare, 2013).
The other side to the argument is rarely explored; what if the weather
not only presents obstacles to turnout, but pleasant Election Day
weather instead is conducive to turnout? The limited research on positive
weather effects on turnout suggests that warmer, more pleasant
weather could boost turnout rates (e.g., Lakhdar and Dubois, 2006;
Eisinga et al., 2012; Van Assche et al., 2017). Some researchers argue
that the influence of weather partially reflects its effects on the enjoyability
of (outdoor) activities other than voting – on the opportunity
costs of voting, in other words (Kang, 2019; Lind, 2020). In theory, poor
weather could indeed increase turnout and pleasant weather reduce it.
But the bulk of existing literature suggests that the change in the direct
costs of voting more than offsets the potential effects on opportunity
cost, which seems plausible given that special, symbolic character of the
voting act as compared to other types of activities.
Another important question requiring attention is if weather-turnout
effects are linear, as often assumed; does a centimeter of rain have the
same implications on a mostly dry Election Day as it would in an election
soaking in rain? Merrifield (1993) found no evidence of nonlinearity,
whereas Meier et al. (2019) more recently did. We provide new evidence
to this question as well.
We emphasize the importance of weather effects on turnout among
marginal voters and draw on Fowler’s understanding of “those whose
decisions to turn out are sensitive to exogenous factors” (2015: 205). The
term thus refers to eligible voters who, for various reasons, are particularly
sensitive to increasing costs of voting. Young people, for example,
are typically marginal voters because they have not had the opportunity
to establish a robust voting habit (Bhatti and Hansen, 2012a; 2016). We
expect weather effects, negative and positive, to exhibit stronger influence
on marginal voters such as young voters, but also, for instance,
those who live alone and are disinclined to make going to the polls a
social activity (Dahlgaard et al., 2021), voters with a low estimated
propensity of voting (Enos et al., 2013), and voters with a history of
abstaining. This heterogeneity is key to understanding the implications
of poor Election Day weather for electoral inequalities and democratic
representation.
2. Research design: fixed effects modelling of individual turnout
and local weather
We approach the question of how Election Day weather influences
turnout through the case of municipal elections in Denmark. Danish
local elections take place simultaneously in all 98 municipalities every
four years on the third Tuesday of November. The fixed election
schedule holds constant various seasonality effects, and the wet and
windy November weather makes it a useful case for exploring the effects
of bad weather. Danish local elections are proportional elections of
municipal councilors, who elect a mayor from among their midst. The
municipalities administrate the lion’s share of the universal welfare
budget including schools, kindergartens, eldercare, local roads, and
public transportation. The salience of the local elections is therefore
comparatively high with an average turnout of 70% over the last 50
years, yet significant variation remains within individuals and across
groups (see Hansen, 2020 for detailed turnout statistics). Voters are
automatically registered in voter files, and all eligible citizens receive a
polling card by mail before the election. In-person voting on Election
Day is a strong norm (94.5% in 2017, the remainder mostly comprises
early voting, e.g., in retirement homes), which allows for reliable
matches of turnout behavior and individually experienced Election Day
weather.
2.1. Voter turnout
Validated voter turnout records are collected for virtually all eligible
voters in the 2013 and 2017 local elections in Denmark.2
We exclude
early voters (~3% of the data set) to create a binary outcome variable
indicating individual-level turnout at the polls on Election Day (1) or
abstention (0). Hence, all included voters showed up in person at their
local polling station to cast their vote. We further restrict the data to
voters of age 80 or younger because of significant selection among the
elderly in early voting and health, and because of sharply decreasing
observations at older ages. We acknowledge the normative issues with
this step and encourage other researchers to focus more on elderly voter
groups (see Bhatti and Hansen, 2012b). Out of the 4,459,145 unique
eligible voters in our final data set, 3,306,504 (~73%) were eligible at
both elections and thus appear twice in our panel.3
Voters in their early twenties are a group with relatively low turnout
rates in many places, including the Nordic countries and the US (Bhatti
et al., 2012). Thus, a distinct turnout-age “rollercoaster” also exists in
Denmark (see Figure A1): turnout rates are high for new voters but
decrease sharply when the young voters move away from home before
growing steadily from the mid-twenties until sometime after age 70
(Bhatti et al., 2012; Bhatti and Hansen, 2012a,b; Hansen, 2020).
2
Individual turnout is registered at the polling station as part of the electoral
procedures. Voting records are thus administrative data administrated by the
municipalities and based on the voting system (voter lists). At the 2013 local
election, all municipalities took part in the effort to collect voting records, and
we thus collected validated records for 98.9% of eligible voters (the negligible
data loss is due to system failure and administrative errors). At the 2017 local
election, we managed to collect validated records for 91.3% of eligible voters (7
out of 98 municipalities decided not to participate because they found it too
time consuming). Crucially, there is no individual selection into the collection
of voting records, which means that they are representative of the country on
other characteristics (see Bhatti et al., 2014; Hansen, 2018). The records are
checked for typos, aggregated to the polling station level, and cross-validated
against the official election result. From the voting records, we have access to
the following individual-level variables: turnout (voted early, abstained, voted
on Election Day), birthday, gender, municipality, and home address geographic
coordinates.
3
The drop in observations in the balanced portion of the panel is explained
by (a) our imposed age restriction (18–80), (b) some voters living in, or moving
to or from, one of seven non-participating municipalities (in 2017) (c) voters
becoming eligible between 2013 and 2017 (e.g., being a first-time voter or a
recently enfranchised immigrant in 2017), or passing away, or, to a lesser
extent, (d) other voters that make use of early voting in at least one election.
S. Damsbo-Svendsen and K.M. Hansen
Electoral Studies 81 (2023) 102573
5
2.2. Election Day weather
The area of Denmark proper is 42,933 km2
(16,577 square miles) –
larger than Switzerland but smaller than Costa Rica, larger than Maryland
but smaller than West Virginia. There are no mountains (the
highest point is 171 meters above sea level) and the entire country
shares a highly homogeneous temperate coastal climate (Danish Ministry
of Energy, Utilities and Climate, 2017). Consequentially, annual
rainfall exhibits very low geographical variation ranging from 50 cm in
the driest regions to 90 cm in the wettest (DMI, 2018). We collect
weather observations from the Danish Meteorological Institute (DMI)
through the new publicly accessible Climate Data API, which provides
detailed weather observations validated by meteorologists (DMI, 2021).
Weather observations from the elections are available for 95, 27, and 45
weather stations for precipitation, solar irradiance, and temperature,
respectively (Fig. 2). Thus, solar irradiation inevitably relies more on
interpolation (plausibly a minor issue for the measurement validity of
sunshine compared to if it had been, e.g., precipitation).
Rainfall is measured as accumulated precipitation (cm), sunshine as
average solar irradiation (W/m2
, normalized), and temperature as
average degrees (Celsius). We refer to solar irradiation as sunshine,
although it also captures the sun’s radiation though the cloud cover in
the absence of visual sunshine. Election Day weather is calculated as the
cumulative sum of rainfall and simple average of sunshine and temperature
based on hourly observations from midnight until the polling
stations close at 8pm.4
The average distance between voters and their nearest weather station
is 10.2 km for rainfall, 17.0 km for sunshine, and 13.4 km for
temperature. For each voter, election date, and weather parameter, we
triangulate local weather from the three nearest weather stations and
compute an inverse-distance weighted average based on the following
formula5
:
Local weathervt =
∑3
i
(
Observationit
Distance2
i
)
∑3
i
(
1
Distance2
i
)
where i indexes the weather station (from nearest to third nearest), t
indicates the election date, and v indicates the voter. Voters are thus
assigned unique values based on the geographical coordinates of their
home address. For example, a voter who lives 10, 20, and 30 km from
weather stations measuring 5, 10, and 0 mm of rain is assigned a
weighted average of 5.51. Due to the distance weighting, approximately
73% of this estimate is contributed by the 10 km-station, 18% by the 20
km-station, and only 8% by the 30 km-station. For voters living close to a
weather station, the lion’s share of the weather estimate will come from
that station. This procedure increases measurement validity and variation
compared to matching voters to only the nearest station, especially
when stations are relatively far away (Wang and Scharling, 2010).
Table 2 describes the variation in Election Day weather and
Figure A2 in the appendix shows maps of the weather distribution.
Variation is limited, but still sufficient due to the large number of observations.
The observed Election Day weather is relative mild and
typical (i.e., non-extreme) for November weather in Denmark, and even
the maximum observed sunshine is several times lower than on any day
in the summer. This arguably makes for a stronger test of the weatherturnout
thesis; if we can detect it here, it is likely to be even more
substantial in cases with more extreme weather.
3. Estimation strategy and models: OLS and two-way fixed
effects
We estimate two types of linear probability models (OLS) with fixed
effects: a pooled OLS model and an individual-level two-way fixed effects
model. In the pooled model, we pool all eligible voters from both
elections and regress Election Day turnout (voted in-person or
abstained) on local rainfall, sunshine, and temperature with municipality
fixed effects to account for (time-invariant) municipal-level patterns in
local climate and voting behavior and election fixed effects to account for
general (unit-invariant) differences between elections. We also add
controls for age (third degree polynomial), gender, living close to the
coast, local population density (natural log), closeness of the election,
and municipal population size (natural log) and share of non-Western
immigrants.6
The latter two are especially important determinants of
turnout in Danish local elections (Bhatti and Hansen, 2019). Adding the
control variables should result in more precise estimates, and it may also
reduce confounding, especially in the pooled model, to the extent that
the control variables are correlates of local weather patterns. Given that
the study spans just four years, climate change per se, i.e., changes in
normal weather, is effectively held constant, and, similarly, local
turnout patterns are also highly invariant across the country within this
short time span, thus alleviating concerns about problematic spatiotemporal
trends (Lind, 2017). Since weather patterns tend to be spatially
dependent, the pooled model uses cluster-robust standard errors at the
level of polling districts and election (alternative standard errors are
provided in Table A10).
Estimates from the pooled model have a causal interpretation to the
extent that weather experiences are allocated as-good-as-random within
municipalities (Lind, 2017). This assumption has been invoked
frequently in studies of weather effects (e.g., Egan and Mullin, 2012;
Persson et al., 2014) and studies that use rainfall as an instrumental
variable (Lind, 2020; Miguel et al., 2004; Sforza, 2014). While the
assumption of as-if randomness in weather exposure is not self-evident –
and has been criticized recently (Mellon, 2021) – we think it is relatively
reasonable in small, flat, and temperate Denmark (conditional on municipality
and living close to coasts). As the appendix shows, weather
exposure is also reasonably, however not perfectly, balanced across age,
gender, distance to coast, and local population density (see
Figure A3-A8).
The key threat to identification in the pooled model is if where
people decide to live is related to the local climate and weather. Naturally,
there is substantial sorting in where individuals and families
choose to live based on, e.g., income, education, profession, having
children, and these factors also shape voting behavior. However, to
induce confounding there needs to be significant systematic correlations
between residential areas and the local climate within municipalities.
While this is possible in theory, the modest variation in local weather in
Denmark likely translates into weak correlations between areas and
local weather. Still, to effectively eliminate this bias risk, we utilize the
panel structure of the data to apply voter fixed effects in our second
estimation strategy.
The panel model substitutes voter fixed effects for municipality fixed
effects, thus establishing a classic two-way fixed effects (TWFE)
4
Polling stations open at 8am. We begin measuring at midnight rather than
8am because weather conditions from before the polling stations open,
including in the night, can carry into the morning and day and influence voters’
decision-making. Specifically, we assume, first, that voters make and change
their plans before going to the polls, especially in the morning right before the
polls open, and, secondly, that voters possibly incorporate memories of the
weather a couple of hours before into judgements of the current weather.
5
This resembles DMIs interpolation algorithm (Wang and Scharling, 2010),
including the weighting by squared distance to give preference to nearby
observations.
6
Closeness of the election is measured as vote share difference between the
largest party and the runner-up (Fraga and Hersch, 2010; Knack, 1994). Living
close to the coast is a dummy variable for living less than 5 km from a coast.
Non-Western immigrants in the municipality are a proxy for individual-level
immigrant status.
S. Damsbo-Svendsen and K.M. Hansen
Electoral Studies 81 (2023) 102573
6
estimator. This model estimates weather effects within voters over time
and, like the pooled model, also accounts for general variation between
the two elections. Specifically, we estimate the following equation:
Turnoutit = αi + αt + β1Rainfallit + β2Sunshineit + β3Temperatureit + Zit
+ εit,
(1)
where αi is individual fixed effects, αt is time fixed effects, and Zit is a
vector of time-varying control variables. The panel model is identical to
the pooled model, except that it replaces municipality fixed effects with
individual fixed effects (and drops the time-invariant gender control).
Since the panel model is modelling within-voter variation, we use
cluster-robust standard errors that account for address (household) and
election (time) dependence (Table A10 provides alternative standard
errors).
This individual-level panel design eliminates any omitted variable
bias from invariant voter characteristics such as gender, personality
traits, and past voting experience. It also implies a strong geographical
control, which eliminates selection bias related to geography, but only
for voters who did not move (to a different local climate) between the
elections. Around 86% lived in the same municipality at both elections.
For movers, the individual fixed effects still greatly reduce variation in
factors that shape decisions about where to live (income, education,
ideology, children, etc.). A drawback of the panel model is that it only
effectively models the balanced portion of the panel, i.e., the 73%
observed at both elections.7
As ours is the first study to use an individual-level panel design, and
to use official voting records for a complete electorate, we can provide
more credible results on the causal effects of Election Day weather on
turnout. Furthermore, our side-by-side presentation of results from a
pooled model and a panel model enables us to empirically gauge the
possible biases from designs that rely on the assumption of as-good-asrandom
weather exposure.
4. Results: rainfall reduces turnout, sunshine increases it
This section presents the results of our analysis of the relationship
between Election Day weather – rainfall, sunshine, and temperature –
and individual turnout. After investigating the weather-turnout thesis at
the general level, we provide evidence that the rainfall effect is
nonlinear and show how young voters are much more susceptible to
weather effects, negative and positive, than more experienced voters.
Table 3 shows the main models’ effect estimates. Recall that the
panel model is methodologically superior for identifying causal weather
effects because it eliminates bias from unit-invariant and time-invariant
factors, i.e., voter characteristics that are effectively constant between
the two elections. The pooled model, with only election and municipality
fixed effects, should yield similar results to the panel model to the
extent that individuals are exposed to local weather in an as-if random
fashion (within municipalities).
As expected, the effect of rainfall on the probability of voting is
negative and statistically significant in both models. Some voters are in
fact deterred from voting when it rains. Based on the panel model, each
centimeter (0.39 inches) of rainfall reduces the probability of voting by
0.95 percentage points.
Sunshine on Election Day also affects voting propensity, even after
controlling for rainfall and temperature, with which it naturally
Fig. 2. Locations of 166 DMI weather stations in Denmark.
Table 2
Descriptive weather statistics.
Full sample 2013 Election 2017 Election
Weather variable Mean (SD) Min – Max Mean (SD) Min – Max Mean (SD) Min – Max
Acc. rainfall (mm) 1.2 (1.6) 0.0–10.0 2.3 (1.5) 0.1–8.5 0.1 (0.4) 0.0–10.0
Avg. sunshine (W/m2
) 19.7 (6.7) 6.2–42.9 15.2 (5.0) 6.2–35.8 24.5 (4.8) 6.5–42.9
Avg. temperature (◦
C) 4.3 (2.2) − 0.5–7.8 6.2 (0.4) 5.2–7.8 2.3 (1.4) − 0.5–5.4
Source: DMI (2021). Measured and summarized at the level of 7,855,649 voters.
7
See footnote 3. Importantly, self-selection into the panel is highly limited.
The pooled model does not exclude these groups.
S. Damsbo-Svendsen and K.M. Hansen
Electoral Studies 81 (2023) 102573
7
correlates.8
A change from the lowest to the highest recorded level of
sunshine (6.2–42.8 W/m2
) increases the probability of voting by 1.55
percentage points, according to the panel model. By implication, a thick
cloud cover also reduces turnout. The fact that sunshine affects voting
behavior after adjusting for rainfall supports the notion that weatherturnout
effects are not only about rain-induced costs and inconveniences,
but also about whether voting is a pleasant activity. What
is less clear, however, is if the sunshine effect mostly reflects that voting
is more enjoyable and convenient, including transportation to and from
the polling place, or rather reflects psychological effects.
For temperature, results are less clear, as the models disagree. In the
panel model, an increase from the lowest to the highest observed Election
Day temperature (− 0.5–7.8 ◦
C) implies an increase of 1.30 percentage
points in the probability of voting.
Most existing studies only examine the effect of one weather variable
(at a time), and predominantly rainfall. To allow for a more direct
comparison, and as a robustness check to alleviate concerns about
multicollinearity, Table A5 in the appendix shows effects for rainfall,
sunshine, and temperature, estimated separately. Here, the panel model
indicates turnout effects of − 1.50 percentage points for rainfall, 0.32 for
sunshine, and 0.20 for temperature (approximately 14–58% stronger
effects).
The panel model greatly reduces any confounding from factors that
shape where voters live but are roughly constant over a four-year period.
Thus, the difference between estimates from the two models can tell us
something about the validity of the as-good-as-random assumption, i.e.,
that local weather exposure is orthogonal to individual factors. The
absolute value of panel model estimates are 2.6 times smaller, 1.6 times
smaller, and 16 times larger, respectively. This indicates that relying
entirely on the assumption of as-if randomness could lead to severe bias,
which echoes recent critiques of treating weather exposure and rainfall
as randomly distributed (Cooperman, 2017; Lind, 2017; Mellon, 2021).
In fact, individual characteristics may be systematically associated with
variations in local weather. We should therefore be cautious with
(pooled) cross-sectional analyses of weather and political behavior and
place more trust in estimates from panel models. Future studies should
aim to employ panel designs, natural experiments, or at least more
comprehensive selection-on-observables designs to isolate conditional
randomness in weather exposure.
5. Nonlinear effects: the effect of rainfall depends on the
baseline
The next millimeter of rain could have very different implications
depending on the amount of rain that has already fallen. In other words,
weather effects may be nonlinear, but the literature only shows limited,
inconclusive evidence about this (Merrifield, 1993; Meier et al., 2019).
We test the proposition by adding squared rainfall, sunshine, and temperature
terms to the panel model. The marginal effects of rainfall and
sunshine depending on the baseline is shown in Fig. 3.
Whereas the sunshine effect is closer to linear, the rainfall effect is
strongly nonlinear.9
In the case of no rainfall, at the outset of the graph
(left panel), an additional millimeter of rain does not affect turnout. The
rainfall effect kicks in after a baseline of around 2 mm of rain (0.08 in),
where it begins to impose real costs on planning and outdoor activities
(Figure A9 illustrates the nonlinear relationship between predicted
turnout and weather). The strongest rainfall effect, at the maximum
observed precipitation of a full centimeter, amounts to almost − 4 percentage
points per centimeter.10
6. Heterogeneous effects: marginal voters are much more
sensitive to weather effects
If increased weather-induced costs of voting exacerbate inequalities
in turnout, this has implications for electoral outcomes, representation,
and, ultimately, the legitimacy of democracy. We hypothesize that
marginal voters are more susceptible to adverse weather effects on their
voting behavior – and likely also to the positive effects of pleasant
weather. Below, we test if weather effects are moderated by voter age or,
more specifically, election cohort. Election cohorts reflect the exact
number of local elections in which a voter has had the opportunity to
vote by the 2013 election.11
For cohort 1, for example, 2013 was the first
election in which they were eligible to vote and 2017 was the second.12
This approach allows us to compare cohorts of experienced voters to
cohorts of new voters who have yet to establish a voting routine and,
consequently, might be more readily swayed by the perceived costs and
benefits of voting.
We extend the panel model (see Table 3) to include interaction terms
between each weather variable and the election cohort, i.e., the number
of municipal elections experienced as an eligible voter by the 2013
election. We treat election cohort as a categorical variable to allow
flexibility in the relationship between weather, turnout, and cohort.
Fig. 4 shows how the partial effects of rainfall and sunshine are
strongly moderated by whether one is an experienced voter (or nonTable
3
Turnout and Election Day weather.
Pooled model Panel model
Election Day turnout
(1)
OLS
(2)
TWFE
Rainfall (cm) − 0.0250*** (0.0005) − 0.0095*** (0.0005)
Sunshine (W/m2
normalized) 0.0045*** (0.0004) 0.0028*** (0.0000)
Temperature (Celsius) 0.0001 (0.0005) 0.0016*** (0.0001)
Election FEs + +
Municipality FEs +
Voter FEs +
Additional controls + +
Cluster-robust SEs Polling district + election Address + election
N observations 7,855,649 7,855,649
N unique voters 4,549,145 4,549,145
Note: Additional controls include age, age,2
age,3
ln(population), close to coast
dummy, ln(local population density), non-western immigrant population share,
closeness of the election, and gender (only in the pooled model). See all coefficients
in the appendix, Table A2. Cluster-robust SEs in parentheses clustered
on (1) polling district and election and (2) address/household and election. ***p
< 0.001; **p < 0.01; *p < 0.05.
8
Rainfall, solar irradiation, and temperatures are inherently correlated,
which could raise suspicions of collinearity issues. For instance, it rarely rains
when the sun is shining. However, this is mostly true at extreme values of the
weather variables, i.e., outside the range of weather observed here, and
bivariate weather correlations are weak to moderate – ranging from − 0.01
(rainfall and sunshine in 2013) to − 0.42 (sunshine and temperature in 2013).
See appendix for bivariate weather variable correlations (Table A1) and separate
models for each weather variable, which yield the same overall conclusions
(Table A5).
9
Based on the quadratic terms in Table A3.
10
The same graph based on the pooled model shows the same nonlinear
pattern for the rainfall effect, only substantially stronger (Figure A4).
11
This assumes that all voters either were born in or immigrated to Denmark
before their eighteenth birthday.
12
Correspondingly, the age of cohort 1 in 2013 ranges from exactly 18
years–22 years (and 1 day), while the age of cohort 2 in 2013 ranges from 22
years (and 2 days) to 26 years (and 3 days). More details in Table A4.
S. Damsbo-Svendsen and K.M. Hansen
Electoral Studies 81 (2023) 102573
8
voter) or a debutant.13
The negative rainfall effect is very strong for
young voters, especially the cohorts 2 and 3 for whom the 2013 election
was the second and third election in their adult life. In cohort 2, the
rainfall effect is − 5.63 percentage points per centimeter of rainfall – an
almost six times stronger effect than the average effect of − 0.95. Strikingly,
the negative effect is virtually non-existent for the youngest firsttime
voters. This mirrors absolute turnout rates depicted in Figure A1
and is probably primarily explained by the fact that parents make sure to
bring their adult children, who typically still live at home, along to the
polls (Bhatti and Hansen, 2012a). For more mature voters, the rainfall
effect gradually wanes and disappears until it shows signs of reversing at
old ages.14
Election Day sunshine shows the same pattern, i.e., a relatively
strong effect on voters in their twenties and early thirties, who have
experienced two to three elections in their adult life, followed by a
gradual tapering off. Turnout in the youngest cohort is unaffected by
nice weather as well, whereas slightly more seasoned voters, who have
typically left their childhood home, are more susceptible.
In sum, the analysis supports the hypothesis that marginal voters are
more susceptible to exogenous changes to the costs and benefits of
voting. Except for first-time voters, young and inexperienced voters are
deterred from voting by bad weather to a much larger degree than more
mature voters. But they are also drawn to the polls by the positive effect
of pleasant weather to a higher degree, which shows the weather-voting
effect is not merely negative, related to obstacles, costs, and inconvenience,
but has a positive counterpart in nice weather, which can make
voting a more appealing activity. The implication is that how the
weather turns out on Election Day potentially induces representational
inequalities into the Election not only due to geography (if it rains in
only one region of the country, fewer voters will turn out there), but
crucially also because marginal voters are affected much more strongly
than core voters.
7. Robustness checks: consistency across model specifications
and samples
In the appendix, we report additional evidence and several robustness
checks in support of our conclusions. First, as mentioned, we show
that effect estimates are similar, generally slightly higher, when only one
weather variable is included in the panel model at a time (Table A5).
These alternative estimates may be more directly comparable to existing
studies and, furthermore, circumvent potential multicollinearity issues.
Secondly, we show that effect estimates are slightly different when
no control variables are added, but not to an extent that alters the
conclusions (Table A6). This speaks to the discussion about whether
individual weather exposure largely is exogenous or if it depends on
covariate adjustment and the selection-on-observables assumption.
Thirdly, despite being a minor concern in the geographic case of
Denmark, we attempt to account for the fact that some voters move to
another municipality, where the local climate may be different. We do
this by (a) adding municipality fixed effects to the panel model and (b)
estimating the panel model with a sample restricted to the approximately
86% who did not move to another municipality, thus controlling
completely for geography by estimating effects within non-movers. Both
approaches result in only marginally different estimates (Table A7).
Relatedly, we restrict the sample to the approximately 1.8 million voters
between age 40 and 59, for whom potential confounders, e.g., education,
occupation, children, income, are closer to invariant over time and,
thus, more successfully eliminated in the panel model. Despite discarding
64% of the data, these resulting estimates are remarkably
consistent with the full-sample estimates (Table A7).
Fourthly, in some rare cases, hundreds of voters share the same home
address and, as a result, also share the exact same weather exposure.
Specifically, 492 addresses (0.02%) have more than 100 eligible voters
registered. These people may live in dorms, retirement homes, or be
assigned a generic address such as the town hall because they are, e.g.,
homeless, expats, or deployed military personnel. This could cause
measurement error but restricting the sample to addresses with less than
100, 50, and 10 residents does not substantially change the results
Fig. 3. Evidence of nonlinear weather effects on turnout. The graphs show marginal weather effects computed for different levels of baseline weather intensity.
Estimates are based on the panel model (see Table 3) with second-order weather terms added to the equation. See regression table (Table A3), pooled model graphs
(Figure A10), and temperature graph (Figure A11) in appendix. 95% CIs (cluster-robust SEs).
13
Graphs drawn for the pooled model can be found in the appendix
(Figure A12). The pooled model allows for estimation based on the entire
sample (e.g., including first-time voters in the 2017 election) and for number of
elections to be based on exact age at the elections rather than cohort. The same
general pattern emerges, albeit more distinctly and with much higher effect
estimates. Most importantly, the starkest contrast in the pooled model is also
between first-time and second-time voters.
14
The reversal at high ages should be interpreted with caution because of
possible selection issues at this end of the spectrum. In short, it tends to be more
healthy elderly individuals who are included in the analysis (less healthy individuals
are more likely to effectively be excluded due to early voting in
retirement homes or passing away between the elections).
S. Damsbo-Svendsen and K.M. Hansen
Electoral Studies 81 (2023) 102573
9
(Table A8).
Fifthly, observations from only the nearest weather station may
intuitively appear more valid than a weighted average of the nearest
three, especially for voters living close to one. Although weather estimates
tend to be dominated by the nearest station, we nevertheless also
empirically examine how sensitive results are to the triangulation procedure
compared to just using the nearest observation by estimating the
panel model with only the nearest weather observation. At the same
time, we apply an increasingly restrictive maximum distance to the
nearest weather station – varying from 30 km to 3 km – to exclude faraway
voters. Figure A14 shows that estimates are stable and consistent
with the main results down to around the 10 km boundary, after which
the sample likely become severely biased.
Sixthly, we conducted a placebo test using mock weather from 30 to
60 days before the elections, which clearly show that Election Day
weather has an especially high impact on turnout. The placebo coefficients
for rainfall amount to merely 2.8% and 10.4% of the real coefficient
in the pooled model (in absolute terms) and 0.4% and 4.2% in
the panel model. The largest rainfall placebo effect in the panel model is
thus a 0.04 percentage point turnout reduction per centimeter of rain
(compared to the true value of 0.95). The placebo coefficients for sunshine
amount to 0.2% and 0.4% (pooled model) and 7.1% and 0.7%
(panel model) of the real coefficient.
Seventhly, with weather data it is notoriously hard to choose the
right standard errors that correctly take into account the various dependencies
in the data (Cooperman, 2017; Lind, 2017). We have used
(cluster-robust) standard error estimators that we believe sufficiently
account for the most important dependencies. But since the reader may
Fig. 4. Marginal turnout effects of rainfall and sunshine conditional on election cohort or the number of elections experienced.
The graphs show estimated marginal weather effects for each cohort of voters that have experienced 1–15 elections as adults (including the 2013 election). Election
cohorts largely, but not perfectly, correspond to voter age in 2013 (square brackets). Estimates are based on the panel model (see Table 3) with interaction terms
added between each weather variable and election cohort (categorical). The model includes all the control variables as reported in Table 3. Temperature graphs and
pooled model graphs are provided in appendix. 95% CIs (cluster-robust SEs).
S. Damsbo-Svendsen and K.M. Hansen
Electoral Studies 81 (2023) 102573
10
hold different views, Table A10 provides an extensive list of alternative
standard errors.
Finally, we provide the main models as logistic regressions to show
that they produce identical results in terms of direction and statistical
significance (Table A11).
8. Conclusion and discussion: weather experience and electoral
turnout
The effect of weather exposure on electoral turnout is an appealing
illustration of the cost of voting theory. Bad weather on Election Day
increases the cost of voting, which means that rainfall should reduce
turnout. Numerous studies have investigated this relationship in
different contexts and with varying data and research designs. But recent
studies challenge the conventional wisdom and important questions
remain unanswered. With this article, we bring new empirical evidence
to these questions.
First, our meta-analysis of 27 comparable weather-turnout studies
shows that a clear majority find a negative effect of rainfall on turnout,
which results in a naïve average effect of − 0.82 percentage points per
centimeter of rainfall. However, two recent studies, which find puzzling
(although not necessarily inexplicable) positive associations between
rainfall and turnout with extremely low uncertainty (Gavazza et al.,
2019; Lind, 2020), dominate the inverse variance-weighted average,
which normally would be a more appropriate estimate than the simple
unweighted average. When we exclude these two outlier studies, we get
a weighted average effect of − 0.42 percentage points per centimeter of
rainfall, and other alternative meta-analytical estimates range between
− 0.915 and 0.003 (Fig. 1).
To bring improved evidence to the still unsettled question of how
Election Day weather shapes turnout, we proceeded with a stand-alone
study that has several significant advantages over the existing body of
work. Most importantly, it is the first individual-level study based on
validated voting records for an entire electorate at two consecutive
elections. Our administrative voter records have low measurement
error, enable us to improve individual-level causal inference in panel
models, and allow us to investigate heterogeneous effects. Using these
superior panel data, we show that bad weather does indeed depress
turnout at the individual level. Specifically, we estimate that a centimeter
of rainfall reduces the probability of voting by 0.95 percentage
points. This rainfall effect is substantial and comparable to, for example,
the effects of Get-Out-The-Vote campaigns (Hansen, 2020).
Rainfall effects are nonlinear in the sense that the first millimeter of
rainfall is largely inconsequential for turnout, whereas turnout decreases
more drastically at high levels of baseline precipitation. This
arguably suggests that as we expect more extreme weather in the future
due to global heating, including more frequent and intense cloudbursts,
we should expect to occasionally see participation in elections severely
affected by bad weather.
We also find that higher levels of sunshine, i.e., more pleasant
Election Day weather, significantly increase the probability of voting net
of rainfall and temperature effects. Both the negative rainfall effect and
the positive sunshine effect correspond with the logic of the cost of
voting; bad weather increases the cost associated with transportation to
and from the polling place, standing in line, etc., while pleasant weather
reduces these costs and makes voting a more enjoyable activity.
A crucial component in our understanding of weather effects on
turnout is their heterogeneous character. Weather effects would not
necessarily be important if all citizens were influenced equally. That
would simply imply a higher or lower overall turnout depending on the
weather with no implications for democratic representation or electoral
outcomes. But if marginal voter groups tend to be more susceptible, bad
Election Day weather can cause severe electoral inequalities.
We show that young voters, who have typically left their childhood
home but not yet established a robust voting habit, are much more
susceptible to the weather. The marginal turnout effect of rainfall peaks
for voters in their mid-twenties, who are not first-time voters, but rather
have experienced two or three elections as eligible voters, and who are
unlikely to still live with their parents. Specifically, in this young voter
group, each centimeter of rain reduces the probability of voting by − 5.6
percentage points – or six times more than the average. A full centimeter
of local rainfall on Election Day is a relatively uncommon event and,
hence, a quite strong treatment, but a response of more than five percentage
points in predicted turnout is nevertheless remarkable. For more
seasoned voters, the rainfall effect gradually wanes. We find similar
reverse patterns for sunshine. Future research should examine the effects
for other marginal voter groups, for instance based on an estimated
propensity to vote approach (Enos et al., 2013).
The heterogeneity of weather effects implies that bad weather on
Election Day increases the turnout gap between high and low propensity
voters, between core and marginal voters, thus increasing inequalities in
electoral participation and harming democratic representation. Based
on rainfall records from the last 30 years in Denmark, we would expect
1.1 mm less rainfall on any given day in April, the driest month,
compared to November, where Danish local elections take place. Simply
moving the elections to April would improve electoral equality by
increasing expected turnout among young voters, and plausibly other
marginal voter groups too, by more than a half percentage point – an
impact close to the typical Get-Out-The-Vote campaign (Hansen, 2020).
In sum, we find robust and consistent effects of local weather on
turnout in small, flat, and geographically homogenous Denmark, where
the electoral system is characterized by extremely effective automatic
voter registration, high-salience elections, and strong civic duty and
turn-out-to-vote norms (Hansen, 2020). In contexts with more heterogeneous
or extreme weather, larger barriers to turnout, weaker civic
duty norms, weaker electoral salience, lower historical turnout, and
larger marginal voter groups, we might expect decisions to turn out for
election to be even more sensitive to Election Day weather conditions.
Furthermore, with more extreme weather expected in the future, the
weather effects presented in this paper’s meta-analysis and new
stand-alone study provide conservative estimates of how the weather
will influence electoral turnout in the future. Once in a while, we will see
elections that rains out with major implications for democratic
representation.
Replication data
Data for the meta-analysis, weather data, and R code are available
through Harvard Dataverse at hhttps://doi.org/10.7910/D
VN/U7HY94. Individual-level turnout records are stored on servers at
Statistics Denmark and cannot be made publicly available for legal, security,
and privacy reasons. An online appendix with supplementary
analysis is published with the article.
Acknowledgement
Previous versions of this paper were presented at the 2022 Midwest
Political Science Association (MPSA) Annual Conference in Chicago, the
2021 American Political Science Association (APSA) annual meeting in
Seattle, and the 2021 Danish Political Science Association (DPSA)
annual meeting in Vejle. We thank all panel participants as well as Brad
Gomez, Jens Olav Dahlgaard, Christoffer Dausgaard, Frederik K. Larsen,
and the four anonymous reviewers.
Appendix A. Supplementary data
Supplementary data to this article can be found online at https://doi.
org/10.1016/j.electstud.2022.102573.
S. Damsbo-Svendsen and K.M. Hansen
Electoral Studies 81 (2023) 102573
11
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