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Losing Hurts: The Happiness Impact of Partisan Electoral Loss

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Partisan identity shapes social, mental, economic, and physical life. Using a novel dataset, we study the consequences of partisan identity by examining the immediate impact of electoral loss and victory on happiness and sadness. Employing a quasi-experimental regression discontinuity model we present two primary findings. First, elections strongly affect the immediate happiness/sadness of partisan losers, but minimally impact partisan winners. This effect is consistent with psychological research on the good-bad hedonic asymmetry, but appears to dissipate within a week after the election. Second, the immediate happiness consequences to partisan losers are relatively strong. To illustrate, we show that partisans are affected two times more by their party losing the 2012 U.S. Presidential Election than both respondents with children were to the Newtown shootings and respondents living in Boston were to the Boston Marathon bombings. We discuss implications regarding the centrality of partisan identity to the self and its well-being. Word count: 3,196
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Losing Hurts:
The Happiness Impact of Partisan Electoral Loss
Lamar Pierce
1
, Todd Rogers
2
, and Jason A. Snyder
3
Forthcoming in Journal of Experimental Political Science
Abstract (Words: 149):
Partisan identity shapes social, mental, economic, and physical life. Using a novel dataset, we
study the consequences of partisan identity by examining the immediate impact of electoral
loss and victory on happiness and sadness. Employing a quasi-experimental regression
discontinuity model we present two primary findings. First, elections strongly affect the
immediate happiness/sadness of partisan losers, but minimally impact partisan winners. This
effect is consistent with psychological research on the good-bad hedonic asymmetry, but
appears to dissipate within a week after the election. Second, the immediate happiness
consequences to partisan losers are relatively strong. To illustrate, we show that partisans are
affected two times more by their party losing the 2012 U.S. Presidential Election than both
respondents with children were to the Newtown shootings and respondents living in Boston
were to the Boston Marathon bombings. We discuss implications regarding the centrality of
partisan identity to the self and its well-being.
Word count: 3,196
Keywords: Partisanship, Political Psychology, Happiness, Elections, Identity, Well-Being,
Obama
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The authors thank John Dick, Ross McGowan, and Zack Sharek at CivicScience, Inc. for providing the data, as
well as Craig Fox, Francesca Gino, Eitan Hersh, Karim Kassam, Marc Meredith, Brendan Nyhan, Mike
Norton, Erik Snowberg, and Kathleen Vohs for thoughtful comments and suggestions. Carly Robinson
provided excellent research assistance.
1
Washington University in St. Louis, Olin Business School, One Brookings Drive Box 1133, St. Louis MO
63130.
2
Harvard Kennedy School, Mailbox 124, 79 JFK Street, Cambridge MA 02138.
3
UCLA, Anderson School of Management, 110 Westwood Plaza, Cornell Hall, Suite D506, Los Angeles CA
90095.
Authors contributed equally and listed alphabetically. Direct correspondence to pierce@wustl.edu.
How important is partisan identity to happiness? It might be of considerable
importance to the two-thirds of Americans who identify with a political party, given its
powerful influence on other dimensions of people’s lives. Partisan identity is stable across
people’s lifetimes (Campbell et al, 1960; Green, Palmquist and Schickler, 2002), causally
shaping political preferences and the factual qualities people associate with policies (Cohen,
2003). People more frequently live near (Glaeser and Ward, 2006; Gimpel and Schuknecht,
2004) and interact with (Grentzkow and Shapiro, 2001) those who share their partisan
identity than with those who do not. Furthermore, partisan identity tends to define media
consumption (Prior, 2007) and other economic behavior (Gerber and Huber, 2009), and can
bias social perceptions and favoritism (Caruso, Mead and Belcetis, 2009; Rand et al, 2009). In
short, social, mental, economic, and physical life is shaped by partisan identity.
Given this importance, political outcomes such as the 2012 U.S Presidential Election
could profoundly impact the happiness of both partisan winners (Democrats) and partisan
losers (Republicans). This research uses a novel dataset that tracks fluctuations in happiness
and sadness to address two questions about the importance of partisan identity to well-being.
First, are the shocks to happiness from winning and losing equivalent? Diverse research
suggests they might not be. Bad events cause stronger reactions than comparable good ones
(Baumeister et al, 2001; Rozin and Royzman, 2001), similar to predictions from prospect
theory’s value function about the gain-loss asymmetry (Kahneman and Tversky, 1979;
McDermott, 2004).
Second, how strong is the shock of partisan loss to happiness? We compare the well-
being consequences of partisan loss to that of two national tragedies that dominated the
national news media for weeks. On December 14, 2012, twenty children and six adults were
fatally shot at Sandy Hook Elementary School in Newtown, Connecticut (“Newtown
1
shootings”). On April 15, 2013, three people were killed and 283 injured after terrorists
attacked the Boston Marathon (“marathon bombings”). While such tragedies are
qualitatively different than elections, comparing their well-being consequences to that of
partisan loss illustrates simply the relative importance of partisan identity to well-being.
Tragedies have both political repercussions (Gillis, 1996) and elicit emotional, financial, and
civic responses from people not directly affected by the trauma (Preston and De Waal, 2002;
Singer et al, 2004). Consequently, one might sensibly expect the hedonic (happiness-based)
impact of partisan loss to pale in comparison.
Using daily responses from CivicScience, Inc., an online polling and data intelligence
company, we employ a quasi-experimental regression discontinuity (RD) design to estimate
the happiness shock of specific events. The RD design overcomes many of the sampling bias
problems in survey-based studies of happiness by focusing on nearly identical respondents
immediately before and after an independent shock (Shadish, Cook and Campbell, 2002;
Imbens and Lemieux, 2008).
We find that the pain of losing an election is much larger than the joy of winning
one, but that this happiness loss is short-lived. Election outcomes strongly affect the short-
term happiness/sadness of partisan losers, with minimal impact on partisan winners. This
result is consistent with studies finding that “bad emotions, bad parents, and bad feedback
have more impact than good ones…bad information is processed more thoroughly than
good…[and] the self is more motivated to avoid bad self-definitions than to pursue good
ones” (Baumeister et al, 2001). Despite the strength of the loss, happiness appears to recover
within a week, consistent with research on people’s tendency to adapt to bad events more
quickly than expected (Gilbert et al, 2004). This temporariness suggests partisan loss impairs
emotional well-being rather than broader life satisfaction (Kahneman and Deaton, 2010).
2
The short-term strength of partisan loss is contrasted with responses to mass
national tragedies; partisans are affected twice as much by their candidate losing the U.S.
Presidential Election than both respondents with children were to the Newtown shootings
and respondents in Boston were to the marathon bombings. The fact that the pain
experienced by partisan losers is stronger than that of people for whom the tragedies were
self-relevant benchmarks the centrality of partisan identity to the self and well-being.
Method
Data. CivicScience polls over 300,000 unique individuals daily across the United
States on over 500 third-party websites. Unpaid volunteers answer three questions in
embedded polls. Tracking technology allows the company to identify returning respondents
across all partner websites, thereby collecting a panel of detailed demographic and attitudinal
data for many respondents. One question that is randomly and continuously distributed
across all partner websites each day asks “How happy are you today -- very happy, happy, so so,
unhappy, or very unhappy?” This question is similar to one used in the Euro-Barometer Survey
Series and the United States General Social Survey—widely used to study happiness in
economics (Easterlin, 2003; Argyle, 2003; Di Tella, MacCulloch, and Oswald, 2003; Alesina,
Di Tella and MacColloch, 2004; Easterlin, 2006). Consistent with the prior literature we
create an indicator variable “happy” equal to 1 if respondents reported being happy or very
happy.
4
CivicScience also collects extensive socio-demographic information (e.g., gender,
income, race, age, and partisan identity) in a pre-determined sequence from return
respondents over multiple visits to partner websites. We had access to data on all
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4
This dichotomized variable is easier to interpret and more meaningful than a 1-5 scale assuming each unit
change is equal. Tables S7A and S7B in the supplementary materials present similar results with the scaled
dependent variable.
3
respondents who had answered the happiness and sadness questions, but not all of these
respondents answered all socio-demographic, party affiliation, and parental status questions.
For example, for the week before and after the election approximately 67% of respondents
with happiness responses and full socio-demographic data had answered the partisan identity
question; eighty five percent of such respondents from the week before and after Newtown
had answered the parental status question. Missing responses for each of question yields a
sparse data matrix, which makes imputing missing data very unreliable. We therefore restrict
the data for each analysis to the respondents for whom there are no missing observations.
CivicScience asks “Politically, do you consider yourself more of a: Republican, Democrat, or
Independent?” Like all surveys, the sample of these individuals is conditioned on the decision
to participate in repeated CivicScience polls. CivicScience respondents were somewhat more
Republican than the general population. We are unable to observe data on which
respondents chose not to answer specific questions. Figure 1 previews our core results.
During the two weeks surrounding Election Day, an average of 210 Republicans and 111
Democrats answered an online happiness question each day. Notice the little change in the
likelihood that Democrats report being happy, while immediately following the election
Republicans’ self-reported happiness drops from approximately 60% to 30%. These data are
collected with enough frequency that daily shocks can be clearly identified, a feature unique
to most research on happiness. We note, however, that our models’ key identifying
assumption is that the sample is similar before and after the election. Additionally, given the
self-selection and uneven geographic distribution of the sample, one must be careful in
extrapolating specific effect magnitudes to the general population. Finally, we note how days
are coded. Across all studies we code days as being 24-hour periods immediately preceding
and following focal events. For instance, the 2012 presidential election was called by the
4
Associated Press at approximately 11PM EST on Election Day, so the previous day began at
11PM EST the day before Election Day.
Model. We use quasi-experimental regression discontinuity models to test how the
happiness levels of Democrats and Republicans discretely change immediately following the
presidential election. RD models assign observations to treatment and control groups based
on a discrete threshold in a continuous assignment variable, which in our case is time (days).
The discrete threshold is the focal event (e.g., Election Day or day of the tragedy). Any
response after the focal event threshold is considered “treated,” while prior days are in the
“control.” RD models are most commonly used in political science, economics, and
psychology (Dal Bo, Dal Bo and Snyder, 2009; Snyder, 2010; Gerber, Kessler, and Meredith,
2011; Pierce, Dahl, and Nielsen, 2013; Hersh, 2014), with many examples applying RD
models to time series data, as we do (Busse, Silvo-Risso, and Zettelmeyer, 2006; Pierce and
Snyder, 2012).
RD models assume that observations just above and below the threshold are
identical on all dimensions except the focal treatment. Table S1-S3 provides detailed
evidence that respondents one week before and after the three events in our data are
reasonably identical on observable dimensions. Simple t-tests of differences in pre/post
means reveal few systematic demographic differences, nor do regression discontinuity
models that replace Happiness with each demographic as the dependent variable in equation
(1) below. Our base specification is as follows at the individual-level respondent:
(1) 𝐻𝑎𝑝𝑝𝑖𝑛𝑒𝑠𝑠
!
=! ! +!𝛽
!
𝑃𝑜𝑠𝑡𝐸𝑣𝑒𝑛𝑡
!
+ ! 𝛽
!
𝐿𝑖𝑛𝑒𝑎𝑟!𝑇𝑖𝑚𝑒!𝑉𝑎𝑟𝑖𝑎𝑏𝑙𝑒
!
+𝛽
!
𝑃𝑜𝑠𝑡𝐸𝑣𝑒𝑛𝑡
!
! 𝐿𝑖𝑛𝑒𝑎𝑟!𝑇𝑖𝑚𝑒!𝑉𝑎𝑟𝑖𝑎𝑏𝑙𝑒
!
+ ! 𝜀
!
Linear Time Variable runs from -7 to 6, where 0 is the day immediately following the
5
focal event. PostEvent is an indicator equal to 1 if the event has already occurred. Figure 2
illustrates this specification for Republican respondents’ happiness in relation to the election.
𝛽
!
estimates the discrete jump between the two regression estimates. 𝛽
!
is the slope prior to
the election and 𝛽
!
is the slope after the election. This specification therefore estimates the
size of the break while controlling for the different time trends before and after the event.
Other specifications include socio-demographic characteristics and higher order time
polynomials for robustness. All results are clustered at the MSA level.
To test the persistence of the happiness effect, a second model relaxes the RD
assumption to examine weekly happiness rates for Republicans and Democrats, conditioning
on socio-demographic information and location. Although this model provides some
evidence of effect persistence, we cannot observe the counterfactual time trend in weeks
distant from Election Day. Any inference about effect length must assume that happiness
would return to pre-election levels absent the election’s effect.
(2) 𝐻𝑎𝑝𝑝𝑖𝑛𝑒𝑠𝑠
!
=! ! +!𝜷
𝟏
𝑊𝑒𝑒𝑘!𝐼𝑛𝑑𝑖𝑐𝑎𝑡𝑜𝑟𝑠
!
+ ! 𝛽
!
𝑆𝑜𝑐𝑖𝑜𝐷𝑒𝑚𝑜𝑔𝑟𝑎𝑝𝑖𝑐!𝐶𝑜𝑛𝑡𝑟𝑜𝑙𝑠 + ! 𝜀
!
Study 1: 2012 U.S. Presidential Election
Tables 1a and 1b report the results for happiness around the election depicted in
Figures 1 & 2, splitting the sample by partisan winners (Democrats) and partisan losers
(Republicans). Across specifications there is little robust evidence that Democrats’ responses
changed immediately after the election. The sign across specifications is positive, but the
statistical significance is inconsistent across specifications. In contrast, partisan losers
experienced significantly larger hedonic shocks than partisan winners. Table 1B shows a
strong negative effect on the baseline level of happiness following the election. The models
are robust to including extensive demographic (race, gender, age, income), geographic
6
(metropolitan area fixed effects defined by IP address), and time control variables. This
robustness across specifications casts doubt on concerns that results are driven by
differences in the types of respondents before and after the election, as do the nearly
identical respondent characteristics before and after the election presented in Table S1. The
negative happiness impact to partisan losers, for example, actually increases from -.151 to -
.246 after all controls are added. Across each pair of columns from Tables 1A and 1B, the
coefficients are statistically different from each other at the 5% confidence level.
Figures 3A and 3B depict parameter estimates and confidence intervals associated
with equation (2). The weekly differences in happiness are all relative to the baseline 8
th
week
before the election and condition on the socio-demographic characteristics described earlier.
Over the 8 weeks before and after the election happiness is relatively constant except for
Republicans in the week immediately following the election. This evidence again shows that
Republicans’ well-being drops after the event, and also suggests that it recovers quickly.
Alternative Explanations. Three possible alternative explanations stand out. First,
the asymmetric hedonic response could stem from rational responses to the election’s policy
implications for Democrats and Republicans (Gerber and Huber, 2009). This seems unlikely,
however, since Figures 1 and 2 show happiness levels converging within one week following
the election, becoming statistically indistinguishable within four weeks.
Second, the asymmetric hedonic response could reflect different outcome
expectations. Overconfidence among partisan losers is common even in blowout elections
(Granberg and Brent, 1983), partly because simply supporting a candidate causes people to
believe that candidate will win (Krizan, Miller, and Johar, 2010). In this alternative
explanation, Republicans would be more affected because they expected a victory and were
7
disappointed, while Democrats, also expecting a victory, were unsurprised. Supplementary
analysis suggests a similar asymmetric shock for only those respondents expecting their
candidate to win, casting doubt on this expectations explanation.
Finally Republicans may simply become less happy after an election, regardless of the
outcome. This alternative seems unlikely, but we cannot directly test this hypothesis with this
data.
Study 2: Newtown Shootings and Marathon Bombings
Two major national tragedies that dominated the media for weeks occurred after the
election: the Newtown shootings and the Boston Marathon bombings. Many respondents
answered the happiness/sadness questions in the weeks surrounding the two tragedies,
averaging 445/day for the Newtown shootings and 639/day for the marathon bombings.
These data are analyzed using the same strategy as with the election data, defining the post-
event treatment dummy by whether each response was before or after the precise time of
the event’s first news coverage. Of course, learning that one’s party lost an election differs in
important ways from observing a national tragedy. For example, partisans are personally
invested in and occasionally involved in elections, while very few people are personally
involved in national tragedies. That said, of the 60% of Americans who identify with a
political party, only about 0.40% were personally involved in the 2012 election by donating
over $200 to a candidate, party, or PAC (Opensecrets.org). Nonetheless, comparing the
hedonic impact of these two national tragedies to that of losing an election can be insightful
because they were the most affectively intense events impacting the mass public during this
period. This comparison serves simply to benchmark the hedonic intensity of partisan loss,
and cannot account for other psychological impacts such as anxiety or fear.
8
Both the Newtown shootings and marathon bombings caused significant negative
hedonic shocks, but they are much smaller than those suffered by partisan losers in the
election. Table 2 presents the RD estimates for respondent happiness in relation to the
Newtown shootings. Across columns (1) – (5) the results are not consistently statistically
significant. The fully-controlled model in column 2 estimates a 7.6% happiness decrease
immediately following the Newtown shootings—only one fifth the size of the decrease
experienced by partisan losers. Likewise Table 3, column 4, shows that happiness decreases
following the marathon bombings by only 4.8%. The statistical significance varies across
multiple reasonable specifications.
Election outcomes are relevant to partisans’ identities. As such, it may not mean
much to compare the hedonic impact of partisan loss to that of national tragedies to a broad
swath of respondents. We therefore assess the hedonic impact of tragedies on those for
whom the tragedies are identity-relevant: the Newtown shootings on self-reported parents
and the marathon bombings on respondents using Boston-based IP addresses. The RD
models are reported in columns (7) & (8) of Tables 2 & 3. As one would predict, these
subsamples show larger impacts than the more general sample. However, the effects are still
only half those on partisan losers from the election (ps<.01). The differences between the
coefficient on Post-Newtown in columns (7) & (8) of Table 2 is significant at the 5%
confidence level, while the difference between the Boston and Non-Boston region is not
statistically significant.
Figure 4 presents the daily happiness and sadness results for all three events
(Presidential election, Newtown shootings, and marathon bombings) for the identity-
relevant groups (Republicans, parents, and Boston residents). The visual comparison,
9
combined with the regression results, strongly suggests that Republicans’ hedonic response
to the election was larger than either response to the two tragedies.
General Discussion
People’s social, physical, economic, and mental lives are shaped by their partisan
identities – and these social identities are widely and deeply held. The current research vividly
shows that these identities also have important consequences to people’s hedonic lives.
Winning an election is fine, but losing one is painful, at least in the short run. Losing an
election appears to dominate the pain caused by national tragedies, even among those
particularly connected to them. While enhancing our understanding of the centrality of
people’s partisanship to their lived experiences, these results also speak to the growing
literature in economics, psychology, and other fields on the factors that affect well-being
(Kahneman, Diener and Schwarz, 2003).
In addition to expanding our understanding of the well-being importance of partisan
identity, this work makes several methodological contributions. First, it tackles a causal
political psychology question by employing a research design (regression discontinuity) that
is under-used in other political psychology research (Shadish, Cook, and Campbell, 2002).
Second, it leverages digital technologies that allow large-scale, yet granular, data collection
over time. One will notice in Figure 4 the rapid adaption of partisan losers to losing an
election; of parents to the Newtown shootings; and of Boston residents to the marathon
bombings. As far as we know, this is the first paper to map the contours of hedonic
adaptation to societal events at this level of granularity. This type of data source provides
new opportunities for scholars involved in the study and measurement of happiness
(Kahneman and Krueger, 2006). Finally, by using hedonic reactions to multiple unrelated
events that are each associated with distinct identities, we illustrate an approach to
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comparing the importance of different beliefs, ideologies, or events to people’s identities
with relatively high ecological validity (Settles, 2004).
One of our main findings is that the pain of losing the 2012 Presidential Election
dominated the joy of winning it. A challenge to making a general claim is the many
idiosyncrasies to this specific election. First, the impact of losing the election may be specific
to Republicans since partisans appear to have systematic differences in how they process and
respond to information (Jost, Federico, and Napier, 2009). Second, it is difficult for us to
disentangle the role of party affiliation from simple candidate preferences. Third, since
President Obama was the incumbent, partisan winners might have perceived retaining the
presidency as maintaining the status quo, thereby muting the joy of winning. In this scenario,
however, partisan losers would have viewed the status quo as not attaining the presidency
(i.e., losing), making this status quo argument inconsistent with the results. It is also
inconsistent with the robust finding that partisans expect their preferred candidates to win,
even when the polls show that winning is unlikely (Granberg and Brent, 1983). The current
findings should be replicated in future elections to resolve these questions.
Furthermore, the results appear inconsistent with research suggesting that prospect
theory’s gain-loss asymmetry arises when people forecast their hedonic reactions, but not
when people actually experience gains and losses with monetary gambles (Kermer et al,
2006). One possible explanation for this inconsistency might be that partisans expect to win
elections (Granberg and Brent, 1983; Krizan, Miller, and Johar, 2010), whereas
overconfidence may be more muted for monetary gambles.
Finally, we note that although partisan losers appear to be only temporarily affected,
such transitive emotional shocks have important personal and social implications. Card and
11
Dahl (2011), for example, find that upset losses in football games increased local domestic
violence reports for a short period following the game.
In sum, partisan identity is even more central to the self than past research suggests.
In addition to affecting thinking, preferences, and behavior, it also has sizable hedonic
consequences, especially when people experience partisan losses.
12
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14
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15
Independent variables (1) (2) (3) (4) (5)
Post-election
.039
(.049)
.033*
(.019)
.037
(.047)
.049
(.052)
.084
(.137)
Post-election * One de-
gree polynomial of days
No No Yes Yes No
Post-election * ree de-
gree polynomial of days
No No No No Yes
Socio - Demographic &
MSA Controls
No No No Yes Yes
Time restriction +/- one day +/- one week +/- one week +/- one week +/- one week
Observations 265 1,553 1,553 1,553 1,553
Note: *, **, and *** indicate signicance at the 10%, 5%, and 1% condence levels, respectively. Standard errors are clus-
tered at the Metropolitan level. Socio-demographic controls include gender, age indicators, race indicators, and income
indicators. MSA controls included indicators for the metropolitan statistical area.
Table 1A: Democrat happiness one week surrounding 2012 election
Dependent variable: Are you happy today?
Independent variables (1) (2) (3) (4) (6)
Post-election
-.310***
(.040)
-.151***
(.016)
-.243***
(.035)
-.246***
(.039)
-.316***
(.096)
Post-election * One de-
gree polynomial of days
No No Yes Yes No
Post-election * ree de-
gree polynomial of days
No No No No Yes
Socio - Demographic &
MSA Controls
No No No Yes Yes
Time restriction +/- one day +/- one week +/- one week +/- one week +/- one week
Observations 465 2,934 2,934 2,934 2,934
Table 1B: Republican happiness one week surrounding 2012 election
Dependent variable: Are you happy today?
16
Independent variables (1) (2) (3) (4) (5) (6) (7)
Post-Newtown
-.063
(.039)
-.014
(.014)
-.076***
(.029)
-.062**
(.029)
-.035
(.077)
.060
(.061)
-.100***
(.034)
Post-Newtown * One degree
polynomial of days
No No Yes Yes No Yes Yes
Post-Newtown * ree degree
polynomial of days
No No No No Yes No No
Socio - Demographic & MSA
Controls
No No No Yes Yes Yes Yes
Time restriction +/- one day +/- one week +/- one week +/- one week +/- one week +/- one week +/- one week
Parents & Non-Parents Both Both Both Both Both Non-Parents Parents
Observations 695 5,304 5,304 5,304 5,304 1,216 4,088
Note: *, **, and *** indicate signicance at the 10%, 5%, and 1% condence levels, respectively. Standard errors are clustered at the Metropolitan level. Socio-demograph-
ic controls include gender, age indicators, race indicators, and income indicators. MSA controls included indicators for the metropolitan statistical area.
Table 2: Self-reported happiness before and after Newtown shooting
Dependent variable: Are you happy today?
17
Independent variables (1) (2) (3) (4) (5) (7) (8)
Post-Boston
-.064**
(.039)
-.022***
(.008)
-.048**
(.022)
-.050**
(.023)
-.069
(.051)
-.048**
(.024)
-.204
(.144)
Post-Boston * One degree poly-
nomial of days
No No Yes Yes No Yes Yes
Post-Boston * ree degree poly-
nomial of days
No No No No Yes No No
Socio - Demographic & MSA
Controls
No No No Yes Yes Yes Yes
Time restriction +/- one day +/- one week +/- one week +/- one week +/- one week +/- one week +/- one week
Boston Region & Non-Boston
Region
Both Both Both Both Both Non-Boston Boston
Observations 1,360 8,939 8,939 8,939 8,939 8,763 176
Note: *, **, and *** indicate signicance at the 10%, 5%, and 1% condence levels, respectively. Standard errors are clustered at the Metropolitan level. Socio-demograph-
ic controls include gender, age indicators, race indicators, and income indicators. MSA controls included indicators for the metropolitan statistical area.
Table 3: Self-reported happiness before and after Boston bombing
Dependent variable: Are you happy today?
18
.3 .4 .5 .6 .7
Percentage indicating that they are happy today
-7 -6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6
Days before and after election
.2 .4 .6 .8 1
Percentage indicating that they are happy today
-7 -6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6
Days before and after election
Figure 1: Self-reported happiness one week before and after the election
First day after
2012 election
Figure 2: Self-reported happiness one week before and after the
election for Republicans with discontinuity model
First day after
2012 election
Note: Dashed lines are +/- 1.96 standard errors. e dierence between the lines at the rst day after the election cor-
responds to the b
1
in equation (1). e dierences in slopes of the estimated line are the consequence of the interaction
between the linear term and the post-election indicator.
Intervals represent
± 1.96 standard errors
Democrats
Republicans
19
-.15 -.1 -.05 0 .05 .1
Week Coefficient (Baseline is 8th Week Before Election)
-7 -6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6 7
Weeks before and after Election
-.1 -.05 0 .05 .1 .15
Week Coefficient (Baseline is 8th Week Before Election)
-7 -6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6 7
Weeks before and after Election
Figure 3A: Self-reported happiness for Democrats eight weeks before
and after the election
Figure 3B: Self-reported happiness for Republicans eight weeks before
and after the election
Regression coecents on week indicator from
equation (1) for the set of Republicans in the
dataset. Week 1 is the baseline. Regressions
included controls for gender, age indicators,
race indicators, and income indicators. MSA
controls included indicators for the metropoli-
tan statistical area. Clustered at the MSA level.
95% condence intervals show as dashed lines.
Regression coecents on week indicator
from equation (1) for the set of Demo-
crats in the dataset. Week 1 is the base-
line. Regressions included controls for
gender, age indicators, race indicators,
and income indicators. MSA controls
included indicators for the metropolitan
statistical area clustered at the MSA level.
95% condence intervals show as dashed
lines.
Drop in happiness for Re-
publicans in the rst week
post-election is disipates in
the subsequent weeks.
20
0 .2
.4 .6 .8 1
Percentage indicating that they are happy today
-7 -6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6
Days before and after event
Figure 4: Comparison of salient groups before and after event
Event Date
Intervals represent
± 1.96 standard errors
Parents before and after Newtown shooting
Republicans before and after 2012 election
Boston region before and after Boston bombing
21
Supplementary Appendix
Losing Hurts: The Happiness Impact of Partisan Loss
Addressing Alternative Expectations Explanation
To address the concern that Democrats and Republicans suffered different
happiness shocks because of different expectations about winning, another question that
CivicScience asked before the election was analyzed: Who do you think will win the US
Presidential election: Barack Obama, not sure but probably Obama, not sure but probably Romney, Mitt
Romney, not sure? 286 respondents answered this question between October 31 and
November 6. 94% of partisan losers and 87% of partisan winners expected to win the
election in the week before the election. Of these respondents, 210 were Republicans.
Uncontrolled RD models run separately on the 197 (94%) expecting a Republican victory
and the 13 (6%) expecting a Democrat victory. Partisan losers expecting to win experienced
a similar negative hedonic shock to earlier results, -0.47 (ps<.001); partisan losers anticipating
losing also experienced a negative impact (-0.88, ps<.05) that is larger but not statistically
different from the shock to other Republicans. Similar analysis for partisan winners shows
no meaningful difference in happiness gains. We must be cautious in drawing strong
conclusions from such a sample of Republicans expecting to lose because it is both very
small and may be fundamentally different than other Republicans. Still, these results cast
some doubt on the alternative explanation of unmet expectations.
!
1
Before After Dierence T-Stat Coe T-Stat
Self-Report: Happy today? 0.613 0.529 -0.084 7.010 -0.143 -6.065
Self-Report: Sad today? 0.076 0.169 0.094 -11.792 0.177 10.308
Democrat 0.223 0.236 0.013 -1.250 0.003 0.144
Independent 0.328 0.347 0.019 -1.674 0.018 0.775
Republican 0.449 0.417 -0.032 2.661 -0.021 -0.813
Female 0.425 0.419 -0.006 0.507 0.004 0.156
Age: Under 18 0.006 0.008 0.002 -0.783 0.001 0.506
Age: 18-24 0.020 0.026 0.006 -1.662 -0.006 -0.749
Age: 25-29 0.022 0.029 0.007 -1.917 0.003 0.329
Age: 30-34 0.037 0.043 0.006 -1.284 0.005 0.480
Age: 35-44 0.119 0.108 -0.011 1.423 -0.018 -1.277
Age: 45-54 0.251 0.233 -0.018 1.738 -0.033 -1.502
Age: 55-64 0.295 0.298 0.004 -0.324 0.006 0.288
Age: 65 & Older 0.251 0.256 0.004 -0.416 0.042 2.095
Caucasian 0.864 0.855 -0.009 1.051 -0.014 -0.867
Hispanic 0.029 0.029 -0.001 0.175 0.002 0.229
African American 0.044 0.046 0.002 -0.391 -0.001 -0.052
American Indian 0.012 0.017 0.004 -1.499 -0.001 -0.193
Aleut Eskimo 0.006 0.005 0.000 0.149 0.002 0.796
Asian or Pacic Islander 0.009 0.010 0.001 -0.229 -0.006 -1.349
Other 0.035 0.038 0.003 -0.651 0.017 1.677
Income: Under $25k 0.127 0.126 -0.001 0.165 -0.003 -0.205
Income: $25k - $35k 0.094 0.094 -0.001 0.078 -0.017 -1.406
Income: $35k -$50k 0.156 0.166 0.010 -1.159 -0.015 -0.797
Income: $50k - $75k 0.205 0.199 -0.006 0.574 -0.012 -0.570
Income: $75k - $100k 0.169 0.161 -0.007 0.825 0.011 0.697
Income: $100k - $125k 0.096 0.102 0.006 -0.815 0.031 2.062
Income: $125k - $150k 0.053 0.059 0.006 -0.993 0.017 1.837
Income: $150k or more 0.100 0.093 -0.007 0.962 -0.012 -0.739
Observations 3,483 3,290
Note: Standard deviations omitted because dummies means and observations describe all moments of the distribution.
Columns (3) & (4) calculate the raw dierences. Columns (5) & (6) apply a discontinuity specication from to equation
(1) using the set of controls as the dependent variables. Coe corresponds to the coecient, similar to specication (3) in
table (1). See supplemental gure S1A for the distribution of the t-statistics on the dierences and S1B for the distribution
of the t-statistics on the coecients.
Table S1A: Summary statistics one week before and after the the elec-
tion
2
Before After Dierence T-Stat Coe T-Stat
Self-Report: Happy today? 0.650 0.683 0.033 -1.381 0.037 0.794
Self-Report: Sad today? 0.057 0.055 -0.001 0.104 0.014 0.602
Democrat 1.000 1.000 0.000 0.000
Independent 0.000 0.000 0.000 0.000
Republican 0.000 0.000 0.000 0.000
Female 0.557 0.534 -0.024 0.940 0.034 0.792
Age: Under 18 0.010 0.008 -0.003 0.534 0.011 1.599
Age: 18-24 0.032 0.041 0.009 -0.949 -0.026 -1.627
Age: 25-29 0.031 0.034 0.003 -0.292 0.015 0.999
Age: 30-34 0.041 0.064 0.023 -2.050 0.008 0.292
Age: 35-44 0.129 0.134 0.005 -0.310 -0.022 -0.723
Age: 45-54 0.264 0.241 -0.023 1.036 0.016 0.427
Age: 55-64 0.318 0.302 -0.016 0.696 -0.015 -0.424
Age: 65 & Older 0.175 0.177 0.002 -0.078 0.012 0.352
Caucasian 0.767 0.767 0.000 0.014 -0.001 -0.013
Hispanic 0.049 0.036 -0.013 1.253 -0.030 -1.170
African American 0.134 0.135 0.001 -0.084 0.007 0.220
American Indian 0.006 0.008 0.001 -0.305 0.003 0.362
Aleut Eskimo 0.005 0.006 0.001 -0.336 0.000 0.056
Asian or Pacic Islander 0.009 0.013 0.004 -0.734 0.004 0.513
Other 0.030 0.035 0.005 -0.579 0.016 0.990
Income: Under $25k 0.147 0.142 -0.005 0.278 0.042 1.146
Income: $25k - $35k 0.112 0.111 -0.001 0.072 -0.039 -1.494
Income: $35k -$50k 0.167 0.144 -0.023 1.248 -0.049 -1.354
Income: $50k - $75k 0.229 0.227 -0.002 0.107 -0.022 -0.480
Income: $75k - $100k 0.148 0.165 0.017 -0.919 0.056 1.696
Income: $100k - $125k 0.084 0.086 0.003 -0.190 0.028 1.066
Income: $125k - $150k 0.035 0.046 0.012 -1.162 0.003 0.174
Income: $150k or more 0.079 0.079 0.000 -0.007 -0.019 -0.797
Observations 777 776
Note: Standard deviations omitted because dummies means and observations describe all moments of the distribution.
Columns (3) & (4) calculate the raw dierences. Columns (5) & (6) apply a discontinuity specication from to equation
(1) using the set of controls as the dependent variables. Coe corresponds to the coecient, similar to specication (3) in
table (1). See supplemental gure S1C for the distribution of the t-statistics on the dierences and S1D for the distribution
of the t-statistics on the coecients.
Table S1B: Summary statistics one week before and after the the elec-
tion (Democrats only)
3
Before After Dierence T-Stat Coe T-Stat
Self-Report: Happy today? 0.615 0.464 -0.151 8.270 -0.243 -6.846
Self-Report: Sad today? 0.075 0.225 0.149 -11.379 0.273 9.634
Democrat 0.000 0.000 0.000 0.000
Independent 0.000 0.000 0.000 0.000
Republican 1.000 1.000 0.000 0.000
Female 0.393 0.377 -0.016 0.874 -0.006 -0.156
Age: Under 18 0.004 0.010 0.006 -2.033 0.006 1.624
Age: 18-24 0.013 0.018 0.005 -1.037 0.010 0.967
Age: 25-29 0.019 0.027 0.008 -1.395 -0.007 -0.557
Age: 30-34 0.032 0.034 0.002 -0.237 0.000 -0.037
Age: 35-44 0.120 0.093 -0.028 2.433 -0.044 -1.969
Age: 45-54 0.245 0.225 -0.020 1.301 -0.059 -1.853
Age: 55-64 0.298 0.287 -0.011 0.683 0.009 0.242
Age: 65 & Older 0.269 0.308 0.039 -2.331 0.085 2.644
Caucasian 0.936 0.921 -0.015 1.548 -0.026 -1.333
Hispanic 0.019 0.023 0.003 -0.644 0.008 0.717
African American 0.004 0.013 0.009 -2.465 0.009 1.416
American Indian 0.006 0.012 0.005 -1.492 0.003 0.386
Aleut Eskimo 0.004 0.005 0.001 -0.511 0.009 2.218
Asian or Pacic Islander 0.010 0.007 -0.002 0.683 -0.003 -0.479
Other 0.020 0.019 -0.002 0.294 0.001 0.062
Income: Under $25k 0.094 0.114 0.020 -1.743 0.022 1.050
Income: $25k - $35k 0.081 0.085 0.004 -0.392 -0.007 -0.430
Income: $35k -$50k 0.146 0.177 0.031 -2.247 -0.010 -0.424
Income: $50k - $75k 0.205 0.201 -0.004 0.279 0.003 0.107
Income: $75k - $100k 0.198 0.150 -0.047 3.400 -0.032 -1.128
Income: $100k - $125k 0.103 0.105 0.002 -0.179 0.006 0.249
Income: $125k - $150k 0.065 0.064 -0.001 0.118 0.020 1.409
Income: $150k or more 0.109 0.105 -0.004 0.327 -0.002 -0.115
Observations 1,563 1,371
Note: Standard deviations omitted because dummies means and observations describe all moments of the distribution.
Columns (3) & (4) calculate the raw dierences. Columns (5) & (6) apply a discontinuity specication from to equation
(1) using the set of controls as the dependent variables. Coe corresponds to the coecient, similar to specication (3) in
table (1). See supplemental gure S1E for the distribution of the t-statistics on the dierences and S1F for the distribution
of the t-statistics on the coecients.
Table S1C: Summary statistics one week before and after the the elec-
tion (Republicans only)
4
Before After Dierence T-Stat Coe T-Stat
Self-Report: Happy today? 0.584 0.571 -0.014 1.012 -0.076 -2.627
Self-Report: Sad today? 0.082 0.091 0.008 -1.086 0.030 2.045
Parent 0.774 0.768 -0.006 0.520 -0.016 -0.784
Female 0.388 0.376 -0.012 0.871 0.007 0.281
Age: Under 18 0.006 0.006 0.001 -0.308 0.004 0.804
Age: 18-24 0.023 0.021 -0.001 0.371 -0.001 -0.152
Age: 25-29 0.028 0.025 -0.003 0.700 -0.008 -0.776
Age: 30-34 0.031 0.038 0.007 -1.467 0.001 0.110
Age: 35-44 0.092 0.098 0.006 -0.787 -0.003 -0.190
Age: 45-54 0.200 0.223 0.023 -2.007 0.028 1.019
Age: 55-64 0.300 0.306 0.006 -0.469 0.019 0.714
Age: 65 & Older 0.321 0.283 -0.038 3.031 -0.040 -1.394
Caucasian 0.856 0.859 0.003 -0.325 0.003 0.115
Hispanic 0.019 0.022 0.003 -0.784 0.004 0.516
African American 0.056 0.047 -0.008 1.321 -0.019 -1.529
American Indian 0.013 0.018 0.005 -1.382 -0.005 -0.723
Aleut Eskimo 0.006 0.003 -0.003 1.841 0.002 0.325
Asian or Pacic Islander 0.007 0.009 0.001 -0.566 0.000 0.026
Other 0.043 0.043 -0.001 0.128 0.016 1.291
Income: Under $25k 0.146 0.145 -0.002 0.171 0.040 2.392
Income: $25k - $35k 0.117 0.113 -0.004 0.433 0.031 1.720
Income: $35k -$50k 0.167 0.166 -0.001 0.086 0.022 1.032
Income: $50k - $75k 0.203 0.202 -0.001 0.065 -0.009 -0.398
Income: $75k - $100k 0.148 0.153 0.005 -0.476 -0.039 -1.577
Income: $100k - $125k 0.082 0.084 0.002 -0.316 -0.024 -1.612
Income: $125k - $150k 0.054 0.049 -0.005 0.763 -0.042 -3.233
Income: $150k or more 0.084 0.088 0.005 -0.598 0.021 1.203
Observations 2,630 2,674
Note: Standard deviations omitted because dummies means and observations describe all moments of the distribution.
Columns (3) & (4) calculate the raw dierences. Columns (5) & (6) apply a discontinuity specication from to equation
(1) using the set of controls as the dependent variables. Coe corresponds to the coecient, similar to specication (3) in
table (1). See supplemental gure S2A for the distribution of the t-statistics on the dierences and S2B for the distribution
of the t-statistics on the coecients.
Table S2A: Summary statistics before and after the Newtown shootings
5
Before After Dierence T-Stat Coe T-Stat
Self-Report: Happy today? 0.481 0.499 0.019 -0.645 0.056 1.067
Self-Report: Sad today? 0.113 0.130 0.018 -0.951 0.023 0.564
Parent 0.000 0.000 0.000 0.000
Female 0.370 0.372 0.002 -0.081 -0.033 -0.566
Age: Under 18 0.024 0.024 0.001 -0.071 0.017 0.802
Age: 18-24 0.086 0.079 -0.007 0.431 -0.011 -0.295
Age: 25-29 0.086 0.079 -0.007 0.431 -0.050 -1.524
Age: 30-34 0.055 0.072 0.017 -1.213 0.022 0.958
Age: 35-44 0.116 0.106 -0.010 0.537 -0.025 -0.544
Age: 45-54 0.190 0.237 0.047 -1.995 0.047 1.036
Age: 55-64 0.259 0.254 -0.004 0.175 0.024 0.384
Age: 65 & Older 0.185 0.148 -0.037 1.718 -0.024 -0.571
Caucasian 0.844 0.820 -0.024 1.121 -0.090 -1.525
Hispanic 0.037 0.026 -0.011 1.119 0.008 0.357
African American 0.040 0.050 0.010 -0.805 0.007 0.259
American Indian 0.013 0.018 0.004 -0.601 -0.008 -0.447
Aleut Eskimo 0.013 0.006 -0.007 1.226 -0.004 -0.255
Asian or Pacic Islander 0.008 0.014 0.006 -1.000 0.002 0.124
Other 0.044 0.066 0.022 -1.713 0.085 3.135
Income: Under $25k 0.218 0.203 -0.016 0.666 0.059 1.392
Income: $25k - $35k 0.118 0.124 0.006 -0.339 -0.003 -0.105
Income: $35k -$50k 0.175 0.184 0.009 -0.399 0.057 1.438
Income: $50k - $75k 0.175 0.172 -0.002 0.114 -0.049 -1.145
Income: $75k - $100k 0.118 0.111 -0.007 0.358 -0.005 -0.158
Income: $100k - $125k 0.071 0.093 0.023 -1.451 0.021 0.615
Income: $125k - $150k 0.040 0.050 0.010 -0.805 -0.033 -1.289
Income: $150k or more 0.086 0.063 -0.023 1.521 -0.047 -1.432
Observations 595 621
Note: Standard deviations omitted because dummies means and observations describe all moments of the distribution.
Columns (3) & (4) calculate the raw dierences. Columns (5) & (6) apply a discontinuity specication from to equation
(1) using the set of controls as the dependent variables. Coe corresponds to the coecient, similar to specication (3) in
table (1). See supplemental gure S2C for the distribution of the t-statistics on the dierences and S2D for the distribution
of the t-statistics on the coecients.
Table S2B: Summary statistics before and after the Newtown shootings
(Non-Parents only)
6
Before After Dierence T-Stat Coe T-Stat
Self-Report: Happy today? 0.615 0.592 -0.022 1.466 -0.115 -3.506
Self-Report: Sad today? 0.073 0.078 0.005 -0.628 0.031 1.881
Parent 1.000 1.000 0.000 0.000
Female 0.393 0.377 -0.016 1.026 0.019 0.708
Age: Under 18 0.000 0.001 0.000 -0.571 0.000 -0.137
Age: 18-24 0.004 0.004 -0.001 0.261 0.000 -0.043
Age: 25-29 0.011 0.008 -0.003 0.832 0.003 0.530
Age: 30-34 0.024 0.028 0.004 -0.844 -0.006 -0.607
Age: 35-44 0.085 0.096 0.011 -1.220 0.004 0.202
Age: 45-54 0.203 0.219 0.015 -1.196 0.022 0.702
Age: 55-64 0.312 0.321 0.009 -0.649 0.019 0.643
Age: 65 & Older 0.361 0.323 -0.037 2.512 -0.041 -1.262
Caucasian 0.859 0.870 0.011 -1.071 0.032 1.411
Hispanic 0.014 0.021 0.007 -1.760 0.003 0.374
African American 0.060 0.047 -0.013 1.876 -0.027 -1.632
American Indian 0.013 0.018 0.005 -1.246 -0.004 -0.586
Aleut Eskimo 0.004 0.002 -0.002 1.402 0.003 0.797
Asian or Pacic Islander 0.007 0.007 0.000 0.023 0.000 -0.093
Other 0.043 0.036 -0.008 1.263 -0.006 -0.486
Income: Under $25k 0.125 0.127 0.002 -0.176 0.032 1.679
Income: $25k - $35k 0.116 0.110 -0.007 0.693 0.041 1.877
Income: $35k -$50k 0.165 0.161 -0.004 0.336 0.011 0.485
Income: $50k - $75k 0.211 0.211 0.000 -0.007 0.004 0.165
Income: $75k - $100k 0.157 0.165 0.008 -0.728 -0.048 -1.727
Income: $100k - $125k 0.085 0.081 -0.004 0.424 -0.037 -2.471
Income: $125k - $150k 0.057 0.049 -0.009 1.252 -0.045 -3.275
Income: $150k or more 0.083 0.096 0.013 -1.446 0.042 2.037
Observations 2,035 2,053
Note: Standard deviations omitted because dummies means and observations describe all moments of the distribution.
Columns (3) & (4) calculate the raw dierences. Columns (5) & (6) apply a discontinuity specication from to equation
(1) using the set of controls as the dependent variables. Coe corresponds to the coecient, similar to specication (3) in
table (1). See supplemental gure S2E for the distribution of the t-statistics on the dierences and S2F for the distribution
of the t-statistics on the coecients.
Table S2C: Summary statistics before and after the Newtown shootings
(Parents only)
7
Before After Dierence T-Stat Coe T-Stat
Self-Report: Happy today? 0.619 0.596 -0.022 2.155 -0.048 -2.162
Self-Report: Sad today? 0.070 0.077 0.007 -1.284 0.006 0.470
Boston Region 0.019 0.021 0.002 -0.607 0.001 0.123
Female 0.445 0.439 -0.005 0.504 -0.014 -0.650
Age: Under 18 0.019 0.018 -0.001 0.290 -0.016 -2.758
Age: 18-24 0.049 0.053 0.004 -0.832 0.010 0.999
Age: 25-29 0.051 0.051 0.000 -0.023 0.005 0.516
Age: 30-34 0.055 0.049 -0.006 1.378 0.015 1.598
Age: 35-44 0.134 0.143 0.010 -1.301 0.038 2.501
Age: 45-54 0.222 0.232 0.010 -1.170 0.030 1.914
Age: 55-64 0.261 0.245 -0.016 1.693 -0.026 -1.331
Age: 65 & Older 0.210 0.209 -0.001 0.112 -0.057 -3.359
Caucasian 0.839 0.838 -0.001 0.125 -0.001 -0.058
Hispanic 0.034 0.031 -0.003 0.712 0.006 0.809
African American 0.055 0.056 0.001 -0.182 -0.003 -0.258
American Indian 0.014 0.012 -0.002 0.837 -0.003 -0.705
Aleut Eskimo 0.007 0.006 -0.001 0.392 -0.003 -0.989
Asian or Pacic Islander 0.014 0.009 -0.005 2.023 -0.004 -0.919
Other 0.038 0.048 0.010 -2.348 0.008 0.940
Income: Under $25k 0.140 0.151 0.011 -1.420 -0.035 -2.589
Income: $25k - $35k 0.100 0.104 0.004 -0.623 0.003 0.275
Income: $35k -$50k 0.157 0.157 0.000 -0.054 0.011 0.698
Income: $50k - $75k 0.204 0.194 -0.011 1.273 0.002 0.097
Income: $75k - $100k 0.161 0.160 -0.001 0.120 0.022 1.470
Income: $100k - $125k 0.095 0.091 -0.004 0.635 -0.017 -1.088
Income: $125k - $150k 0.058 0.058 -0.001 0.172 0.007 0.675
Income: $150k or more 0.086 0.087 0.001 -0.243 0.007 0.582
Observations 4,316 4,623
Note: Standard deviations omitted because dummies means and observations describe all moments of the distribution.
Columns (3) & (4) calculate the raw dierences. Columns (5) & (6) apply a discontinuity specication from to equation
(1) using the set of controls as the dependent variables. Coe corresponds to the coecient, similar to specication (3) in
table (1). See supplemental gure S3A for the distribution of the t-statistics on the dierences and S3B for the distribution
of the t-statistics on the coecients.
Table S3A: Summary statistics before and after the Boston bombings
8
Before After Dierence T-Stat Coe T-Stat
Self-Report: Happy today? 0.618 0.597 -0.022 2.077 -0.047 -2.065
Self-Report: Sad today? 0.069 0.076 0.007 -1.185 0.004 0.330
Boston Region 0.000 0.000 0.000 0.000
Female 0.442 0.437 -0.005 0.469 -0.010 -0.486
Age: Under 18 0.019 0.017 -0.002 0.745 -0.016 -2.830
Age: 18-24 0.048 0.053 0.005 -1.043 0.013 1.314
Age: 25-29 0.049 0.050 0.002 -0.369 0.005 0.459
Age: 30-34 0.055 0.049 -0.006 1.309 0.014 1.459
Age: 35-44 0.136 0.144 0.009 -1.200 0.038 2.468
Age: 45-54 0.223 0.231 0.008 -0.876 0.031 1.910
Age: 55-64 0.261 0.246 -0.015 1.628 -0.027 -1.391
Age: 65 & Older 0.209 0.209 0.000 -0.021 -0.056 -3.328
Caucasian 0.839 0.839 -0.001 0.111 -0.001 -0.040
Hispanic 0.033 0.031 -0.002 0.566 0.006 0.736
African American 0.055 0.057 0.002 -0.354 -0.003 -0.264
American Indian 0.014 0.012 -0.002 1.015 -0.003 -0.629
Aleut Eskimo 0.007 0.006 -0.001 0.518 -0.004 -1.313
Asian or Pacic Islander 0.014 0.009 -0.005 2.148 -0.003 -0.591
Other 0.038 0.047 0.009 -2.193 0.008 0.860
Income: Under $25k 0.140 0.151 0.011 -1.407 -0.033 -2.416
Income: $25k - $35k 0.100 0.105 0.005 -0.741 0.004 0.338
Income: $35k -$50k 0.155 0.157 0.001 -0.184 0.010 0.657
Income: $50k - $75k 0.205 0.193 -0.012 1.398 0.001 0.079
Income: $75k - $100k 0.162 0.161 -0.001 0.153 0.022 1.446
Income: $100k - $125k 0.095 0.091 -0.004 0.669 -0.019 -1.168
Income: $125k - $150k 0.058 0.057 0.000 0.086 0.007 0.723
Income: $150k or more 0.085 0.086 0.001 -0.151 0.006 0.502
Observations 4,235 4,528
Note: Standard deviations omitted because dummies means and observations describe all moments of the distribution.
Columns (3) & (4) calculate the raw dierences. Columns (5) & (6) apply a discontinuity specication from to equation
(1) using the set of controls as the dependent variables. Coe corresponds to the coecient, similar to specication (3) in
table (1). See supplemental gure S3C for the distribution of the t-statistics on the dierences and S3D for the distribution
of the t-statistics on the coecients.
Table S3B: Summary statistics before and after the Boston bombings
(Non-Boston region only)
9
Before After Dierence T-Stat Coe T-Stat
Self-Report: Happy today? 0.642 0.589 -0.053 0.711 -0.165 -1.881
Self-Report: Sad today? 0.086 0.116 0.029 -0.645 0.119 3.567
Boston Region 1.000 1.000 0.000 0.000
Female 0.580 0.547 -0.033 0.436 -0.245 -3.996
Age: Under 18 0.000 0.063 0.063 -2.517 0.014 0.235
Age: 18-24 0.123 0.074 -0.050 1.092 -0.206 -1.867
Age: 25-29 0.148 0.063 -0.085 1.809 0.044 1.965
Age: 30-34 0.062 0.042 -0.020 0.578 0.124 1.030
Age: 35-44 0.037 0.084 0.047 -1.326 0.060 1.013
Age: 45-54 0.136 0.274 0.138 -2.304 0.023 0.165
Age: 55-64 0.222 0.189 -0.033 0.532 0.067 0.738
Age: 65 & Older 0.272 0.211 -0.061 0.938 -0.126 -0.509
Caucasian 0.802 0.800 -0.002 0.041 -0.041 -0.520
Hispanic 0.074 0.042 -0.032 0.891 0.049 0.839
African American 0.062 0.021 -0.041 1.324 0.008 0.155
American Indian 0.000 0.021 0.021 -1.422 -0.018 -1.631
Aleut Eskimo 0.012 0.021 0.009 -0.452 0.068 1.098
Asian or Pacic Islander 0.025 0.032 0.007 -0.275 -0.115 -1.213
Other 0.025 0.063 0.038 -1.261 0.049 1.704
Income: Under $25k 0.148 0.158 0.010 -0.178 -0.170 -2.665
Income: $25k - $35k 0.086 0.053 -0.034 0.867 -0.053 -1.061
Income: $35k -$50k 0.222 0.168 -0.054 0.890 0.046 0.416
Income: $50k - $75k 0.173 0.221 0.048 -0.801 0.021 0.119
Income: $75k - $100k 0.086 0.105 0.019 -0.423 0.028 0.845
Income: $100k - $125k 0.074 0.084 0.010 -0.247 0.100 5.703
Income: $125k - $150k 0.086 0.063 -0.023 0.579 -0.037 -0.170
Income: $150k or more 0.123 0.147 0.024 -0.461 0.066 0.494
Observations 81 95
Note: Standard deviations omitted because dummies means and observations describe all moments of the distribution.
Columns (3) & (4) calculate the raw dierences. Columns (5) & (6) apply a discontinuity specication from to equation
(1) using the set of controls as the dependent variables. Coe corresponds to the coecient, similar to specication (3) in
table (1). See supplemental gure S3E for the distribution of the t-statistics on the dierences and S3F for the distribution
of the t-statistics on the coecients.
Table S3C: Summary statistics before and after the Boston bombings
(Boston region only)
10
Independent variables (1) (2) (3) (4) (5)
Post-election
.036
(.030)
-.001
(.011)
.014
(.023)
.013
(.026)
-.051
(.056)
Post-election * One de-
gree polynomial of days
No No Yes Yes Yes
Post-election * ree de-
gree polynomial of days
No No No No Yes
Socio - Demographic &
MSA Controls
No No No Yes Yes
Time restriction +/- one day +/- one week +/- one week +/- one week +/- one week
Observations 265 1,553 1,553 1,553 1,553
Note: *, **, and *** indicate signicance at the 10%, 5%, and 1% condence levels, respectively. Standard errors are clus-
tered at the Metropolitan level. Socio-demographic controls include gender, age indicators, race indicators, and income
indicators. MSA controls included indicators for the metropolitan statistical area.
Table S4A: Democrat sadness one week surrounding 2012 election
Dependent variable: Are you sad today?
Independent variables (1) (2) (3) (4) (6)
Post-election
.423***
(.039)
.149***
(.012)
.273***
(.028)
.268***
(.031)
.450***
(.064)
Post-election * One de-
gree polynomial of days
No No Yes Yes No
Post-election * ree de-
gree polynomial of days
No No No No Yes
Socio - Demographic &
MSA Controls
No No No Yes Yes
Time restriction +/- one day +/- one week +/- one week +/- one week +/- one week
Observations 465 2,934 2,934 2,934 2,934
Table S4B: Republican sadness one week surrounding 2012 election
Dependent variable: Are you sad today?
11
Independent variables (1) (2) (3) (4) (5) (6) (7)
Post-Newtown
.032
(.021)
.008
(.006)
.030**
(.015)
.024
(.016)
.002
(.043)
-.003
(.042)
.029
(.018)
Post-Newtown * One degree
polynomial of days
No No Yes Yes No Yes Yes
Post-Newtown * ree degree
polynomial of days
No No No No Yes No No
Socio - Demographic & MSA
Controls
No No No Yes Yes Yes Yes
Time restriction +/- one day +/- one week +/- one week +/- one week +/- one week +/- one week +/- one week
Parents & Non-Parents Both Both Both Both Both Non-Parents Parents
Observations 695 5,304 5,304 5,304 5,304 1,216 4,088
Note: *, **, and *** indicate signicance at the 10%, 5%, and 1% condence levels, respectively. Standard errors are clustered at the Metropolitan level. Socio-demograph-
ic controls include gender, age indicators, race indicators, and income indicators. MSA controls included indicators for the metropolitan statistical area.
Table S5: Self-reported sadness before and after Newtown shooting
Dependent variable: Are you sad today?
12
Independent variables (1) (2) (3) (4) (5) (6) (7)
Post-Boston
.005
(.014)
.007
(.005)
.006
(.013)
.010
(.013)
-.003
(.033)
.008
(.013)
.090
(.062)
Post-Boston * One degree poly-
nomial of days
No No Yes Yes No Yes Yes
Post-Boston * ree degree poly-
nomial of days
No No No No Yes No No
Socio - Demographic & MSA
Controls
No No No Yes Yes Yes Yes
Time restriction +/- one day +/- one week +/- one week +/- one week +/- one week +/- one week +/- one week
Boston Region & Non-Boston
Region
Both Both Both Both Both Non-Boston Boston
Observations 1,360 8,939 8,939 8,939 8,939 8,763 176
Note: *, **, and *** indicate signicance at the 10%, 5%, and 1% condence levels, respectively. Standard errors are clustered at the Metropolitan level. Socio-demograph-
ic controls include gender, age indicators, race indicators, and income indicators. MSA controls included indicators for the metropolitan statistical area.
Table S6: Self-reported sadness before and after Boston bombing
Dependent variable: Are you sad today?
13
Independent variables (1) (2) (3) (4) (5)
Post-election
.073
(.106)
.073*
(.037)
.088
(.096)
.097
(.110)
.226
(.247)
Post-election * One de-
gree polynomial of days
No No Yes Yes No
Post-election * ree de-
gree polynomial of days
No No No No Yes
Socio - Demographic &
MSA Controls
No No No Yes Yes
Time restriction +/- one day +/- one week +/- one week +/- one week +/- one week
Observations 265 1,553 1,553 1,553 1,553
Note: *, **, and *** indicate signicance at the 10%, 5%, and 1% condence levels, respectively. Standard errors are clus-
tered at the Metropolitan level. Socio-demographic controls include gender, age indicators, race indicators, and income
indicators. MSA controls included indicators for the metropolitan statistical area. Happiness on scale of 1-5 where 1 is very
unhappy, 2 unhappy, 3 neither happy nor sad, 4 happy, and 5 very happy.
Table S7A: Democrat happiness one week surrounding 2012 election
Dependent variable: Happiness on scale of 1-5
Independent variables (1) (2) (3) (4) (6)
Post-election
-1.139***
(.104)
-.448***
(.034)
-.796***
(.074)
-.789***
(.081)
-1.231***
(.217)
Post-election * One de-
gree polynomial of days
No No Yes Yes No
Post-election * ree de-
gree polynomial of days
No No No No Yes
Socio - Demographic &
MSA Controls
No No No Yes Yes
Time restriction +/- one day +/- one week +/- one week +/- one week +/- one week
Observations 465 2,934 2,934 2,934 2,934
Table S7B: Republican happiness one week surrounding 2012 election
Dependent variable: Happiness on scale of 1-5
14
0 .1 .2 .3 .4
-2 -1 0 1 2 3
T-statistics of coefficients from Supplemental Table 1A
0 .1 .2 .3 .4
-2 -1 0 1 2 3
T-statistics of differences from Supplemental Table 1A
Figure S1A: Kernel density distribution for mean differences t-statistics
from Table S1A
Figure S1B: Kernel density distribution for coefcient t-statistics from
Table S1A
Note: Normal approximation shown with kernel density estimate. Variables Happy & Sad omitted from density estimation
because they are dependent variables.
15
0 .1 .2 .3 .4
-2 -1 0 1 2
T-statistics of coefficients from Supplemental Table 1B
0 .2 .4 .6
-2 -1 0 1 2
T-statistics of differences from Supplemental Table 1B
Figure S1C: Kernel density distribution for mean differences t-statistics
from Table S1B
Figure S1D: Kernel density distribution for coefcient t-statistics from
Table S1B
Note: Normal approximation shown with kernel density estimate. Variables Happy & Sad omitted from density estimation
because they are dependent variables.
16
0 .1 .2 .3 .4
-2 0 2 4
T-statistics of coefficients from Supplemental Table 1C
0 .05 .1 .15 .2 .25
-4 -2 0 2 4
T-statistics of differences from Supplemental Table 1C
Figure S1E: Kernel density distribution for mean differences t-statistics
from Table S1C
Figure S1F: Kernel density distribution for coefcient t-statistics from
Table S1C
Note: Normal approximation shown with kernel density estimate. Variables Happy & Sad omitted from density estimation
because they are dependent variables.
17
0 .1 .2 .3
-4 -2 0 2 4
T-statistics of coefficients from Supplemental Table 2A
0 .1 .2 .3 .4
-2 0 2 4
T-statistics of differences from Supplemental Table 2A
Figure S2A: Kernel density distribution for mean differences t-statistics
from Table S2A
Figure S2B: Kernel density distribution for coefcient t-statistics from
Table S2A
Note: Normal approximation shown with kernel density estimate. Variables Happy & Sad omitted from density estimation
because they are dependent variables.
18
0 .1 .2 .3 .4
-2 0 2 4
T-statistics of coefficients from Supplemental Table 2B
0 .1 .2 .3 .4
-3 -2 -1 0 1 2
T-statistics of differences from Supplemental Table 2B
Figure S2C: Kernel density distribution for mean differences t-statistics
from Table S2B
Figure S2D: Kernel density distribution for coefcient t-statistics from
Table S2B
Note: Normal approximation shown with kernel density estimate. Variables Happy & Sad omitted from density estimation
because they are dependent variables.
19
0 .1 .2 .3 .4
-4 -2 0 2
T-statistics of coefficients from Supplemental Table 2C
0 .1 .2 .3 .4
-2 -1 0 1 2 3
T-statistics of differences from Supplemental Table 2C
Figure S2E: Kernel density distribution for mean differences t-statistics
from Table S2C
Figure S2F: Kernel density distribution for coefcient t-statistics from
Table S2C
Note: Normal approximation shown with kernel density estimate. Variables Happy & Sad omitted from density estimation
because they are dependent variables.
20
0 .1 .2 .3
-4 -2 0 2 4
T-statistics of coefficients from Supplemental Table 3A
0 .1 .2 .3 .4
-4 -2 0 2
T-statistics of differences from Supplemental Table 3A
Figure S3A: Kernel density distribution for mean differences t-statistics
from Table S3A
Figure S3B: Kernel density distribution for coefcient t-statistics from
Table S3A
Note: Normal approximation shown with kernel density estimate. Variables Happy & Sad omitted from density estimation
because they are dependent variables.
21
0 .1 .2 .3
-4 -2 0 2 4
T-statistics of coefficients from Supplemental Table 3B
0 .1 .2 .3 .4
-4 -2 0 2 4
T-statistics of differences from Supplemental Table 3B
Figure S3C: Kernel density distribution for mean differences t-statistics
from Table S3B
Figure S3D: Kernel density distribution for coefcient t-statistics from
Table S3B
Note: Normal approximation shown with kernel density estimate. Variables Happy & Sad omitted from density estimation
because they are dependent variables.
22
0 .1 .2 .3
-4 -2 0 2 4 6
T-statistics of coefficients from Supplemental Table 3C
0 .1 .2 .3 .4
-3 -2 -1 0 1 2
T-statistics of differences from Supplemental Table 3C
Figure S3E: Kernel density distribution for mean differences t-statistics
from Table S3C
Figure S3F: Kernel density distribution for coefcient t-statistics from
Table S3C
Note: Normal approximation shown with kernel density estimate. Variables Happy & Sad omitted from density estimation
because they are dependent variables.
23
-.5 -.4 -.3 -.2 -.1 0
Coefficient estimate on Post-Election for Republicans
0 5 10 15 20 25 30
Days before and after Election
-.2 -.1 0 .1 .2 .3
Coefficient estimate on Post-Election for Democrats
0 5 10 15 20 25 30
Days before and after Election
Supplemental Figure S4A: Table (1A) column (4) regression where time
window changes
Supplemental Figure S4B: Table (1B) column (4) regression where time
window changes
24
-.5 -.4 -.3 -.2 -.1 0
Coefficient estimate on Post-Boston
0 5 10 15 20 25 30
Days before and after Boston bombings
-.5 -.4 -.3 -.2 -.1 0
Coefficient estimate on Post-Newtown
0 5 10 15 20 25 30
Days before and after Newtown shootings
Supplemental Figure S4C: Table (2) column (4) regression where time
window changes
Supplemental Figure S4D: Table (3) column (4) regression where time
window changes
25
-.15 -.1 -.05 0 .05 .1
Week Coefficient (Baseline is 8th Week Before Election)
-7 -6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6
Weeks before and after Election
-.1 -.05 0 .05 .1
Week Coefficient (Baseline is 8th Week Before Election)
-7 -6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6
Weeks before and after Election
Supplemental Figure S5A: Democrat self-reported emotional state two
months before and after the election: “Are you happy today?”
Supplemental Figure S5B: Republican self-reported emotional state two
months before and after the election: “Are you happy today?”
Democrats before and
after 2012 election
Republicans before and
after 2012 election
Regression coecents on week indicator from
equation (1) for the set of Democrats in the data-
set. Week 1 is the baseline. Regressions include no
controls and are clustered at the MSA level.
Drop in happiness for Re-
publicans in the rst week
post-election is disipates in
the subsequent weeks.
Regression coecents on week indicator from
equation (1) for the set of Democrats in the data-
set. Week 1 is the baseline. Regressions include no
controls and are clustered at the MSA level.
26
250 300 350 400 450
Total Responses
-7 -6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6
Days before and after Newtown shooting
300 400 500 600 700
Total Responses
-7 -6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6
Days before and after election
Supplemental Figure S6A: Count of responses before and after the Elec-
tion
Supplemental Figure S6B: Count of the responses before and after New-
town
27
400 600 800 1000
Total Responses
-7 -6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6
Days before and after Boston Marathon bombing
Supplemental Figure S6C: Count of responses before and after the Bos-
ton
28
.45 .5 .55 .6 .65
(mean) happy
-10 -5 0 5 10
weekelect
Treated: predicted Treated: actual
Regression with Newey-West standard errors
Supplemental Figure S8A: Republican happiness two months before and
after the election: Weekly interrupted time series
Break has point estimate of
-.1247 and t-stat of -3.02 on 16
data points using interrupted
time series approach
29
.3 .4 .5 .6 .7 .8
(mean) happy
-50 0 50
dayelect1
Treated: predicted Treated: actual
Regression with Newey-West standard errors
Supplemental Figure S8B: Republican happiness two months before and
after the election: Daily interrupted time series
Break has point estimate of
-.1217 and t-stat of -5.15 on 120
data points using interrupted
time series approach
30
... We also examined whether the psychological dynamics of a presidential election would provoke associated positive and negative emotions. Because partisans are personally invested in election outcomes, their group-based emotional experience should reflect emerging knowledge that their favored candidate won or lost the election [22,23]. Negative feelings of anger, anxiety, irritability, and nervousness are indicators of disappointment and, we reason, the discomfort stemming from any cognitive dissonance elicited from an unexpected election loss [24]. ...
... From the undeclared to the declared period, Democrats' emotions became more prevailingly positive and less negative while Republicans' emotions became less positive and more prevailingly negative. These emotional profiles may both reflect the arousal of dissonance [22][23][24] and increasingly confident assessments that the election was illegitimate or legitimate. ...
Article
Full-text available
The present study, conducted immediately after the 2020 presidential election in the United States, examined whether Democrats’ and Republicans’ polarized assessments of election legitimacy increased over time. In a naturalistic survey experiment, people ( N = 1,236) were randomly surveyed either during the week following Election Day, with votes cast but the outcome unknown, or during the following week, after President Joseph Biden was widely declared the winner. The design unconfounded the election outcome announcement from the vote itself, allowing more precise testing of predictions derived from cognitive dissonance theory. As predicted, perceived election legitimacy increased among Democrats, from the first to the second week following Election Day, as their expected Biden win was confirmed, whereas perceived election legitimacy decreased among Republicans as their expected President Trump win was disconfirmed. From the first to the second week following Election Day, Republicans reported stronger negative emotions and weaker positive emotions while Democrats reported stronger positive emotions and weaker negative emotions. The polarized perceptions of election legitimacy were correlated with the tendencies to trust and consume polarized media. Consumption of Fox News was associated with lowered perceptions of election legitimacy over time whereas consumption of other outlets was associated with higher perceptions of election legitimacy over time. Discussion centers on the role of the media in the experience of cognitive dissonance and the implications of polarized perceptions of election legitimacy for psychology, political science, and the future of democratic society.
... Therefore, if this population viewed Trump as a symbol of hope as a self-identified white person who represents them and would address their grievances, then this group would have experienced symbolic empowerment as a result of the November 2016 election. Previous research has examined the role of individuals' personal political affiliations with their health and happiness following elections, in particular with regard to being affiliated with the losing political party (Pierce et al., 2016;Rosman et al., 2021). However, our study differs in that it examines differences in responses to the sociopolitical environment by race/ethnicity due to the way that U. S. presidential politics tend to polarize people by the social constructs of race/ethnicity (Abramowitz and McCoy, 2018). ...
... On the contrary, the predicted number of poor mental health days was slightly elevated compared to on average in November 2016 and February 2017, but these differences were not as great as those seen among their counterparts in Clinton states. These findings suggest that for the white population, especially in states where Clinton won the majority of votes, the election of President Trump was a stressful event, which may coincide with existing research on stress and poor mental health resulting from being on the losing side of an election (Pierce et al., 2016;Baum-Baicker, 2020). It is possible that white populations in Clinton states experienced symbolic disempowerment in a sociopolitical climate that portended that they or issues they cared about would be marginalized. ...
Article
The 2016 election of United States (U.S.) President Donald Trump was a political event that may have affected population-level mental health. A prominent theme in the Trump election was anti-immigrant policy that contributed to a racist and xenophobic sociopolitical climate. Applying a symbolic dis/empowerment framework, this study examines whether there was an effect of the Trump election on the mental health of the U.S. population that differed by race/ethnicity, language of interview, and state-level support for Trump or Clinton. We used data from the Centers for Disease Control and Prevention's Behavioral Risk Factor Surveillance System 2011–2018 to examine trends in poor mental health days in the five months after the U.S Presidential election (November 2016 to March 2017) compared to all other survey months. We conducted difference-in-differences analyses using negative binomial regression models to examine the effect of the five post-election months on the rate of poor mental health days, comparing six population categories: 1) non-Latinx white populations in Trump states, 2) non-Latinx white populations in Clinton states, 3) English-speaking Latinx populations in Trump states, 4) English-speaking Latinx populations in Clinton states, 5) Spanish-speaking Latinx populations in Trump states, and 6) Spanish-speaking Latinx populations in Clinton states. White populations in Clinton states reported more poor mental health days in response to the five months post-election period compared to white populations in Trump states. English-speaking Latinx people living in Trump states experienced higher than expected poor mental health days in November 2016 and February 2017. Spanish-speaking Latinx people, by contrast, reported fewer poor mental health days in the post-election period. The 2016 U.S. presidential election preceded population-level changes in mental health that support a symbolic dis/empowerment framework. We discuss possible explanations and the mental health implications for future major political events.
... Election outcomes are an important source of mass political attitudes. Those on the losing side of elections report less satisfaction with democracy (Anderson & LoTempio, 2002;Blais & Gélineau, 2007), more doubt about election integrity (Cantú & García-Ponce, 2015), diminished feelings of political efficacy (Anderson et al., 2005), and a host of negative emotions (Oc et al., 2018;Pierce et al., 2016). Much less research has explored the relationship between election outcomes and trust in the general public among partisan losers. ...
... And because party identification increasingly reflects deeper social cleavages, partisan victory is experienced as a 'home team' victory (Green et al., 2004;Holmberg, 1999). Meanwhile, partisan losers express a bundle of negative emotions, including more negative economic perceptions of the nation, feelings of unhappiness, lower self-esteem, and even a decline in testosterone (Oc et al., 2018;Pierce et al., 2016;Quaranta et al., 2020;Stanton et al., 2009). ...
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Election outcomes often produce winner-loser gaps in political attitudes. However, existing research has largely overlooked the relationship between election outcomes and generalized trust. This article explores the impact of electoral loss on how partisan losers view the trustworthiness of the general public. I test this question with panel data collected before and after the 2016 U.S. presidential election. The results indicate that the only group of voters with a meaningful decline in trust following the election were those who voted for the losing candidate and had strong attachments to the Democratic Party. Those with weak attachments to the Democratic Party, as well as election winners, experienced no substantial change in trust following the election. The evidence suggests that in times of partisan polarization, election outcomes may weaken some people’s willingness to trust others.
... First, building on an instrumental perspective on voting and assuming that citizens vote for the party that is ideologically closest to them, the (expected) utility from the system is lower for losers and higher for winners (Esaiasson, 2011). Second, political legitimacy beliefs may also change because of the emotions voters have invested in the electoral game (Esaiasson, 2011;Holmberg, 1999;Pierce et al., 2016): losing hurts emotionally and winning makes people happy. Third, it could be that losers in particular adjust their level of legitimacy beliefs in reaction to the electoral defeat for reasons of cognitive consistency (Anderson et al., 2005;Daniller, 2016;Esaiasson, 2011). ...
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Modern democracies are dependent on regular elections and citizens’ legitimacy beliefs. Studies have shown that repeated electoral defeats are associated with lower levels of satisfaction with democracy and political trust. However, previous studies have only considered one type of legitimacy belief at a time, never in comparison. What is more, all previous work is based on observational studies and has not been able to identify any causal effects of losing repeatedly. Building on previous work and classic theories of political legitimacy beliefs, we argue that repeatedly losing in elections represents a form of long‐term exclusion from democratic power that has additional negative effects on people's legitimacy beliefs because they lose faith in the system and start questioning its evenhandedness. We support our predictions using six‐wave panel data and test our hypothesis a total of sixteen times within the same context. The findings show that repeated losers are never less satisfied with democracy but that an additional electoral loss leads to lower levels of political trust. The findings have important implications for the meaning of different indicators of legitimacy beliefs but also for electoral research and the underpinnings of stable democracies. This article is protected by copyright. All rights reserved
... In particular, partisans display a marked aversion to the prospect of electoral defeat (Huddy, Mason, and Aarøe 2015). While winning elections confers strong psychological affirmation to partisans on the winning side, electoral defeat elicits even stronger feelings of despair and unhappiness among partisans (Pierce et al. 2016). ...
Preprint
The future of the COVID-19 pandemic in the United States will likely depend upon Americans’ openness to vaccination against the virus. Yet a sizable partisan gap in perceptions of the vaccine has emerged. In this Letter, we propose that partisans’ psychological aversion to electoral loss presents an opportunity for the deployment of framing messages to increase openness to the COVID-19 vaccine. Specifically, we analyze the effects of a “Shot to Win” (STW) message that frames vaccination as a means of ensuring that a party’s members remain healthy enough to vote and defeat the opposing party. Results of a pre-registered survey experiment provide evidence that STW messaging increases Republicans’ openness to vaccination across a variety of attitudinal and behavioral outcomes, and that STW’s effectiveness extends beyond its function as a mere elite cue. More broadly, these results exemplify how out-party attitudes might be effectively leveraged in service of the public interest.
... At current, similar phenomena have only been documented for political trust outcomes from natural disasters (Dussaillant & Guzmán, 2014). In the case of life satisfaction and happiness, analogous effects have been observed for partisan electoral results (Pierce et al., 2016) and unexpected victories in sports events (Janhuba, 2019), albeit for a much shorter time frame. ...
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Consequences of social trust are comparatively well studied, while its societal determinants are often subject to debate. This paper studies both in the context of Catalan attempts to secede from Spain: First, we test whether Catalonia enjoys higher levels of social capital that it is prevented from capitalizing on. Second, the paper examines whether secessionist movements create animosity and political divisions within society that undermine trust. Employing the nine available waves of the European Social Survey for Spain, we only find weak indications that social trust levels are higher in Catalonia than in the rest of the country. Interestingly, we further find testimony of a purely transient “exuberance effect” after secession became a real option, indicating that the long‐run evolution of social trust may best be thought of as a stable punctuated equilibrium.
... It is this type of speech that this study is concerned with. Partisan losers are affected strongly as compared to partisan winners [9]. While this may cut across cultures, it is thought that in a high context culture like Malaysia, the impact is felt even more when defeat is not a topic that is open for discussion in an explicit manner. ...
Conference Paper
Concession speeches are usually given immediately after results of an election are released. In these speeches, a losing candidate will concede defeat, before the winner presents his/her acceptance speech. However, defeats are also mentioned in speeches other than concession speeches. These are political speeches which happen some time after the defeat. By nature, political speeches provide insights into how language is utilised by politicians to achieve particular objectives. Many studies have investigated speech acts in political speeches, looking at how the speaker attempts to persuade the audience through particular language use. However, limited attention has been given to how language is used in other post-defeat speeches other than concession speeches. This study investigates a 2018 speech delivered by a politician in his losing party’s first ever mass assembly after an election loss. We describe ways in which the defeat is addressed based on discourse and sociocultural practices in Fairclough’s three dimensional framework. We show how the use of intertextuality and offensive strategies through word-use lead to the communication of values that aim to create a desired mindset in the audience and to gain their confidence in the politician’s leadership.
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This study analyses the changes in mental health in the UK that occurred as a result of the 2016 referendum on UK membership of the EU (Brexit). Using the Household Longitudinal Study, we compare the levels of self-reported mental distress, mental functioning and life satisfaction be-fore and after the referendum. A linear fixed effects analysis revealed an overall decrease in mental health post-referendum with higher levels of mental distress, and a decline in the SF-12 Mental Component Summary score. Furthermore, the study does not find evidence of significant changes in overall life satisfaction in the two years after the referendum. Younger men, highly educated and Natives, especially those living in stronger “Remain areas”, seem to be the groups most affected by the Brexit in terms of mental health. Overall, the results of this study suggest that the outcome of the referendum and the economic uncertainty that it brought impacted the mental health of voters in a negative and diverging way.
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How do partisans react when their candidate wins or loses a gubernatorial election? Previous work shows that when parties win presidential elections, demand for their affiliated local newspapers decreases relative to the losing party’s newspapers. However, it is unclear if this negative link extends beyond presidential races into state-level elections. To test this relationship, we analyze demand for partisan and non-partisan newspapers in Virginia and New Jersey—two states that hold off-cycle gubernatorial elections with no competition from federal elections—from 1933 to 2005. We find demand for local newspapers associated with the winning party declines after gubernatorial elections compared to demand for other newspapers. The results also shed light on whether (and which) winning partisans are disengaging completely or shifting their consumption to independent newspapers. Taken together, our study suggests that state-level elections significantly influence local newspaper consumption and adds valuable local context to our understanding of the political dynamics of news demand.
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We study the effect of the Brexit referendum result on subjective well‐being in the United Kingdom. Using a quasi‐experimental design, we find that the referendum’s outcome led to an overall decrease in subjective well‐being in the United Kingdom compared to a control group. The effect is driven by individuals who hold an overall positive image of the European Union and shows little signs of adaptation during the Brexit transition period. Economic expectations are potential mechanisms of this effect.
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Automobile manufacturers frequently use promotions involving cash incentives. While payments are nominally directed to either customers or dealers, the ultimate beneficiary of the promotion depends on the outcome of price negotiation. We use program evaluation methods to compare the incidence of these two types of promotions. Customers obtain 70 to 90 percent of a customer rebate, but only 30 to 40 percent of a dealer discount promotion, a $500 difference for a typical promotion. Our leading hypothesis is that pass-through rates differ because of information asymmetries: customer rebates are well-publicized to customers, while dealer discount promotions are not.
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The division of America into red states and blue states misleadingly suggests that states are split into two camps, but along most dimensions, like political orientation, states are on a continuum. By historical standards, the number of swing states is not particularly low, and America's cultural divisions are not increasing. But despite the flaws of the red state/blue state framework, it does contain two profound truths. First, the heterogeneity of beliefs and attitudes across the United States is enormous and has always been so. Second, political divisions are becoming increasingly religious and cultural. The rise of religious politics is not without precedent, but rather returns us to the pre-New Deal norm. Religious political divisions are so common because religious groups provide politicians the opportunity to send targeted messages that excite their base.
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The media environment is changing. Today in the United States, the average viewer can choose from hundreds of channels, including several twenty-four hour news channels. News is on cell phones, on iPods, and online; it has become a ubiquitous and unavoidable reality in modern society. The purpose of this book is to examine systematically, how these differences in access and form of media affect political behaviour. Using experiments and new survey data, it shows how changes in the media environment reverberate through the political system, affecting news exposure, political learning, turnout, and voting behavior.
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This article investigates the long-term effect of September 11, 2001 on the political behaviors of victims' families and neighbors. Relative to comparable individuals, family members and residential neighbors of victims have become-and have stayed-significantly more active in politics in the last 12 years, and they have become more Republican on account of the terrorist attacks. The method used to demonstrate these findings leverages the random nature of the terrorist attack to estimate a causal effect and exploits new techniques to link multiple, individual-level, governmental databases to measure behavioral change without relying on surveys or aggregate analysis.
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In the United States happiness rises slightly, on average, from ages 18 to midlife, and declines slowly thereafter. This pattern for the total population is the net result of disparate trends in the satisfaction people get from various life domains: their financial situation, family life, health, and work. The slight rise in happiness through midlife is due chiefly to growing satisfaction with one's family life and work, which together more than offset decreasing satisfaction with health. Beyond midlife, happiness edges downward as a continuing decline in satisfaction with health is joined by diminishing satisfaction with one's family situation and work; these negative trends are offset considerably, however, by a sizeable upturn in later life in people's satisfaction with their financial situation. These findings come from an analysis of the United States General Social Surveys, using the demographer's synthetic panel technique. They support neither the mainstream economics view that well-being depends only on one's objective conditions nor the psychologists' strong setpoint model in which adaptation to such conditions is rapid and complete. They are consistent with a "bottom up" model in which happiness is the net outcome of both objective and subjective factors in various life domains.