Journal of Experimental Political Science, Page 1 of 16
doi:10.1017/XPS.2018.13
Weather, Risk, and Voting: An Experimental Analysis of the
Eﬀect of Weather on Vote Choice
Anna Bassi∗
Abstract
A number of theoretical and empirical studies analyze the eﬀect of inclement weather on
voter turnout and in turn on parties’ vote share. However, empirical ﬁndings suggest that
the eﬀect of weather on parties’ vote share is greater than can be explained by its inﬂuence
on voter turnout alone. This article provides experimental evidence of the eﬀect of weather
on vote choice between more- versus less-risky candidates. Findings show that bad weather
signiﬁcantly and sizeably depresses risk tolerance making voters less likely to vote for risky
candidates. This article also provides evidence of a possible mechanism: unpleasant weather
conditions depress agents’ mood, making agents less inclined to vote for candidates who are
perceived as more risky.
Keywords: weather, risk attitudes, voting choice, mood
Scholars have long analyzed the eﬀect of inclement weather on presidential election
turnout and on parties’ vote share. Ludlum (1984) documents several instances
in which inclement weather has been claimed to be decisive in swinging election
results. A notable case is the 1960 presidential elections, when a cold front in swing
states appeared to be the critical factor enabling Kennedy to win by a razor-sharp
margin over Nixon.
An earlier version of this paper has previously circulated as “The indian rain dance of the incumbent.
The eﬀect of weather beyond turnout.” I am grateful to Daniel Butler, Riccardo Colacito, Paolo
Fulghieri, Andrew Healy, Luke Keele, Neil Malhotra, Dan Myers, Michael MacKuen, Torrey
Shineman, Dave Siegel, Jim Stimson, Alex Theodoridis, Agnieszka Tymula, Jon Woon, the editors
Eric Dickson and Rick Wilson, and three anonymous reviewers for their useful comments. I would like
to thank the seminar’s participants at the 2012 Midwest Political Science Association, 2013 Southern
Political Science Association, 2013 American Political Science Association, 2013 Economic Science
Association, the 6th Annual NYU-CESS Experimental Political Science Conference, and the 2014
Behavioral Models of Politics Conference for providing valuable feedback. All errors remain my own.
The data, code, and any additional materials required to replicate all analyses in this article are available
at the Journal of Experimental Political Science Dataverse within the Harvard Dataverse Network, at
doi:10.7910/DVN/CVXOOS (Bassi, 2018). The experiment was approved by the University of North
Carolina at Chapel Hill’s Institutional Review Board (Study #11-0389).
∗Department of Political Science, The University of North Carolina at Chapel Hill, NC, USA, e-mail:
anna.bassi@unc.edu
C The Experimental Research Section of the American Political Science Association 2018
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2 Weather, Risk, and Voting
Most of the literature focuses on the eﬀect of weather on voter turnout and on
the impact of turnout on the Democratic versus Republican share of the vote (De
Nardo, 1980; Gomez et al., 2007; Knack, 1994). However, as found by Horiuchi
and Kang (2018), the eﬀect of weather on parties’ vote share is greater than can
be explained by diﬀerential turnout.1
They suggest that the eﬀect of weather on
elections can be decomposed into two diﬀerent components: the eﬀect on turnout
and the eﬀect on vote choice. While the former has received attention in the
literature, the latter has not yet been investigated. This article constitutes the ﬁrst
attempt to analyze the mechanism through which election day weather aﬀects not
the decision to vote, but the candidate the voter selects.
A growing body of research spanning several disciplines has documented that
environmental factors can aﬀect cognitive processes and individual behavior. The
eﬀect of weather on decision-making activity has been investigated by Hirshleifer
and Shumway (2003), who document that daily nominal returns on a nation’s stock
index are negatively associated with the level of (above average) daily cloudiness
in the city where the national stock market is located; Kamstra et al. (2003), who
document a seasonal aﬀective disorder (SAD) eﬀect in the seasonal cycle of stock
returns; and Kramer and Weber (2012), who ﬁnd that people who suﬀer from
SAD displayed signiﬁcantly stronger preferences for safe choices during the winter
than during the summer. Bassi et al. (2013) propose and test a possible mechanism
through which weather aﬀects ﬁnancial decisions and economic behavior: sunshine
promotes risk-taking behavior, while overcast conditions enhance risk-aversion.
Risk attitudes and uncertainty play an important role in determining voting
behavior (Morgenstern and Zechmeister, 2001; Nadeau et al., 1999; Shepsle, 1972).
Voters need to forecast both the set of feasible actions that candidates might
take once elected, and the probability that those actions will be carried out.
Candidates can be perceived to carry diﬀerent degrees of risk. For example, voters
might believe that an incumbent is less risky than a challenger because they have
more information about the incumbent and feel they can better anticipate his/her
future actions (Bernhardt and Ingerman, 1985; Shepsle, 1972). Candidates with
experience in executive oﬃces might also be considered less risky because they are
more likely to be competent. Candidates with ambiguous policy positions, instead,
may be perceived to be risky, because voters are uncertain about their true policy
stances (Palfrey and Poole, 1987 and Tomz and Van Houweling, 2009). Finally,
candidates’ cohesion with the party establishment may signal a lower degree of
riskiness if voters believe the party will support the candidate once in oﬃce. All
of these factors contribute to the “perceived riskiness” of a candidate. Hence,
environmental factors might aﬀect vote choice by enhancing or haltering voters’
1Horiuchi and Kang (2018) use the data in Gomez et al. (2007) and run seemingly unrelated regressions
constraining the sum of Republican candidate votes, Democratic candidate votes, and the number of
abstainers to be equal the total number of eligible voters. They ﬁnd that the Republican advantage
caused by inclement weather is ascribed not only to a possible diﬀerential turnout but also to a vote
shift.
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Anna Bassi 3
tolerance to risk. For example, we might think that inclement weather negatively
aﬀects the election prospects of risky candidates by making voters less tolerance to
risk.
Voter behavior in risky environments is also aﬀected by the state of the world:
Kahneman and Tversky’s prospect theory (1979) postulates that when agents
believe themselves to be acting in a relatively good state (positive prospect), they
are less likely to take risks that could result in the dissipation of gains; in contrast,
when agents believe the state to be a relatively bad one (negative prospect), they
are more likely to take risks that could result in the avoidance of losses. Quattrone
and Tversky (1988) and MacKuen et al. (1992) ﬁnd evidence of this eﬀect: when
the economy is up or doing better than expected, voters lean toward the incumbent
rather than toward riskier challengers.
To study the eﬀect of weather conditions and prospects on vote choice, I conduct
a series of experiments in which subjects are asked to vote in a set of elections
between two candidates, one characterized as free of risk and one as risky. The
results identify a sizeable and statistically signiﬁcant eﬀect of weather on individual
vote choice. Inclement weather increases the likelihood that voters will choose the
risk-free candidate by an average of 15% in both prospects.
This article contributes to the literature ﬁrst by establishing the eﬀect of weather
on risk attitudes not only in positive, but also in negative prospects, in which agents
are expected to be less averse to risk, or even risk seeking. Second, it sheds light
on the mechanism that links weather and voting choice by providing evidence that
weather aﬀects voters’ mood and that, in turn, mood aﬀects voters’ willingness to
accept risks and to vote for candidates who are perceived as more risky. Last but not
least, it contributes to the prospect theory literature by ﬁnding that the incidence
of reﬂection behavior around the reference point is reduced when reference points
only aﬀect prospects’ perceptions but not objective prospects’ payoﬀs.
THE EXPERIMENTAL DESIGN
The experimental design of this study builds on the classic study of Quattrone
and Tversky (1988), in which two candidates with identical policy preferences but
diﬀerent degrees of riskiness are compared in a positive and a negative prospect.
Subjects are tasked to vote in their country’s election for one of the two candidates.
The design extends this baseline framework in two important ways. First, choices
are consequential and aﬀect subjects’ earnings. This increases the salience of the
experiment and gives more internal validity to the results (Holt and Laury, 2002).
Second, subjects participate in multiple elections, allowing for a more accurate
measurement of the extent to which weather aﬀects vote choice.
To help subjects interpret the task as an actual voting decision rather than
as an abstract choice, the risky candidate is labeled “challenger” while the
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4 Weather, Risk, and Voting
Table 1
Payoﬀ Tables—Risk Aversion Elicitation
Task in positive prospect treatment
Round Challenger Incumbent Reference point
1 50% of $66,000, 50% of $42,000 100% of $42,000 $42,000
2 50% of $66,000, 50% of $42,000 100% of $46,000 $42,000
3 50% of $66,000, 50% of $42,000 100% of $48,000 $42,000
4 50% of $66,000, 50% of $42,000 100% of $50,000 $42,000
5 50% of $66,000, 50% of $42,000 100% of $52,000 $42,000
6 50% of $66,000, 50% of $42,000 100% of $54,000 $42,000
7 50% of $66,000, 50% of $42,000 100% of $56,000 $42,000
8 50% of $66,000, 50% of $42,000 100% of $58,000 $42,000
9 50% of $66,000, 50% of $42,000 100% of $60,000 $42,000
10 50% of $66,000, 50% of $42,000 100% of $62,000 $42,000
Note. This table reports the payoﬀs in each of the rounds. The “Challenger” column describes the payoﬀs that the challenger are predicted
to yield and their likelihoods. The “Incumbent” column describes the payoﬀ that the incumbent is predicted to yield. The rightmost
column describe the payoﬀ yielded by candidates in comparable countries that was provided to the subjects as reference point in the
positive prospect treatment. In the negative prospect treatment, everything remain the same but for the reference point which is $66,000
for all ten rounds.
risk-free candidate is labeled “incumbent.”2
For each of the two candidates,
subjects were provided with two experts’ forecasts of what their payoﬀ would be
should a candidate win the election.3
While the payoﬀs forecasted by the experts
for the incumbent are identical, they diﬀer for the challenger, generating a positive
variance and riskiness for the latter.
I operationalize positive and negative prospect treatments by providing
information about the payoﬀ yielded in other comparable countries, thereby
creating a so-called “reference point.”
In Table 1, I describe the experimental task. Subjects are asked to choose 1 of
the 2 candidates in 10 diﬀerent election rounds. The matrix of payoﬀs is set up
in such a way that a risk-neutral subject would choose the challenger as long as
the expected utility is higher than the expected utility of choosing the incumbent
(in the ﬁrst ﬁve rounds), he/she be indiﬀerent in the sixth round, and then he/she
would switch to voting for the incumbent (in the last four rounds). The rightmost
column reports the payoﬀ generated in comparable countries used in the positive
prospect treatment.4
The simplicity of this task, in which subjects compare a
sure option (incumbent) with an option that yields two potential payoﬀs with
equal probability (challenger), reduces subjects’ cognitive eﬀort and confusion to
2A manipulation check treatment with abstract candidate labels has been run to test for possible framing
eﬀects. The results reported in Table A.4 of the Online appendix show no signiﬁcant diﬀerence between
the contextualized and the context-free treatments.
3The payoﬀ is described to subjects in terms of Standard of Living Index (SLI) that would be yielded to
all citizens by each of the two candidates.
4In the negative prospect treatment, the matrix of payoﬀ remains the same, while the reference point is
set at $66,000 rather than $42,000.
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Anna Bassi 5
a minimum, yielding higher internal validity compared to designs that use more
complex tasks (as in Bassi et al., 2013).
After all subjects completed the experimental task and before being paid, subjects
were asked to complete a questionnaire about socioeconomic characteristics,
political leaning, and weather assessment.5
In addition, beginning in August
2012, subjects were also asked to complete a math quiz and a PANAS-X aﬀect
scale form to measure their mood. The Online appendix provides details about
experimental procedures and instructions, and the speciﬁcs of the experimental
sessions, including weather data.
A within-subject design, in which every subject participated in both prospect
treatments sequentially, was used to analyze the prospect eﬀects, while a betweensubject
design was used to analyze the weather eﬀects.6
To randomize the subjects
between good and bad weather treatments, I scheduled twin pairs of experimental
sessions per week on days with the largest spread of forecasted likelihood of precipitation
or sunshine. Subjects were asked to register for both sessions but were told
that they would be ultimately selected to participate in only one of the two sessions.
Subjects were randomly allocated by the experimenter to one of the two sessions.
A total of 199 participants were recruited from December 2011 to January 2013,
with 166 subjects actually participating in the experiment. The participation rate
was very similar across weather sessions, with 85.2% and 81.7% of the registered
subjects participating in the bad and good weather treatments, respectively. The null
hypothesis of a balance test to investigate whether the characteristics of the subjects
participating in the good versus bad weather sessions are identical cannot be
rejected (p value of 0.84). In Online appendix Table A.2, I report the demographics
of the respondents in the good and bad weather treatments, and in Table A.3, I
report the details of the balance test.
THEORETICAL EXPECTATIONS AND EXPERIMENTAL RESULTS
Consistent with Quattrone and Tversky (1988), I expect subjects to choose the
candidate who carries the least risk in the positive prospect and the candidate who
carries the most risk in the negative prospect.
Hypothesis 1 (Prospect eﬀect). The vote share for the risk-free candidate is larger
than the vote share for the risky candidate in a positive prospect, but smaller in a
negative prospect.
Furthermore, consistent with the results of Bassi et al. (2013), I expect bad
weather conditions to positively aﬀect the likelihood of choosing the less-risky
5To avoid inﬂuencing subjects’ behavior in the experimental task and in the mood questionnaire, the
evaluation of whether conditions question was put at the end of the experiment.
6The order of the prospect treatments was randomized to eliminate any order eﬀect.
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6 Weather, Risk, and Voting
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
1 2 3 4 5 6 7 8 9 10
PosiƟve Prospect
NegaƟve Prospect
Risk neutral
ProbabilityofvoƟngfortheIncumbent
Rounds
Figure 1
Probability of Voting for the Risk-Free (Incumbent) Candidate, by Round. The Vertical Axis
Reports the Percentage of Votes for the Risk-Free (Incumbent) Candidate. The Horizontal
Axis Reports the Number of the Round. The Blue Dashed Line Refers to Observations in the
Positive Prospect; the Red Solid Line Refers to the Negative Prospect. The Dotted Green Line
Depicts, as a Benchmark, the Votes that a Risk-Neutral Agent would Cast. (Color online)
option in a positive prospect. Although no previous research has established the
eﬀect of weather in a negative prospect, I expect that inclement weather will aﬀect
the probability of voting for the less-risky option in the same direction in both the
positive and negative prospects.
Hypothesis 2 (Weather eﬀect). The vote share for a risk-free candidate is larger in
bad-weather days than in good-weather days in both positive and negative prospects.
Prospects can enhance the weather eﬀect: a negative prospect is expected to boost
the eﬀect of good weather (in which subjects are expected to be most tolerant of
risk), while a positive prospect is expected to intensify the eﬀect of bad weather (in
which subjects are expected to be most averse to risk). However, prospects can also
hinder the weather eﬀect: a positive (negative) prospect is expected to moderate the
eﬀect of good (bad) weather.
Prospect eﬀects
In Figure 1, I report the distribution of the votes for the risk-free candidate
(incumbent) across 10 rounds for both prospects. Although subjects appear to be
risk averse in both prospects, the likelihood of choosing the risk-free candidate
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Anna Bassi 7
is smaller in the negative than in the positive prospect (on average 5.55 and
5.96, respectively, across the 10 rounds). This diﬀerence is statistically signiﬁcant
according to a Welch test (p value of 0.018).
Although the prospect eﬀect is signiﬁcant, the results are not consistent with
hypothesis 1. The diﬀerence between these results and the ﬁndings of Quattrone and
Tversky (1988) might be explained by the use of real—rather than hypothetical—
incentives, which reduces the incidence of reﬂection behavior around the reference
point (Laury and Holt, 2008).
Weather eﬀects
Even though the random assignment of subjects to the forecasted weather
sessions avoids self-selection biases, the experimenter cannot fully control for the
allocation of actual weather conditions. Ideally the actual weather conditions would
match the forecasted conditions. However, since the experimental sessions were
planned (and the subjects recruited) 1 or 2 weeks prior to the experimental sessions,
forecasted and actual weather conditions did not always coincide. Because the focus
of this study is the eﬀect of the eﬀective weather to which the subjects were exposed
on the day of the experiment, I use actual weather data in my analysis rather than
forecasted ones.7
Four deﬁnitions of weather quality are used.
First, the amount of sunlight has proved to be critical in aﬀecting individual
behavior by Hirshleifer and Shumway (2003). Following their approach, I collected
data on the number of minutes the sky was clear (including scattered and partly
cloudy) or overcast (including mostly cloudy or rainy) between 7 a.m. and the time
of the end of the experiment (all experimental sessions have been run between 2 p.m.
and 4 p.m.). A good weather day is deﬁned as one in which the sky was clear for
more than 50% of the time. This measure is referred to as “objective weather.”
Because subjectivity plays a key role in assessing the perceived quality of weather,
I used subjects’ answers to the questionnaire item “How do you feel about the
weather today?” as a measure of perceived weather. Subjects were asked to rate
the weather on a scale from 1 (Terrible) to 7 (Awesome). I assume a good weather
day to be described with scores higher than 4 (fair) and a bad weather day to be
described with scores lower than 4. This assessment of the weather condition is
referred to as “subjective weather.”
Precipitation provides a third indicator of the quality of weather. Consistent with
Gomez et al. (2007), who measure rainfall relative to the average precipitation in
the area of study, I deﬁne a bad weather day as one in which the amount of rainfall
exceeds the average daily amount (0.12 inches in the area in which the experiment
was conducted). This measure is referred to as “relative precipitation.” Absolute
7When subjects were recruited, they were not told that the goal of the experiment was to test the eﬀect
of weather on their decisions. For this reason, the forecasted weather is not expected to have any eﬀect
on subjects’ behavior on the day of the experiment.
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8 Weather, Risk, and Voting
Table 2
Average Frequencies of Votes for Incumbent
Subjective Objective Relative Absolute
weather weather precipitation precipitation
Positive prospect
Good weather
Avg. no. of votes 5.66 5.48 5.82 5.69
Std. dev. (0.22) (0.24) (0.16) (0.20)
N 77 62 133 84
Bad weather
Avg. no. of votes 6.45 6.25 6.55 6.24
Std. dev. (0.18) (0.16) (0.25) (0.18)
N 56 104 33 82
p value [0.006] [0.009] [0.015] [0.043]
Negative prospect
Good weather
Avg. no. of votes 5.19 5.06 5.40 5.29
Std. dev. (0.22) (0.25) (0.16) (0.21)
N 77 62 133 84
Bad weather
Avg. no. of votes 6.11 5.85 6.18 5.83
Std. dev. (0.18) (0.16) (0.22) (0.17)
N 56 104 33 82
p value [0.002] [0.008] [0.004] [0.045]
Notes. This table reports the average number of votes for the risk-free candidate (incumbent), the standard deviation of the mean (in
parentheses), and the number of observations across the ten elections for each prospect and each weather conditions. The last row reports
the p values (in brackets) of the Welch test for the null hypothesis that the mean in good weather conditions is equal to the mean in bad
weather conditions.
rainfall is also considered, with presence or absence of rain deﬁning bad or good
weather, respectively. This measure is referred to as “absolute precipitation.”
In Figure 2, I report the raw data on the eﬀect of weather. In all panels, bad
weather conditions produce a leftward shift of the frequency lines, suggesting
support for hypothesis 2. The impact of weather is especially pronounced in rounds
4–7, in which the two candidates’ expected values are very similar, and in which
one would expect the majority of subjects to start switching from the risky to the
risk-free candidate.
To assess whether the diﬀerences shown in Figure 2 are statistically signiﬁcant, I
calculate the average number of votes for the risk-free candidate and the Welch
test t-statistics for the null of identical number of votes across treatments. The
diﬀerences in vote choice between good and bad weather are statistically signiﬁcant
at conventional levels for all measures of weather conditions. In Table 2, I report
the results of the test.8
Consistent with hypotheses 1 and 2, agents are shown to
8A sensitivity analysis shows that the results are not sensitive to the presence of observations from any
of the experimental sessions. Details are provided in Table A.5 of the Online appendix.
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Anna Bassi 9
PRECIPITATION(relaƟve)PRECIPITATION(absolute)
POSITIVE PROSPECT NEGATIVE PROSPECT
OBJECTIVEWEATHERSUBJECTIVEWEATHER
0
0.1
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0.3
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0.5
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0.9
1
1 2 3 4 5 6 7 8 9 10
Good weather
Bad weather
ProbabilityofvoƟngforthe
Incumbent
0
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1 2 3 4 5 6 7 8 9 10
Good weather
Bad weather
ProbabilityofvoƟngforthe
Incumbent
0
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Good weather
Bad weather
ProbabilityofvoƟngforthe
Incumbent
0
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Good weather
Bad weather
ProbabilityofvoƟngforthe
Incumbent
0
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Bad weather
ProbabilityofvoƟngforthe
Incumbent
0
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ProbabilityofvoƟngforthe
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0
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0
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Good weather
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ProbabilityofvoƟngforthe
Incumbent
Figure 2
Probability of Voting for the Risk-Free (Incumbent) Candidate, by Weather Treatment. The
Vertical Axes Report the Percentage of Votes for the Risk-Free Candidate. The Horizontal
Axes Reports the Number of the Round. In Each Panel, the Blue Single Line and the Red
Double Line Describe Observations in the Good and Bad Weather Treatments, Respectively.
The Left and Right Panels Refer to Observations in the Positive and Negative Prospect
Treatments, Respectively. Observations are Grouped by Weather Measure. Starting from the
Top, the First Plots Refer to Subjective Weather Assessments; the Second to Objective
Measures; and the Third and Fourth to Relative and Absolute Precipitation Measures,
Respectively. (Color online)
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10 Weather, Risk, and Voting
be more likely to vote for the risk-free candidate in positive prospects and with
inclement weather conditions.
To analyze more accurately the eﬀect of weather conditions on vote choice, I categorize
the objective weather measure and the precipitation measure into quintiles,
and the subjective weather into the seven questionnaire levels (see Table 3).
Voting behavior proves to be signiﬁcantly diﬀerent across fairly extreme weather
conditions. For example, vote choice in sessions when the sky was clear for less
than 40% of the time is signiﬁcantly diﬀerent than when the sky was clear for
more than 60% of the time. However, we cannot reject the null hypothesis that vote
choice is identical between relatively sunny (or cloudy) sessions. Similarly, behavior
in sessions with no rainfall is signiﬁcantly diﬀerent than behavior in sessions with
more than average rainfall (more than 0.30 inches), but a small amount of rain (less
than 0.20 inches) does not aﬀect vote choice relative to no rain at all. The eﬀect
of subjective weather is statistically signiﬁcant only when extremely poor weather
(rated 1 or 2) is compared to better weather conditions.
Causal Mechanisms
In this section, I investigate and analyze two diﬀerent possible underlying causal
paths of the relation between weather and voting choice. One possible explanation
is that weather aﬀects cognitive behavior and thus decision making. Previous
literature claims that the environment aﬀects the following: (i) the interpretation of
available data; (ii) the processing of information, or (iii) the tendency of agents to
rely on simple decision heuristics, such as imitating prior decisions or maintaining
the status quo (Isen, 2000).
To test this mechanism, I ﬁrst investigate if weather aﬀects cognitive eﬀort, using
the responses to a mathematical quiz that the subjects were asked to complete
after the voting task.9
The responses to the mathematical quiz are not statistically
diﬀerent across any measure of weather (see Table 4), indicating that weather does
not aﬀect the type of cognitive eﬀort required of the subjects need during the
experiment.
A second explanation for the link between weather and voting choice is that
mood is an intermediate variable through which weather aﬀects decision making.
This possible causal path is supported by studies suggesting that pleasant weather
conditions enhance agents’ mood (Sanders and Brizzolara, 1982) and aﬀect the
subjective assessment of the likelihood of future events (Wright and Bower, 1992),
making agents more inclined to accept risks. To test this possibility, I investigate
whether the weather aﬀects mood and whether mood aﬀects voting choice.
To measure positive and negative aﬀect mood states, I use the responses that
subjects provided in the PANAS-X aﬀect scale form. Positive aﬀect is deﬁned as
9The Online appendix reports the questions included in the quiz.
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AnnaBassi11
Table 3
Interval Levels Categorization of Weather Conditions
Objective weather (Percentage of clear sky)
Avg. no.
Interval 0%–20% 21%–40% 41%–60% 61%–80% 81%–100% of votes N
0%–20% 11.99 78
(0.35)
21%–40% [0.482] 12.47 17
(0.60)
41%–60% [0.679] [0.886] 12.33 9
(0.78)
61%–80% [0.075] [0.045] [0.120] 10.85 46
(0.53)
80%–100% [0.026] [0.014] [0.032] [0.271] 9.69 16
(0.92)
Pooled 11.52 166
(0.26)
Precipitation (in inches of rain)
Avg. no.
Interval 0 (0–0.10) [0.10–0.20) [0.20–0.30) [0.30–) of votes N
0 10.98 84
(0.39)
(0–0.10) [0.318] 11.61 41
(0.51)
[0.10–0.20) [0.431] [0.890] 11.75 8
(0.92)
[0.20–0.30) [0.069] [0.352] [0.572] 12.37 19
(0.65)
[0.30–) [0.001] [0.023] [0.163] [0.290] 13.21 14
(0.48)
11.52 166
(0.26)
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12Weather,Risk,andVoting
Table 3
Continued
Subjective weather
Avg. no.
Interval 1 2 3 4 5 6 7 of votes N
1 14.00 6
(0.26)
2 [0.348] 13.44 18
(0.54)
3 [0.000] [0.020] 11.78 32
(0.45)
4 [0.000] [0.007] [0.497] 11.30 33
(0.55)
5 [0.000] [0.007] [0.385] [0.821] 11.11 26
(0.63)
6 [0.000] [0.001] [0.133] [0.423] [0.591] 10.65 37
(0.61)
7 [0.023] [0.067] [0.507] [0.776] [0.890] [0.834] 10.93 14
(1.22)
Pooled 11.52 166
(0.26)
Notes. This table reports the analysis of the eﬀect of weather on the likelihood of voting for the risk-free candidate (incumbent) for diﬀerent interval categorizations of the weather quality. The top panel refer to
objective weather; the middle to precipitation; and the bottom to subjective weather. The rightmost two columns report the average number of votes for the risk-free candidate, the standard deviation of the mean (in
parentheses), and the number of observations across the two prospects. The matrices in the middle columns report the p values (in brackets) for the null hypothesis that the average number of votes for the risk-free
candidate in two diﬀerent weather condition intervals are equal.
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Anna Bassi 13
Table 4
Cognitive Mechanism
Subjective Objective Absolute Relative
weather weather precipitation precipitation
Good weather
Avg. no. of votes 17.97 17.86 17.84 17.66
Std. dev. (0.37) (0.54) (0.40) (0.31)
N 65 29 51 89
Bad weather
Avg. no. of votes 17.11 17.67 17.62 17.89
Std. dev. (0.59) (0.32) (0.38) (0.60)
N 29 87 65 27
p value [0.211] [0.754] [0.676] [0.735]
Notes. This table reports the average number of correct math answers, the standard deviation of the mean (in parentheses), and the number
of observations for each weather condition. The last row reports the p values (in brackets) of the Welch test for the null hypothesis that the
means in good and bad weather conditions are equal.
feelings such as happiness, joy, excitement, enthusiasm, and contentment. Negative
aﬀect measures feelings such as fear, anger, anxiety, and depression.10
In top panel of Table 5, I report the aﬀect score diﬀerences between bad-weather
and good-weather (the left and right column refers to the positive and negative
aﬀect scores, respectively).11
A positive number indicates that the score is higher
under bad-weather conditions than under good-weather conditions. The results
show that when weather conditions worsen, positive aﬀect scores signiﬁcatively
decrease (overcast sky and absolute precipitation levels aﬀect positive mood scores
at 1% and 5% signiﬁcance levels, respectively), while negative mood scores are not
signiﬁcantly aﬀected by weather conditions. In the bottom panel of Table 5, I report
the results of the regression of the the votes for the risk-free candidate on the aﬀect
states. The results suggest that the likelihood of voting for the risk-free candidate is
signiﬁcantly inﬂuenced by both positive and negative aﬀect states.
I also test the mediational hypothesis using a Sobel test (1982) for partial
mediation.12
The hypothesis is that the relationship between weather and vote
choice is an indirect eﬀect due to the inﬂuence of mood (the mediator). The test
shows that the null hypothesis of no mediation of positive aﬀect in the documented
eﬀect of weather on vote choice is rejected at conventional levels of statistical
signiﬁcance when weather quality is accounted for by the objective measure (Pvalue
0.009).
10Subjects were required to assess on a scale from 1 to 5 the extent to which they had felt each feeling and
emotion. The individual scores for each feeling were added within each mood category and re-scaled on
a 0–1 scale.
11The subjective weather measure was excluded because of a possible reverse causation problem: weather
might aﬀect subjects’ mood, but mood might aﬀect the subjects’ assessment of weather conditions.
12The Sobel test is a t-test that provides a method to determine whether the reduction in the eﬀect of the
independent variable, after including the mediator in the model, is a signiﬁcant reduction and therefore
whether the mediation eﬀect is statistically signiﬁcant.
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14 Weather, Risk, and Voting
Table 5
Mediation Analysis of the Mood Mechanism
Eﬀect of inclement weather on mood Positive aﬀect Negative aﬀect
Objective weather − 0.093 − 0.011
[0.001] [0.580]
Relative precipitation − 0.024 − 0.003
[0.554] [0.879]
Absolute precipitation − 0.062 − 0.006
[0.033] [0.712]
Eﬀect of mood on vote choice for the risk-free candidate
Positive aﬀect Negative aﬀect
Intercept 11.371∗∗∗ 11.371∗∗∗
(0.360) (0.378)
Mood − 0.519∗∗ − 0.071
(0.241) (0.446)
Sobel (Objective weather) [0.009] [0.492]
Sobel (Relative precipitation) [0.130] [0.954]
Sobel (Absolute precipitation) [0.539] [0.985]
Note. The top panel shows the eﬀect of weather on mood. Each entry reports the spread of the scores between the bad and the good
weather conditions and the p values (in brackets) for the null hypothesis that the scores under bad and good weather conditions are equal.
The bottom panel shows the eﬀect of mood on voting choice. It reports the estimated coeﬃcients of the regressions of the number of votes
for the risk-free candidate on an intercept and on the standardized scores of the PANAS-X categories. The numbers in parentheses are
the standard deviations of the estimated coeﬃcients. The last row shows the results of the Sobel test mediation analysis: the p values (in
brackets) for the null hypothesis of no mediation of mood in the eﬀect of weather on the vote choices.
The mood-risk channel investigated in this article does not exclude other
mechanisms through which weather may aﬀect individuals’ behavior. A more
thorough analysis, in which mood states are induced and controlled rather than
self-reported, is needed to fully understand and distinguish among diﬀerent (and
potentially interacting) causal mechanisms. I regard this as an important question
for future research.
CONCLUSION
This article provides experimental evidence of the eﬀect of weather–measured
in terms of precipitation, sunlight exposure, and subjective weather quality—on
the likelihood that voters will vote for candidates who are perceived to be risky.
My ﬁndings show that bad weather decreases risk tolerance, thus, increasing the
likelihood that voters will vote in favor of a less-risky candidate, while good weather
conditions promote risk-taking behavior. In close elections, bad weather may result
in up to a twice as great a probability of choosing a less-risky candidate over a
more-risky one.
Horiuchi and Kang (2018) provide an estimate of the eﬀect of weather on parties’
vote share in presidential elections, after controlling for the eﬀect of weather on
turnout. My results show that weather on election day can aﬀect parties’ vote
share by swinging the vote choice of the so-called marginal voters, that is, voters
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Anna Bassi 15
who are almost indiﬀerent between the candidates. Weather does not seem to
aﬀect the vote choice when candidates yield substantially diﬀerent utilities; hence,
one should not expect weather to swing the votes of partisans or of voters who
perceive the candidates to be substantially diﬀerent in terms of political attitudes,
policy programs, or personal characteristics. Rather, weather is more likely to aﬀect
the vote choice of voters in lower-stakes elections, in which voters might be less
informed about the policy preferences of the candidates, and the risk of candidates
not carrying through on campaign promises may be the key factor in swing voters’
decisions.
The results of this analysis suggest that not only policy ambiguity but also
performance uncertainty may aﬀect vote choice, especially when candidates’ policy
preferences are similar. Although the ﬁndings of this study could be reinterpreted
to allow candidates to carry diﬀerent types of risks, which ultimately would aﬀect
voters’ utilities, further studies are needed to test the magnitude of the weather
eﬀect in the presence of such risks.
This study opens up new questions to pursue that will advance our collective
understanding of risks and their eﬀect on vote choice. What kind of personal
characteristics and actions cause a candidate to be perceived as risky? To what
type of risks are voters more sensitive? The design used in this article could be
fruitfully extended to analyze the eﬀect of diﬀerent types of candidate riskiness on
vote choice, such as uncertainty about future policy programs or unknown capacity
for the fulﬁllment of political promises.
SUPPLEMENTARY MATERIALS
To view supplementary material for this article, please visit https://doi.org/10.
1017/XPS.2018.13
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