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The “Issues and Leaders” model shows that aggregate votes for President in U.S. elections from 1972 to 2012 can be accurately predicted from people's perceptions of the candidates' issue handling competence and leadership qualities. For the past five elections, the model's ex ante forecasts, calculated three to two months prior to Election Day, were competitive with those from the best of eight established political economy models. Model accuracy substantially improved closer to Election Day. The Election Eve forecasts missed the actual vote shares by, on average, little more than one percentage point and thus reduced the error of the Gallup pre-election poll by 30%. The model demonstrates that the direct influence of party identification on vote choice decreases over the course of the campaign, whereas issues gain importance. The model has decision-making implications in that it advises candidates to engage in agenda setting and to increase their perceived issue-handling and leadership competence.
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Issue and Leader Voting in U.S. Presidential Elections
(Highlights)
The Issues and Leaders model predicts U.S. presidential elections from voters perceptions
of candidates issue-handling competence and leadership skills.
The model uses a different approach and different data than the established models and is
thus particularly valuable for a combined forecast.
Two months prior to Election Day, the model provides as accurate forecasts as
established models. Its Election Eve forecast outperformed the final Gallup pre-election
poll.
The model shows that issues gain importance over the course of the campaign, whereas
the direct influence of party identification on aggregate votes decreases.
The model has decision-making implications for those involved in political campaigns.
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Issue and Leader Voting in U.S. Presidential Elections
Andreas Graefe
Department of Communication Science and Media Research
LMU Munich, Germany
+49-151-27087041
a.graefe@lmu.de
Acknowledgments: J. Scott Armstrong, Alfred Cuzán, James Garand, and Michael
Lewis-Beck provided helpful comments. I also received valuable suggestions when
presenting earlier versions of the paper at the 2012 International Symposium on Forecasting
in Boston, the 2013 SPSA Annual Conference in Orlando, and at an internal talk at the
Department of Communication Science and Media Research at LMU Munich. Jamie Elfant
has done editorial work.
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Issue and Leader Voting in U.S. Presidential Elections
Abstract. The “Issues and Leaders” model shows that aggregate votes for President in U.S.
elections from 1972 to 2012 can be accurately predicted from people’s perceptions of the candidates’
issue handling competence and leadership qualities. For the past five elections, the model’s ex ante
forecasts, calculated three to two months prior to Election Day, were competitive with those from the best
of eight established political economy models. Model accuracy substantially improved closer to Election
Day. The Election Eve forecasts missed the actual vote shares by, on average, little more than one
percentage point and thus reduced the error of the Gallup pre-election poll by 30%. The model
demonstrates that the direct influence of party identification on vote choice decreases over the course of
the campaign, whereas issues gain importance. The model has decision-making implications in that it
advises candidates to engage in agenda setting and to increase their perceived issue-handling and
leadership competence.
Keywords. Political economy models, forecasting, model accuracy, voter perceptions, issue
ownership
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1 Introduction
Many traditional election forecasting models predict the election outcome based on the state of
the economy and the incumbent’s popularity months before the start of the general election campaign.
When these models are evaluated based on how often they correctly predict the winner of the popular
vote, their performance is impressive: they rarely fail. This track record has fostered the dominant view
that an election is a referendum on the government’s performance (Tufte 1978). That is, voters are
assumed to assess the government’s record and reward or punish the incumbent party accordingly. If
voters are satisfied, they keep the government in place; otherwise, they vote for the out-party. According
to that view, it is not necessary to consider the quality of the candidates’ campaigns when making
forecasts of the election outcome.
However, none of the established models were designed to predict election winners; they were
designed to predict vote-shares. Thus, the deviation of the forecast from the actual election result is the
preferred measure for evaluating accuracy. The mean absolute error of eight established models that
published forecasts prior to each of the past five elections from 1996 to 2012 was 3.1 percentage points.
Given that U.S. presidential elections are often very close, an error of that size can make a difference.i
Given the size of the error associated with the models, it is possible that they might miss
important information. Evidence from a large body of literature suggests that this missing piece might be
information that becomes available during campaigns. Research has found campaign events to have an
effect on public opinion (Holbrook 1994, Shaw 1999). In a meta-analysis of the effects of viewing
presidential debates, Benoit, Hansen, and Verser (2003) found that watching debates affects vote
preference. Gordon and Hartmann (2013) analyzed television advertisements from the 2000 and 2004
campaigns. They found that the ads had a robust effect, capable of shifting electoral votes in multiple
states.
As a matter of fact, incumbents sometimes lose even in healthy economies. Therefore, campaigns
may very well matter, at least in some elections. Starting from this logic, Vavreck (2009) studied thirteen
presidential elections from 1952 to 2000 and found that candidates can increase their chance of winning
by effective campaigning. Simply put, when the economy is good, the incumbent should tell voters about
it; when the economy is bad, the incumbent should talk about something else. Vice versa, out-party
candidates should talk about the economy only in bad times. In healthy economies, out-party candidates
should emphasize non-economic issues that favor them.
These findings have led election forecasters to reconsider the minimal effects of campaigns.
Nadeau and Lewis-Beck (2012) calculated the impact of campaigns on the accuracy of the average in-
sample forecasts of six well-known political economy models from 1952 to 2000.ii This was done by
regressing the error of the model average on Vavreck’s classification of each campaign as pro-incumbent
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or pro-challenger. This approach lowered the error of the average model forecasts by 1.1 percentage
points; given the closeness of many elections, this is a considerable improvement in accuracy.
These results suggest that there might be value in developing models that draw on information
about campaigns. As well as offering potential improvements upon accuracy, such models could shed
light on the impact of campaigns on election outcomes, serve as a guide for party professionals in
selecting the best nominee, and help them decide which issues to emphasize in a campaign.
The present study develops a model based on three fundamental factors that shape vote choice in
U.S. Presidential elections: party identification, issues, and candidates. The model performs well
compared to traditional election forecasting models and has decision-making implications for those
involved in campaigns.
2 Traditional election forecasting models
Most of the established election forecasting models are political economy models. That is, they
include at least one measure of the state of the economy, usually accompanied by one or more political
measures. The generic specification of these models reads as:
Vote = f(politics, economics)
Since the late 1970s, political scientists have developed various versions of election forecasting
models. These models were used to test theories of voting, to estimate the effects of specific variables on
aggregate vote choice, and, of course, to predict election outcomes.
The models differ mainly in the selection of the economic variable(s). The dominant economic
variable used is economic growth, measured in terms of GDP (Abramowitz 2012, Campbell 2012) or
GNP (Lewis-Beck and Tien 2012). Other measures used include perceptions of personal income, either
retrospective (Holbrook 2012) or prospective (Lockerbie 2012), job growth (Lewis-Beck and Tien 2012),
or an index of leading economic indicators (Erikson and Wlezien 2012a). The choice of the economic
variables does not seem to be crucial, as the relationship among the different variables is quite robust.
By comparison, there is somewhat more agreement on the use of political variables, as many
models include a measure of presidential approval, measured at different points in time. Other popular
variables include whether or not the incumbent president is running and how long the incumbent party has
held the White House (Fair 2009, Abramowitz 2012). Finally, there are also models that deviate from the
classic political economy specification. For example, Hibbs (2012) focuses on economic factors, while
Norpoth and Bednarczuk (2012) use only political variables.
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It is beyond the scope of this paper to discuss the specifics of each individual model in detail. In
addition, most of the models have been revised more or less fundamentally over the past election cycles.
For a recent overview of the variables used in these and other models see Holbrook (2010). For a
comprehensive access to the literature on election forecasting models see Campbell and Garand (2000)
and Lewis-Beck (2005). In order to trace how the models have changed and performed over time, one
may want to consult the special issues of Political Methodologist 5(2), American Politics Research 24(4)
and PS: Political Science and Politics 34(1), 37(4), 41(4), and 45(4), which published forecasts of the
major models prior to each election since 1992.
2.1 Track record
Since the 1990s, forecasts of most established models have been regularly released around Labor
Day in the election year. The top section of Table 1 shows ex ante forecasts and corresponding errors of
eight models that were available for each of the five elections from 1996 to 2012.iii
The models’ track record in predicting the winner is impressive. Out of a total of fourty forecasts,
there were only four cases in which a model’s forecast was not on the right side of the 50% mark. In
2012, the models by Fair (2009) and Lewis-Beck and Tien (2012) predicted a victory of Romney. In
2008, the model by Campbell (2012) forecasted a two-party vote-share for McCain of 52.7%. In 2004, the
model by Lewis-Beck and Tien (2012) predicted a virtual tie in the popular vote, a forecast that turned out
to be the second most accurate of all eight models in that year.
As pointed out in the introduction, the adequate means for assessing the models’ accuracy is the
absolute error, which measures the percentage points by which the forecast missed the actual outcome.
Here, the picture is somewhat mixed. The models’ best year was 2012, when the average error was only
1.9 percentage points.iv In contrast, the models had their worst year in 2000. Although each model
correctly predicted Al Gore to win the popular vote, the average error was at its highest value (5.1
percentage points). Across all five elections, the average error was 3.1 percentage points. In comparison, a
naïve forecast of predicting that the votes are split equally among the two major parties (i.e., 50%) in each
of the past five elections would have yielded an error of 2.4 percentage points, which is 24% lower than
the average error of the forecasting models.v
There is no clear pattern of which model is most accurate. In each of the five elections, a different
model ranked first. Across all five elections, the model by Erikson and Wlezien (2012a) performed best,
closely followed by Abramowitz (2012).vi Of course, combining forecasts improves accuracy (Graefe et
al. 2013). The simple average of the eight models’ forecasts yielded a MAE of 2.3 percentage points,
which is 25% lower than the average error.
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2.2 Limitations
To be able to contribute to the substantive literature in election forecasting, it is necessary to
understand the limitations of existing models, which have been acknowledged by observers as well as the
model forecasters themselves.
2.2.1 Retrospective voting
Many models assume that voting is retrospective. This assumption is supported by a large body of
literature that demonstrates the importance of voter evaluations of the incumbent’s past performance (e.g.,
Fiorina 1981). However, several studies have shown that voters are also prospective in their evaluations
of candidates (e.g., Miller and Wattenberg 1985). Voters have different expectations of their personal, or
the nation’s, economic future were either candidate to win, and vote for the one under whom they expect
to be better off. Prospective evaluations might be even more important than retrospective ones, as people
might be more concerned with their future than with their past.
Most extant forecasting models lack an explicitly prospective component. An exception is
Lockerbie (2012), who uses a question from the Index of Consumer Sentiment that asks people whether
they think they will be better off financially, worse off, or about the same, in a year from now. While this
is clearly a prospective measure, it does not directly link the perceived economic conditions to the
responsibility of the government.vii Others incorporate trial-heat polls, which can be expected to also
capture prospective evaluations of candidate performance (Erikson and Wlezien 2012a, Campbell 2012).
Probably the main reason why many models focus solely on retrospective measures of the state of
the economy (such as GDP growth) or the general performance of the government (such as the
incumbent’s popularity) is that these figures are easy to obtain, especially for historical elections. In
addition, this decision is backed by the reasonable assumption that any prospective evaluation is affected
by retrospective evaluations. If the economy has been doing well, voters might take this as a positive sign
about their future well-being under the same government. That said, it appears promising to develop
forecasting models that incorporate prospective measures.
2.2.2 Incumbent centricity
Due to their retrospective nature, many established models imply that vote choice is based on
evaluations of the incumbent’s performance. Since most models include the incumbent popularity
measure, voters are essentially assumed to ignore the candidate of the challenging party when deciding
for whom to vote. This assumption might be reasonable when the incumbent is running. But it is harder to
justify in open-seat elections, in which people have been found to vote less retrospectively (e.g., Nadeau
and Lewis-Beck 2001, Miller and Wattenberg 1985). Campbell (2008) shows that the incumbent
popularity measure explains only 21% of the vote variance in open seat elections, compared to 67% in
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races with the incumbent running. Similarly, Holbrook (2010) finds that the impact of the retrospective
variables used in his model (i.e., personal finances and incumbent popularity) is larger in incumbent races
than in open-seat elections.
2.2.3 Measurement error
The state of the economy is difficult to measure. Often, there is a large variance between the
initial and the revised estimate. For example, on January 30, 2009, the Bureau of Economic Analysis at
the U.S. Department of Commerce initially estimated a real GDP decrease of 3.8 percent for the fourth
quarter of 2008. One month later, the figure was revised to 6.2 percent, and, at the time of writing, the
latest estimate showed a decrease of 8.9 percent. Revisions of this size are not exceptional. Runkle (1998)
analyzed deviations between initial and revised estimates of quarterly GDP growth from 1961 to 1996.
Revisions were common. The figures revealed upward revisions by as much as 7.5 percentage points and
downward revisions by as much as 6.2 percentage points.
It is clear that such measurement errors can have a large impact on the accuracy of forecasting
models. As noted by Holbrook (1996, 509), “forecasting models that rely on second- or third-quarter
economic data may not be able to forecast the election at all, at least not with reliable data.”
2.2.4 Perceptions of the economy and political context
Models that include structural economic variables such as GDP growth assume that voters can
accurately observe changes in the state of the economy and can infer how these changes affect their future
well-being. This is a challenging assumption for several reasons. Initial estimates of economic figures
often vary widely from actual figures (see above), the media might not always accurately inform the
public about the state of the economy (Goidel and Langley 1995, Hetherington 1996), and most people
are badly informed about economic conditions (Holbrook and Garand 1996).
In addition, the political interpretation of objective economic measures may vary over time. For
example, a GDP growth rate of three percent may appear prosperous in a sluggish economy, in which
people’s expectations are generally low. By comparison, the same growth rate may seem less impressive
during a booming economy. Political economy models cannot account for the variances in the meaning of
economic variables depending on the political context.
2.2.5 Non-economic issues
The economy is not the only issue of concern to voters. Depending on the context of a specific
election, many other issues influence the vote decision and often voters consider them as more important
than the state of the economy. In a cross-national study of 39 elections, Singer (2011) finds that economic
issues were more important than other issues during recessions or volatile times. However, the economy
was less in focus in times when other government crises exist, such as terrorist attacks. Lewis-Beck and
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Rice (1992, 29) summarize Gallup data from polls that ask voters to name the most important problem
facing the country. In 29 of the 42 years (i.e., 69%) from 1948 to 1989, non-economic issues (in particular
foreign policy concerns) made the top of the list.
As with measuring the state of the economy, estimating the impact of non-economic issues on the
election outcome is difficult. The importance of issues varies within and between elections. New issues
arise and others become irrelevant. In addition, the small number of observations that are usually
available for election forecasting prevent forecasters from adding additional variables to their regression
models. Forecasters are thus confined to using the broad measure of incumbent popularity as a shortcut.
The underlying assumption is that this measure is a global indicator for the president’s personality or his
performance to handle issues. In other words, incumbent popularity is endogenous to the campaign,
which might be the reason why the variable has been identified as the single best predictor for forecasting
U.S. presidential elections (Lewis-Beck and Rice 1992).
However, the incumbent popularity measure has its limitations. First, as discussed earlier, the
measure is retrospective in nature and thus centered around the incumbent. Second, the measure does not
explain the why. Why is it that an incumbent is unpopular? Is it because of his ability to handle the issues,
his personality, a festering scandal, or some other factor? This is of course no concern if the sole purpose
of the model is to forecast. However, models that are based on incumbent popularity cannot provide a
decision aid to those involved in political campaigns (see Section 2.2.6). Third, in serving as a proxy for
issue handling competence, incumbent popularity also includes the public’s perceptions of how the
president is handling the economy. Ostrom and Simon (1985) found that incumbent performance is a
function of both economic and non-economic factors. After showing that GNP growth is correlated with
incumbent performance (r = .48), Lewis-Beck and Rice (1992, 46) note that “ideally, we would like to
take the strictly economic component out of the popularity measure, thus leaving an altered measure of
popularity that varied only with noneconomic issues”.viii
2.2.6 Decision-making implications
None of the established models incorporates variables that measure the quality of campaigns.
This is not to say that campaigns do not matter. Most model forecasters emphasize their importance, since
campaigns inform voters about the candidates’ policy positions and thus “enlighten” them to their true
preferences (Gelman and King 1993). To some extent, campaigns are assumed to assure that election
forecasting models work (Erikson and Wlezien 2012b). Since the state of the economy is an important
issue in most elections, the campaign directs voters’ attention to economic issues, which are measured in
terms of other economic indicators such as GDP growth.
Even if campaign variables might not be necessary to accurately forecast election winners, there
is a downside to foregoing them. There is not much that candidates, parties, and campaign strategists can
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learn from political economy models other than that incumbents benefit if the economy is doing well, and
pay the cost in votes if the economy is doing poorly. Political economy models cannot provide advice on
questions such as who to nominate, which issues to emphasize, or which policies to pursue.ix
3 The Issues and Leaders model
In an effort to address some of these limitations, the present study departs from the traditional
work on political economy models and develops a model that is based on a different theory and different
data.
3.1 Voting theory and model specification
Three leading factors are known to shape the vote decision in U.S. presidential elections: party
identification, issues, and candidates (Asher 1992, 200). This theory of voting has emerged from a vast
body of literature on individual voting behavior, most prominently The American Voter by Campbell et
al. (1960). Party identification, issues, and candidate evaluations are also the three principal explanatory
variables in the valence politics model (Clarke et al. 2011). A generic vote equation can be formulated as:
Vote = f(issues, candidates, party identification)
This is not the first study that aims at developing a forecasting model based on this theory. Lewis-
Beck and Rice (1992, 51-55) extended their political economy model, which used GNP growth and
incumbent popularity, by two variables to account for party identification and candidate appeal. Party
identification was measured as the performance of the incumbent party in midterm House elections. The
performance of the incumbent in the primaries was used as a measure of candidate appeal. While the
model fit existing data well, it lasted only one election; it was abandoned after it failed to predict George
H. W. Bush’s defeat in 1992. Lewis-Beck and Tien (1996) argued that the model suffered from
specification error as it included irrelevant variables.
3.1.1 Issues
Voters favor candidates that share their views on important issues. Campbell et al. (1960, 170)
proposed three simple but necessary conditions for an issue to influence vote choice: (1) the voter is
aware of the issue, (2) the issue is of some importance to him, and (3) he expects one party to do a better
job in handling the issue than the other parties. Only if these conditions are met, is an issue said to have
an impact on vote choice that goes beyond party identification.
In an attempt to meet these conditions, results from two types of polls were combined to obtain
how voters perceive the candidates’ relative issue-handling competence.
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Issue-salience polls. The string [“most important problem” OR “most important issue”] was used
to search all Gallup polls stored in the iPoll databank of the Roper Center for Public Opinion Research.x
This resulted in a total of 270 relevant polls for the eleven elections from 1972 to 2012. Each issue was
assigned to one of three categories: economic, foreign, and other.xi For each poll, the percentage of issues
named per category was calculated.xii The result is the issue salience score St,c per category c at day t. If
no new poll was released on a certain day, the value from the previous day was used.
Issue-handling polls. The string “[Republican candidate] AND [Democratic candidate] AND
(issue OR problem)” was used to search the iPoll databank of the Roper Center for Public Opinion
Research for polls on the relative issue-handling competence of the candidates. This resulted in 5,671
questions for the eleven elections from 1972 to 2012, in which voters were asked to assess the candidates’
relative issue-handling competence. As with issue importance, each issue was assigned to one of three
categories: economic, foreign, and other. For each issue category, the two-party voter support for the
candidate of the incumbent party was calculated across all issues in that category. If more than one poll
was released on the same day, the average of these polls was used for that day. The resulting score is the
incumbent party’s candidate issue handling competence score Ct,c for category c at day t.
Simple exponential smoothing was used to combine issue handling competence scores over time
in a particular election year. Exponential smoothing is a common procedure in forecasting for
extrapolating time-series data. The underlying idea is to weight the most recent data most heavily; it can
be formulated as:
Ct,c=
α
Ct,c+(1
α
)Ct1,c,
where Ct,c represents the latest issue handling competence score for category c at time t, and
C
t1,c
represents the smoothed average of the issue competence series for category c at t-1, which was
calculated the day the most recent poll was released. The factor α determines how much weight to assign
to the most recent issue handling competence score: the higher the factor, the heavier the weight. The
Issues and Leaders model uses an α of 0.7, which means that 70% of the new average come from the
latest issue handling competence score, and the other 30% come from the previous average. Thereby, the
weight assigned to previous issue salience scores drops of geometrically. If no new poll was released on a
certain day, the smoothed average from the previous day was used. The smoothed issue handling
competence scores at day t for category c are referred to as Ct,c.
This procedure of averaging and smoothing polls was expected to reduce measurement error and
thus to increase accuracy as it moderates the impact of single polls.
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Issue score calculation. For each day, the weighted averages of the daily scores of issue-salience
and issue-handling competence were calculated across the three categories. The result is the issues score It
used in the vote equation of the model:
It=St,cCt,c
c
Table 2 shows the number of polls used and the resulting issue scores for the candidate of the
incumbent party, calculated on Election Eve for each of the eleven presidential elections (1972 to 2012).
In each of the eleven elections, the final issue score on Election Eve correctly predicted the popular vote
winner.xiii
3.1.2 Candidates
While the importance of issues on vote choice has varied across elections, candidate evaluations
have always been an important factor for vote choice. Some researchers would argue that elections are
choices between candidates and that candidate evaluations influence party identification and perceived
issue-handling competence (e.g., Asher 1992). Miller, Wattenberg, and Malanchuk (1986) found that
voters focus predominantly on candidates’ personality traits rather than on issues or party affiliation. The
authors focused on five categories of candidate characteristics: competence, integrity, reliability,
charisma, and personal qualities. Bartels (2002) analyzed the impact of candidates’ personalities on the
outcomes of U.S. presidential elections. Across the six elections from 1980 to 2000, he estimated that the
average net effect of candidate traits on the vote was about 1.6 percentage points.
One factor that has often been studied along with candidate evaluations is leadership. In
analyzing the impact of several personality traits on candidate evaluations for the three presidential
elections in 1984, 1988, and 1992, Funk (1999) found leadership quality to be an important factor. Bartels
(2002) found that, out of five personality traits, leadership had the second strongest influence on
individual vote choice. Pillai and Williams (1998) identified positive effects of leadership perceptions of
candidates on both intent to vote and voting behavior. These effects remained stable after accounting for
the influence of party identification. Finally, leadership evaluations are a core variable in the valence
politics model (Clarke et al. 2011).
Therefore, voters’ perceptions of candidates’ leadership quality were used as a variable in the
model. The string “[Republican candidate] AND [Democratic candidate] AND leader” was used to search
the iPoll databank of the Roper Center for Public Opinion Research for polls that reveal information on
the relative leadership quality of the candidates. This resulted in a total of 158 relevant polls from 1972 to
2012.xiv For the three elections in 1972, 1976, and 1992, no leadership poll was released until 71, 37, and
8 days prior to Election Day. In these cases, it was assumed that the two candidates do not differ in their
perceived leadership skills. That is, the incumbent was assigned a leadership score of 50%. For each poll,
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the two-party share of voters that favored the candidate of the incumbent party was calculated. If more
than one poll was released on the same day, the average of these polls was used for that day. As with
issue polls, simple exponential smoothing was used to combine polls over time in a particular election
year (α = 0.7). If no new poll was released on a certain day, the smoothed average from the previous day
was used. The result is incumbent party’s candidate leadership score Lt at day t.
Table 2 shows the number of polls used and the resulting leadership scores for the candidate of
the incumbent party, calculated on Election Eve of each of the eleven elections from 1972 to 2012. The
leadership score correctly predicts the popular vote winner in eight of the eleven elections. It was wrong
in 1976 and 1992, when the incumbent presidents Gerald Ford and George H. W. Bush were perceived as
stronger leaders than the young challengers Jimmy Carter and Bill Clinton. In 2000, the closest election in
the sample, there were no differences in the perceived leadership qualities of George W. Bush and Al
Gore.
3.1.3 Party identification
Party identification is the psychological attachment (or loyalty) to a political party. This measure
plays a central role in studies of U.S. presidential elections and is commonly regarded as a long-term,
stable, and powerful predictor of individual vote choice (Asher 1992, Bartels 2000). In addition, party
identification has been found to strongly influence issue and candidate evaluations.
According to the theory of issue ownership (Petrocik 1996), a party “owns” an issue if the public
consistently perceives the party to be better at handling the issue. Democrats have an advantage on issues
related to social welfare and intergroup relations, whereas voters tend to favor Republicans on issues
associated with taxes, spending, and the size of government (Petrocik, Benoit, and Hansen 2003). Issues
that are not tied to party constituency are referred to as performance issues. Evaluations of performance
issues are retrospective as they depend on the record of the incumbent. Thus, performance issues have an
impact on vote choice that goes beyond party identification. For example, in times of high unemployment,
challengers can gain performance-based ownership of economic issues, since the weak economy
demonstrates the incumbent’s incapability to handle the job.
In studying voters’ perceptions of the personalities of the major candidates for the six U.S.
presidential elections from 1980 to 2000, Bartels (2002) found that candidate evaluations are strongly
influenced by party identification. Voters tend to evaluate the personality traits of their party’s candidate
more positively, and assign more negative rating to the candidate of the opposing party.
Thus, the predictor variables used already capture part of the effect of party identification on vote
choice. What remains are voters who do not pay attention to politics and thus would never change their
party identification. These people will vote for the candidate of the party they feel attached to, regardless
of the candidate’s personality or issue positions (Lewis-Beck et al. 2008, 121). It is therefore assumed that
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the share of the incumbent’s vote that is directly linked to party identification is captured by the intercept
of the vote equation of a multiple linear regression model.xv
3.2 Model specification
The specification of the model developed in the present study, hereafter referred to as the “Issues
and Leaders” model, reads as:
Vt = P + b It + c Lt
where It the represents the issues score and Lt the leadership score, measured t days before
Election Day. The intercept P reflects party identification. The dependent variable Vt refers to the
incumbent’s actual share of the two-party popular vote.xvi
3.3 Model estimation and fit
The forecast starting point began 100 days prior to Election Day and was moved forward one day
at a time until Election Eve. The vote equation was estimated for each of the 100 days in the forecast
horizon across the eleven elections from 1972 to 2012. That is, the resulting vote equation changes
whenever new polls become available in an election year. Needless to say, with only few elections to
analyze, any statistical results remain tentative. On the first day of the forecast horizon (i.e., day 100
before Election Day, which is around the end of July), the model equation reads as:
V100 = 26.5 + 27.6 I100 + 23.2 L100. R2= .30; SSE = 5.0
(1.8) (1.1) (1.5) (t-values in parentheses)
That is, the model predicts the incumbent to start out with 26.5% of the two-party vote, which is
the share of voters that decide solely based on party identification. In addition, the incumbent can gain
votes depending on his perceived issue-handling competence and leadership quality. If both candidates
are perceived as equal (i.e., issues = candidates = 50%), the model predicts the incumbent to gain 51.9%
of the vote. An increase in his issue-handling competence of 10 percentage points would increase the
incumbent’s vote-share by 2.8 percentage points. An increase in leadership quality of 10 percentage
points would increase his vote-share by 2.3 percentage points.
On the last day in the forecast horizon (Election Eve), the model equation reads as:
V1 = 9.6 + 49.6 I1 + 30.7 L1. R2= .97; SSE = 1.0
(3.9) (9.9) (7.0) (t-values in parentheses)
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The intercept is almost 17 percentage points lower than in the previous equation, which suggests
that fewer people cast their vote based on party identification and nothing else. As the campaign develops,
the importance of issues increases; the coefficient of the issues variable is more than 20 points higher. An
increase in issue-handling competence on Election Eve by ten percentage points increases the
incumbent’s vote share by 5.0 percentage points. In comparison, the coefficient of the leadership variable
remains more stable and increases by only 7.5 points. In addition, as shown with the black line in Figure
1, the model’s fit increases substantially over the course of the campaign.
3.4 Predictive performance
However, model fit is not necessarily related to forecast accuracy. The hardest accuracy test is
how well the model forecasts prospectively (that is, for years not included in the estimation sample), and
compared to benchmark forecasts. The bottom section of Table 1 reports the errors of quasi ex ante
forecasts of the Issues and Leaders model for the five elections from 1996 to 2012. Since these
predictions were not issued at the time of each particular election, they cannot be considered true
“forecasts”. However, these forecasts were calculated using only data that would have been available at
the time and thus provide the most realistic estimate.xvii For instance, to predict the 2012 election, data on
the ten elections from 1972 to 2008 were used, for the 2008 election, data on the nine elections from 1972
to 2004 were used, and so on. Thus, when predicting the 1996 elections, only six data points were
available. This procedure of simulating ex ante forecasts, also known as ‘successive updating’ or ‘step-
ahead’ method, is a standard practice for evaluating the accuracy of forecast models after the fact (Lewis-
Beck 2005).
To compare the model’s accuracy to established models, it is necessary to define a certain lead
time for when the forecasts are generated. Most models publish their forecasts around Labor Day, about
eight to nine weeks prior to Election Day. In an effort to make the forecasts comparable, Table 1 shows
the average forecasts of the Issues and Leaders model, calculated over the period from 90 to 60 days prior
to Election Day. In addition, Table 1 shows the model’s Election Eve forecast.xviii
Across all five elections, the early September forecasts of the Issues und Leader model yielded a
MAE of 2.9 percentage points, which makes it rank fourth in terms of accuracy after Erikson and Wlezien
(2012a), Abramowitz (2012), and Lewis-Beck and Tien (2012). In addition, the model’s error is lower
than the error that one would have achieved if one had randomly picked a model (3.1 percentage points).
This was achieved despite the model’s poor performance in the 1996 election, for which only six
elections (and generally fewer polls for these elections) were available to calibrate the model.xix
Such one-off comparisons conceal an important advantage of the Issues and Leaders model,
which is the ability to track campaigns. The model fit suggested that the accuracy of the model increases
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over the course of the campaign. This is confirmed by the grey line in Figure 1, which reflects the MAE
of the ex ante forecasts from 1996 to 2012, calculated as one-week moving averages.
Across the past five elections, the model’s Election Eve forecasts missed the election outcome by,
on average, 1.1 percentage points (cf. Table 1). By comparison, the corresponding error of the final
Gallup pre-election poll was 30% higher, at 1.6 percentage points. That is, the Issues and Leaders model
provided better forecasts than directly asking people for whom they intend to vote.
4 Discussion
The Issues and Leaders model departs from the theory underlying most traditional models,
according to which an election is a referendum on the incumbent’s performance. In contrast, the model is
built on a vast literature on individual voting behavior, which found that Americans elect their president
based on three fundamental factors: party identification, issues, and candidates. In addition, the Issues and
Leaders model differs from most established models in that it uses no economic indicators as predictor
variables but relies solely on polling data.
In building on a different theoretical foundation and different data, the Issues and Leader model
addresses some of the limitations of established models. In particular, the poll data included in the model
(1) cover both retrospective and prospective aspects of voting, (2) evaluate the relative performance of
candidates instead of being centered on the incumbent, (3) directly measure people’s perceptions instead
of taking, for example, the detour of using other indicators such as structural economic data, (4) directly
measure non-economic issues instead of taking the shortcut of presidential approval ratings, and (5)
account for the relative importance of issues as seen by voters across specific elections. For example,
economic issues are likely to have more weight during recessions or times of poor economic performance,
whereas during an international crisis or threats of national security, the importance of foreign issues will
increase. Although the long-term forecasts of the Issues and Leaders model did not necessarily
outperform established models, they provide different information and thus should contribute to the
accuracy of a combined forecast (Graefe et al. 2013). Thus, the Issues and Leader model should be
regarded as a supplement rather than an alternative to existing models when it comes making forecasts
early in the election.
4.1 Issues and Leaders v. political economy models
Shortly before Election Day, the Issues and Leaders model provided highly accurate predictions,
with Election Eve forecasts that outperformed the final Gallup pre-election poll. This suggests that the
performance of the model increases as the election nears. The ability to update the forecast as new
information on issues and candidates is gained is thus an advantage of the Issues and Leaders model.
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The question that one might ask is to what extent the Issues and Leaders model encompasses
information from other forecast models and, if so, how this changes over the course of the campaign? In
order to test the extent to which one forecast encompasses information contained in another forecast, one
can conduct the simple regression analysis (Chong and Hendry 1986):
V=
α
1
ˆ
V
1
+
α
2
ˆ
V
2
+
ε
,
where V is the actual vote-share of the candidate of the incumbent party,
ˆ
V
1
and
ˆ
V
2
are the
respective forecasts of models 1 and 2, and ε is the error term. The results of such an encompassing
regression should be interpreted as follows: if the regression coefficients α1 and α2 are both different from
zero, both models contain independent information. Now assume that α1 is nonzero and α2 is zero. In
such a situation, both models contain information but the information in model 2 is completely contained
in model 1 and model 1 contains further relevant information.
Figure 2 shows the model coefficients resulting from these regressions when doing pair-wise
comparisons of the in-sample forecasts of sevenxx of the eight established models listed in Table 1 and the
Issues and Leaders model.xxi An encompassing regression was conducted for each of the 70 days prior to
Election Day, across the eleven elections from 1972 to 2012.xxii For observants of statistical significance,
dotted data points in all figures visualize coefficients that are twice as large as their standard error (i.e.,
the t-statistic).
The general pattern is similar for most comparisons. The established models dominate until about
six weeks prior to Election Day. In most cases, they have a much larger weight in the encompassing
regression than the Issues and Leaders model. This suggests that the Issues and Leaders model provides
little or no unique information early in the campaign. Around six weeks prior Election Day, the picture
reverses. In the last one and a half months prior to Election Day, the Issues and Leaders model contains
the information embodied in most benchmark models (i.e., the pre-campaign fundamentals) as well as
further relevant information (e.g., the specific context of each election).
4.2 Impact of campaigns
The increase in model fit and predictive accuracy towards Election Day suggests that information
that becomes available during campaigns matters for the election outcome.
4.2.1 Relative influence of party identification, issues, and candidates over time
Figure 3 shows the development of one-week moving averages of the model coefficients (i.e.,
issues, leadership, and party identification) for explaining the vote over the last 100 days prior to Election
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Day. In general, the influence of the party identification constant decreases towards Election Day, the
importance of issues increases, and the weight of leadership remains relatively stable.
One possible explanation for this development is the nature of the three predictors. Party
identification leads people to vote for their party’s candidate. It can influence the vote decision directly or
indirectly. People who are directly influenced by party identification ignore all other information such as
issues and candidates when deciding for whom to vote. Thus, party identification should be particularly
valuable as a long-term predictor before the start of the campaign, when little is known about the
candidates, and even less about their stands on the issues. In such a situation, voters have little choice but
to decide solely based on party identification. Candidate evaluations are a medium-term predictor, as
voters possess much information about candidates before the start of the hot phase of the campaign.
Voters know virtually everything about the incumbent, and the nomination process of the parties has
revealed insights about the candidates’ experience and personality. Therefore, it is unlikely that much new
information on the candidates’ leadership qualities will be revealed as the campaign evolves. In contrast,
due to the initial focus on candidates, it may be more difficult for voters to learn about the issues early in
the campaign. But, as the election nears, the campaign increasingly draws people’s attention to the issues
and allows voters to focus on substance. As a result, the importance of voter perceptions of candidates
issue-handling competence as a predictor of the election outcome increases towards the end of the
campaign. Consequently, the influence of party identification on aggregate vote choice fades.
It is clear that the model’s macro view does not allow for drawing causal inferences on why
voters perceive the candidates as they do. It might be that a candidate is able to convince some voters of
his ability to better handle a certain issue (or to be a better leader) than his opponent through successful
campaigning. At the same time, another group of voters might solely be influenced by partisanship. Such
voters would simply align their perceptions of issues and candidates with their party identification. The
aggregate perceptions then arise from a mix of different strategies that depend on various factors such as
the strength of partisanship or the type of issue (e.g., whether an issue is linked to party constituency or
performance, see Section 4.3.1). The Issues and Leaders model cannot provide insight into what drives
voters’ perceptions. But it does demonstrate that these perceptions matter for the election outcome, no
matter how they are created. As a result, the model can provide insights into what happens over the course
of the campaign.
4.2.2 Conventions and debates
There are two key events that achieve a particular high level of attention in a campaign: the party
conventions and the presidential debates. The conventions are usually held shortly before or after Labor
Day and the presidential debates start around six weeks prior to the Election. The massive media coverage
of these events is accompanied by a large number of released polls.
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As shown in Figure 1, model fit and predictive performance of the Issues and Leaders model
somewhat decreases around the time of the conventions. A possible explanation for this result is the bump
that candidates usually get from their conventions. Conventions tend to unite the party, create favorable
media coverage, and thus increase people’s enthusiasm for their party and candidate (Campbell, Cherry,
and Wink 1992). As a result, polls might have difficulties to accurately measure issue perceptions around
conventions, since people might be strongly influenced by party identification. To some extent, the model
reflects this. Figure 3 reveals a small convention bump; the party identification constant and the
leadership coefficient increase, whereas the issues coefficient decreases during that time period.
The performance of the model then improves with a sharp increase in the influence of issues,
starting around the time when the presidential debates usually begin (cf. Figure 3). Thereafter, the
influence of issues decreases somewhat but remains at a high level. Findings from the meta-analysis by
Benoit, Hansen, and Verser (2003) help to explain the sudden increase in the importance of issues. Their
results show that watching debates increased people’s knowledge and salience of issues, influenced
candidate evaluations of issue-handling competence, and impacted agenda-setting (i.e., the issues that
voters regard as important). By comparison, debates had only small effects on evaluations of candidates’
leadership quality. The Issues and Leaders model supports these findings. The increase in the weight of
the issues coefficient suggests that debates can alter public opinion on issues. On the contrary, the
leadership coefficient remains relatively stable, which conforms to the finding that debates have little
effect on leadership perceptions.
4.2.3 Campaign effects over time
In addition to analyzing how the model coefficients change over the course of the campaign, one
might be interested in how the coefficients changed across past elections. Figure 4 shows how the model
coefficients developed across the eight elections from 1984 to 2012. These coefficients were estimated
using data up to and including each particular election. For example, when estimating the coefficients for
the 1984 election, only the four observations from 1972 to 1984 were used. When estimating the
coefficients for the 2000 election, data from 1972 to 2000 were used, and so on.
The model coefficients are remarkably robust, even for early elections, when estimated based on
very limited data.xxiii The importance of the party identification constant remained basically unchanged
over time. The only change that can be observed is a slight increase in the importance of candidate
perceptions, at the cost of issues, from 1984 to 1996. Since then, all three coefficients are stable.
These results suggest that the model is robust to the specifics of certain elections. Or, to put it
differently, the issue and leadership variables do a good job in capturing the electoral context. Even a
major crisis such as the financial meltdown two months before the 2008 election does not seem to impact
the model. This conforms to the results of the encompassing regression, which showed that the model
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contains all of the pre-campaign information included the established model, as well as other relevant
information. The party identification constant remains stable across all eight elections in the sample,
which suggests that the election outcome is largely driven by people’s perceptions of candidates and
issues (which, again, might in part be driven by party identification and pre-campaign fundamentals). In
addition, the model suggests that the relative importance of candidates over issues increased during the
1980s and into the mid-1990s, a finding that conforms to the literature on candidate-centered politics
(Wattenberg 1991).
4.3 Model validity
These explanations are, of course, speculations based on aggregated data and few observations.
One should generally be careful with drawing definite conclusions from macro models such as the Issues
and Leaders model. Future research should further investigate these and other questions using the Issues
and Leader model along with micro-level data on the effects of campaigns on individual voting behavior.
Another possible constraint of the model’s validity is its reliance on polling data. As with any
measure of public opinion, voters’ perceptions of issues and candidates are subject to measurement error
associated with sample size, interviewer instructions, and the order and phrasing of questions, etc. In
order to limit potential biases introduced by a single poll, the model therefore combines information from
a large number of surveys (Graefe et al. 2013).
But there are good reasons to have some confidence in the results. First, the model is based on an
established theory that emerged from a vast literature of individual voting behavior. Second, the Issues
and Leaders model provides accurate forecasts. Early in the campaign, the model’s accuracy is
comparable to those from established models. As the election nears, accuracy increases; the model’s
Election Eve forecasts were more accurate than the final Gallup pre-election poll. Third, encompassing
regression showed that, from the time of the presidential debates, the Issues and Leaders model includes
the pre-campaign fundamentals covered by the established election forecasting models, as well as other
relevant information such as the specific context of a particular election. Finally, the Issues and Leader
provides empirical macro-level support for various findings from other studies using different data and a
different approach. In particular, the model supports previous findings such as (i) issues gain importance
as the election nears, (ii) conventions generate a bump in party identification, (iii) debates affect issue
importance and issue (but not leadership) evaluations, and (iv) the importance of candidates has increased
in the 1980s.
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4.4 Decision-making implications
Established election forecasting models usually publish their forecasts before the start of the
general election campaign. While a long lead time is a desirable feature of forecasting models (Lewis-
Beck 2005), these models cannot provide advice to campaign strategists and political observers.
The Issues and Leader model relies on public opinion of candidates’ issue-handling competence
and leadership quality. In recent elections, polls that reveal such information have become frequently
available during the course of a campaign. The model can thus provide advice to those that are involved
with, or observe, campaigns.
4.4.1 Implications for campaign strategists
The model suggests three levers for candidates to increase their share of the popular vote. First,
candidates should aim at increasing their issue-handling reputation. The most straightforward way to
achieve this is to gain ownership of an issue. That is, candidates should try to convince voters that they
are better at solving a particular problem than their opponent. However, Petrocik (1996) suggests limited
success of this strategy for issues that are linked to party constituency. The reason is that ownership for
such issues rarely changes. However, the strategy might be beneficial for performance issues, which are
not tied to party constituency but depend on the context of a particular election. But gaining issue-
ownership is not necessary to increase issue-handling reputation. For issues that favor the opponent,
candidates can aim at reducing the perceived difference on issue-handling competence. For issues that
favor themselves, candidates should try to further increase the difference with their opponent.
Second, the model implies that issues that are seen as more important by voters have a larger
impact on the election outcome. Therefore, candidates should engage in agenda setting. That is,
candidates should aim at directing public attention to issues for which voters favor them and should frame
issues that favor the opponent as less important. Thereby, issues for which public opinion is one-sided are
preferable. Agenda-setting also involves competition over media coverage, since the media plays an
important role in influencing which issues are important to voters and how voters evaluate the issues
(McCombs and Shaw 1972, Hetherington 1996).
Third, parties should support candidates that are likely to be perceived as strong leaders. Several
factors can influence perceptions of leadership. This includes factors that are inherent to a candidate’s
biography (such as age, education, professional experience) and appearance (such as attractiveness, facial
competence, height). Since most of these factors are difficult to manipulate during a campaign, parties are
advised to take this into account when nominating candidates (Armstrong and Graefe 2011). In addition
to socio-demographic factors, meta-analyses have found a strong link between personality and leadership
(Lord, De Vader, and Alliger 1986, Judge et al. 2002). Personality traits that are positively correlated with
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both leader emergence and performance are extraversion (e.g., sociability, assertiveness), openness (e.g.,
curious, imaginative), agreeableness (e.g., trustful, compassionate), and conscientiousness (e.g.,
dependability, self-confidence). In contrast, neuroticism (e.g., anxious, depressive) hurts leadership
evaluations. For an overview of predictors of leadership see Antonakis (2011).
4.4.2 Implications for observers
Journalists and political commentators need to meet the demands of the news cycle, which asks
for a constant flow of interesting stories and analysis. In this endeavor, they often select stories based on
their level of newsworthiness (such as gaffes by candidates) rather than their relevance. In particular,
journalists increasingly treat elections as horse-races and inform the public about who is ahead in the
polls, without providing explanations for the relative performance of candidates (Patterson 2005,
Rosenstiel 2005).
The Issues and Leaders model provides a tool for tracking campaigns. It can be used to assess the
impact of campaign events on the public’s perceptions of the candidates’ issue-handling competence as
well as their leadership quality. In addition, it can be used to assess the effect of such changes in public
opinion on the predicted vote share. Therefore, the model is useful for political observers and journalists
as well as researchers in the fields of communication studies and public opinion research.
5 Concluding remarks
The Issues and Leaders model is built on three major determinants of individual vote choice:
party identification, issues, and candidates. The model uses public opinion data from polls that ask voters
about the importance of issues, which candidate they think can better handle the issues, and which
candidate is the stronger leader. For the past four elections, the model’s ex ante forecasts, calculated three
to two months prior to Election Day, were competitive with those from the best of eight political
economy models. Model accuracy substantially improved over the course of the campaign, when the
model captures information on the pre-campaign fundamentals as well as the specific electoral context.
The Election Eve forecasts of the model were more accurate than the final Gallup pre-election poll.
The results suggest that information that becomes available during campaigns matter for the
outcome of U.S. presidential elections. The direct influence of party identification was found to decrease
over the course of the campaign, whereas issues gain importance, especially during the presidential
debates. On Election Eve, issue evaluations are the most important factor for predicting the election
result. The model has decision-making implications for campaign strategists. Candidates should engage in
agenda setting and should try to increase their perceived issue-handling and leadership competence in
order to gain votes.
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Footnotes
i Over the sixteen elections post World War Two, the mean absolute deviation of the incumbent’s two-party vote
share from the 50% mark was 4.4 percentage points.
ii The six models were the models by Abramowitz (2012), Campbell (2012), Erikson and Wlezien (2012a),
Holbrook (2012), Lewis-Beck and Tien (2012), and Norpoth and Bednarczuk (2012).
iii Most forecasts were derived from the special issues of American Politics Research, 24(4) and PS: Political
Science & Politics, 34(1), 37(4), 41(4), and 45(4). The forecasts from Ray Fair’s model were obtained from his
website fairmodel.econ.yale.edu.
iv The average error is the error that one can expect if one would randomly pick one of the eight models.
v Of course, such a naïve forecast is useless if one is interested in who will win.
vi Although probably most important, accuracy is not the only criteria for assessing the quality of a forecasting
model. Lewis-Beck (2005) provides guidance for how to evaluate forecasting models across four dimensions:
accuracy, reproducibility, parsimony, and lead time.
vii As Lockerbie (2000) points out, the missing connection between the government’s actions and the economy might
be a problem as this is an important component in studies of voting behavior.
viii An alternative to using incumbent popularity is to create an index of non-economic issues. Although not
differentiating between economic and non-economic issues, Graefe and Armstrong (2012) use the index method to
simply count the number of issues for which voters favor each candidate. Then, they use the incumbent’s final score
as the single predictor variable in a linear regression model.
ix An exception is the fiscal model, which posits that incumbents who restrain the growth of spending regularly
retain the White House while those who do not tend to lose it (Cuzán and Bundrick 2008).
x All data analyzed in the present study are available upon request.
xi Economic issues include issues such as the state of the economy, jobs, or trade. Foreign issues include issues such
as foreign policy, Iraq, or terrorism. Other issues include issues such as health care, social security, or abortion.
Appendix I in the supporting online material shows the full classification of issues to categories.
xii All cases in which less than 1% of respondents mentioned a particular issue, or in which people did not provide an
answer, were excluded.
xiii The issues score differs in several ways from the “issue index” developed by Graefe and Armstrong (2012). For
each issue, their “issue index” assigns a score of 1 to the candidate that voters expect to better handle the issue. The
candidate with the higher overall score is then predicted to win the election. The “issue-index” has been criticized as
it considers all issues as equally important and does not account for the magnitude of voter support on any one issue.
That is, the index assigns the same weight to issues such as the war in Iraq or smog in American cities. Also, each
issue’s impact on the vote is the same, regardless whether a candidate is favored by two (51 vs. 49) or twenty
percentage points (60 vs. 40).
In including issue-salience polls, the issues measure developed in the present study accounts for the relative
importance of issues within and across elections, and captures the magnitude of voter support on each issue. This
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procedure leads to substantial gains in accuracy. A simple linear regression of the incumbent’s “issue index” score,
published in Graefe and Armstrong (2012), on the actual two-party vote shares over the ten elections from 1972 to
2008 yielded in-sample forecasts that missed the election results on average by 2.8 percentage points. By
comparison, using the issue score developed in the present study as the explanatory variable, the average error of the
in-sample forecasts was 2.0 percentage points. This refers to an error reduction of 28%.
xiv For the 1972 election, no leadership polls were available at iPoll. In this case, results from a Newsweek poll,
conducted at August 28 of that election year, were used. The poll asked respondents to rate Nixon and McGovern on
several personality traits. The present study used people’s ratings on the trait “strong, forceful” to determine the
candidates’ relative leadership scores. The poll results are reported in Asher (1992, 162).
xv While this might be a strong assumption, the idea that party identification has only a limited effect on the vote
forecast seems reasonable, as there is relatively little variance in party identification from election to election. In
contrast, issue evaluations fluctuate greatly, and thus can swing more votes at the aggregate level. This procedure
also avoids potential problems associated with the long-term stability of party identification and multicollinearity
with the other predictor variables, both of which are factors that limit the performance of regression analysis.
xvi The use of the incumbent two-party vote-shares as the dependent variable is common in election forecasting. The
underlying assumption is that independent or third party candidates draw support equally from the two major parties
(Campbell and Garand 2000).
xvii An exception are the 2012 forecasts, which were published prior to the election at PollyVote.com.
xviii To calculate an ex ante forecast, say 70 days prior to Election Day, one would use only historical polls conducted
prior to 70 days before Election Day in each election year. The Election Eve forecast would use all available polls
from all prior elections.
xix The limited number of observations and polls is detrimental to the performance of the Issues and Leaders model.
The absence of polling data for elections before 1972 leaves only eleven elections to draw upon, which is
problematic when calculating ex ante forecasts through successive updating. In addition, there are generally fewer
polls for early elections, which limits the validity of the issues and leadership scores over the course of the
campaign. For example, for the 1972 election, data from only a single leadership poll, conducted more than two
months prior to Election Day, were available. In such a situation, it is impossible to incorporate changes in voter
perception during the campaign. The performance and validity of the Issues and Leaders model should improve with
more observations and more polling data.
xx For the model by Norpoth and Bednarczuk (2012), no data were available.
xxi The in-sample forecasts were estimated based on all available data for each model. That is, in the case of the
Issues and Leader model, the eleven elections from 1972 to 2012 were used. In the case of, for example, the Fair
(2009) model, the 25 elections from 1916 to 2012 were used. It is important to note that the in-sample forecasts of
the established models are based on replications of the latest model specifications and data that were used to forecast
the 2012 election. Expect for the Fair model, for which the data is published at http://fairmodel.econ.yale.edu, all
models are described in the special issue of PS: Political Science & Politics 45(4). That is, these forecasts cannot
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exactly replicate the models’ forecasts for elections prior to 2012, since the model specifications and data may have
changed over time.
xxii The forecasts of the established models are one-off predictions and thus did not change in the encompassing
regression. In contrast, the forecasts of the Issues and Leader model were updated daily.
xxiii Note that the 1984 election is the first election that can be estimated without using all degrees of freedom.
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Table 1: Ex ante forecasts and corresponding errors of eight political economy models and the Issues and Leaders model (1996-2012)
1996 2000 2004 2008 2012
Election result 54.7 50.3 51.2 46.3 52.0
FC AE FC AE FC AE FC AE FC AE MAE
Benchmark models 4.0 0.6 3.1 5.5 1.4
Wlezien & Erikson 56.0 1.3 55.2 4.9 51.7 0.5 47.8 1.5 52.6 0.6 1.8
Abramowitz 56.8 2.1 53.2 2.9 53.7 2.5 45.7 0.6 50.6 1.4 1.9
Lewis-Beck & Tien 54.8 0.1 55.4 5.1 49.9 1.3 49.9 3.6 48.2 3.8 2.8
Fair 51.2 3.6 50.8 0.5 57.5 6.2 48.5 2.2 49.5 2.5 3.0
Campbell 58.1 3.4 52.8 2.5 53.8 2.6 52.7 6.4 52.0 0.0 3.0
Norpoth 57.1 2.4 55.0 4.7 54.7 3.5 49.9 3.6 53.2 1.2 3.1
Holbrook 57.2 2.5 60.0 9.7 54.5 3.3 44.3 2.0 47.9 4.1 4.3
Lockerbie 57.6 2.8 60.3 10.0 57.6 6.4 41.8 4.5 53.8 1.8 5.1
Average forecast (mean of individual forecasts) 56.1 1.4 55.3 5.1 54.2 2.9 47.6 1.3 51.0 1.0 2.3
Average error (mean of individual errors) 2.3 5.1 3.3 3.0 1.9 3.1
Issues and Leaders model (quasi ex ante)
Average of 90 to 60 days prior to Election Day 50.7 4.0 50.8 0.6 54.3 3.1 51.8 5.5 53.4 1.4 2.9
Election Eve 57.2 2.4 51.0 0.8 52.6 1.3 45.8 0.5 51.3 0.7 1.1
Bold: Most accurate model in election year.
Italics: Model predicted the wrong election winner
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Table 2: No. of polls, issues and leadership scores, and in-sample forecasts calculated on Election Eve
Issue-salience Issue-handling competence Leadership quality
% of issues mentioned as
most important per
category
No. of poll questions per issue
category
Incumbent's issue
handling score per issue
category
Popular
two-party
vote share
Election
year No. Econ Foreign Other No. Econ Foreign Other Econ Foreign Other
Issues
score No.
Incumbent's
leadership
score FC Act.
Abs.
error
1972 17 28% 35% 37% 43 17 4 22 53% 66% 63% 62% 1 67% 60.6 61.8 1.2
1976 16 67% 9% 24% 94 33 24 37 47% 60% 40% 47% 4 54%* 49.3 49.0 0.3
1980 20 71% 14% 15% 81 45 18 18 37% 50% 61% 42% 5 43% 43.8 44.8 1.1
1984 19 34% 35% 31% 403 170 108 125 69% 55% 40% 55% 9 71% 58.9 59.1 0.3
1988 10 54% 17% 29% 392 153 120 119 55% 62% 56% 56% 4 51% 53.3 53.8 0.6
1992 21 54% 6% 40% 477 186 66 225 40% 68% 36% 40% 1 57%* 46.9 46.4 0.5
1996 14 35% 4% 61% 637 216 85 336 60% 54% 60% 60% 4 54% 56.0 54.7 1.3
2000 12 18% 5% 78% 813 200 67 546 50% 49% 53% 53% 23 50% 50.9 50.3 0.6
2004 42 30% 36% 34% 1340 399 346 595 48% 57% 48% 51% 48 57% 52.5 51.2 1.3
2008 50 63% 13% 24% 853 265 269 319 43% 53% 43% 44% 33 47% 46.0 46.3 0.3
2012 49 60% 5% 35% 538 248 85 205 49% 55% 57% 52% 26 52% 51.3 52.0 0.6
Total/
Avg. 270 47% 16% 37% 5671 1932 1192 2547 158 0.7
* Leadership score pointed to wrong election winner.
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-- Figure 1: R2 (in-sample, 1972-2012) and mean absolute error (ex ante, 1996-2012) over the course of the campaign --
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-- Figure 2: Encompassing regression coefficients of the Issues and Leaders model and seven benchmark models --
Black line: Issues and Leaders model
Grey line: Benchmark model
Dotted data points visualize coefficients that are twice as large as their standard error (t-statistic)
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-- Figure 3: Model coefficients over the course of the campaign --
Dotted data points visualize coefficients that are twice as large as their standard error (t-statistic)
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-- Figure 4: Model coefficients across election years --
Dotted data points visualize coefficients that are twice as large as their standard error (t-statistic)
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Appendix I: Categorization of issues
Economic issues:
Adequate relief, Agricultural problems, Automation, Businesses, Cost of living, Depression, Economic
stimulus plan, Economy, Energy, Farms, Federal budget, Federal spending, Financial crisis, Gas prices,
Gold standard, Handling big business, Housing, Industry, Inflation, Interest rates, Jobs, JobsForeign policy,
Keeping America prosperous, Labor management, Labor problems, Making America competitive, Making
America more competitive, national debt, Nuclear energy, Promoting new businesses, Recession,
Reconversion, Savings and loan crisis, Shortages, stock market, Taxes, Technology, Trade, Trade,
Unemployment, Veterans (jobs), Wages, Minimum wage.
Foreign issues:
Afghanistan, American prestige, Arms control, Atomic bomb, Berlin, Berlin crisis, Bosnia, Cambodia,
Central America, China, Communism, Congo, Cuba, Defense, Defense spending, Draft, Earth satellite,
Eastern Europe, Europe, Foreign affairs, Foreign policy, Foreign relations, Greece, Haiti, Homeland
security, International conflicts, International crisis, Iran, Iraq, Japan, Korea, Kosovo, Laos, Latin America,
Lebanon, Middle East, Military strength, Nicaragua, Nuclear test ban treaty, Nuclear war, Panama Canal,
Peace, Quemoy, Respect for America, Russia, Saddam Hussein, Somalia, South Africa, Southeast Asia,
Soviet Union, Space program, Space programme, Suez Canal, Terrorism, TerrorismForeign policy, Turkey,
United Nations, Vietnam, War.
Other issues:
Abortion, Abuse of power, Affirmative action, AIDS, Airline security, Alcohol, American dream, American
values, Art, Attitude of American people, Big business abuses, Breaking the gridlock in Washington,
Campaign finance reform, Changing the tone in Washington, Child abuse, Children, Cities, Civil rights,
Clinton, Clothing, Consumer protection, Controlling the CIA, Corporate abuse, Corporate accounting,
Corporate corruption, Corporate fraud, Corporate responsibility, Corporate scandals, Corruption, Corruption
in Government, Corruption in government, Cost of government, Crime, Crisis management, Death penalty,
Disaster management, Discrimination, Diseases, Dishonesty, Domestic affairs, Domestic crisis, Domestic
issues, Domestic policy, Drugs, Ecology, Education, Elderly, Election, End-of-life decisions, Environment,
Espionage, Ethics in government, Fairness, Families, Family values, Farm policy, Federal bureaucracy,
Flag burning, Food safety, Freedom of speech, Gap between rich and poor, Gap between the rich and the
poor, Gay rights, Generation gap, Getting Congress to act, Global warming, Government, Government
employees, Government integrity, Gun violence, Guns, Handling relations with Congress, Health care,
Health care costs, Health insurance, Healthcare, Helping businesses, Helping farmers, Helping helping
people like yourself, Helping minorities, Helping people like yourself, Helping people with disabilities,
Helping the elderly, Helping the homeless, Helping the middle class, Helping the poor, Helping the
unemployed, Highways, Hispanic community, Homeownership, Homosexuality, Honesty in government,
Human rights, Hunger, Illegal immigration, Immigration, Impeachment proceedings against Bill Clinton,
Individual rights, Integration, Integrity, Integrity in government, Integrity of government, Judicial system,
Juvenile delinquency, Labor unions, Lack of respect, Law and order, Law enforcement, Leadership,
Lobbyism, Managing government, Managing the government, McCarthy, Media, Medical malpractice,
Medicare, Middle class, Military families, Minorities, Monica Lewinsky, Moral values, National parks,
National unity, Natural disaster, Nixon, None, Other, Overpopulation, Parental rights, Partisanship, Party
cooperation, Patient rights in HMOs, Patient's bill of rights, Patients' rights, Pentagon bribery scandal,
Pollution, Poverty, Power of the IRS (Internal Revenue Service), Prescription drug benefit for Medicare),
Prescription drug costs, Prescription drugs, Privacy, Problems of ordinary Americans, Protecting
consumers in HMOs, Public schools, Quality of life, Race, Race problems, Race relations, Reagan,
Reforming business practices, Reforming government, Reforming HMOs, Reforming the government,
Regulating HMOs, Religion, Representing the middle class, Representing your values, Representing your
views, Republicans, Research funding, Restoring faith in government, Restoring honor in Washington,
Retirement savings, Riots, Roosevelt, Same-sex marriage, Sanitation, School prayer, School violence,
School vouchers, Schools, Secularization, Segregation, Sexual harassment, Size of government, Slums,
Smog, Smoking, Social, Social programs, Social security, Social welfare, Soldiers, Stem cell research,
Student protests, Supreme Court appointments, Term limits, Transparency of government, Transportation,
Trust in government, Unifying the country, Unrest, Veteran's problems, Veterans, Violence in the media,
Voting, Weather, Welfare, Women's health issues, Women's rights, Working with Congress, Y2K, Youth.
... I developed the "Issues and Leaders" model in an effort to aid decision making in political campaigns. The model, which was used to predict the national popular two-party vote in the US presidential elections of 2012 (Graefe 2013) and 2016 (Graefe 2016), is consistent with standard voting theories in assuming that both shortterm (issues and candidate evaluations) and long-term (party identification) forces affect people's vote choice (Campbell et al. 1980;Clarke et al. 2011;Lewis-Beck et al. 2008). In particular, the model assumes that voters prefer a candidate who they (1) expect to do a better job in handling the issues, and (2) perceive as a stronger leader. ...
... As shown by Graefe (2013), the relative importance of issueshandling and leadership scores changes during the course of the campaign, which must be considered when making longterm forecasts. For example, the vote equation estimated 12 weeks prior to the election was as follows: ...
... 2. In other words, 70% of the new average is from the latest issues-handling competence score and the remaining 30% is from the previous average. This is the same weighting procedure used in 2012 (Graefe 2013) and 2016 (Graefe 2016). ...
... Thus, perceptions of candidates' warmth may therefore be particularly relevant to electoral behavior in the 2016 general election. This possibility is contrary to what has been observed in prior research investigating evaluations of candidates' traits at both the implicit (Ksiazkiewicz et al., 2018) and explicit level (e.g., Graefe, 2013), which indicates that competence assessments are more consequential than alternative trait assessments. ...
... Across many contexts, warmth and competence perceptions operate orthogonally and correspond with beliefs about the targets' intentions and ability to execute those intentions (Fiske et al., 2007). Indeed, candidates gain votes when they are perceived as likeable (e.g., Lewis-Beck and Stegmaier, 2000) and competent (e.g., Graefe, 2013). Thus, we expected implicit warmth and competence assessments to each predict evaluations of the presidential candidates and related political groups, as well as vote intentions in the 2016 election, above and beyond explicit analogs, demographics variables, and partisan identification (Hypothesis 1). ...
... Additionally, based on prior research investigating the impact of both implicit (Ksiazkiewicz et al., 2018) and explicit (e.g., Graefe, 2013) trait evaluations of political candidates, we expected that competence would be more consequential for political attitudes and behavior. Results confirmed this prediction. ...
Article
A major challenge to understanding the causes and consequences of how citizens assess political candidates is the extent to which relevant attitudinal evaluations are accessible at the conscious and unconscious level. The current research examines a dual-process model of candidate trait perceptions in the context of the 2016 U.S. Presidential elections. We expected that implicit evaluations of the warmth and competence of Donald Trump and Hillary Clinton would predict explicit evaluations of the presidential candidates and related political groups, as well as voting behavior. We find that these implicit constructs, especially competence, demonstrated predictive validity for outcomes of interest in the context of the 2016 election, above and beyond explicit analogs, demographics variables, and partisan identification. The larger role of implicit competence, compared to implicit warmth, may be due, in part, to increased assimilation of implicit associations into explicit evaluations on the warmth but not the competence dimension. These findings are suggestive of the possibility that warmth assessments were also consequential in this electoral context, consistent with other research examining the impact of gender stereotypes on evaluations of females in positions of leadership. Implications and future directions for the study of political cognition, gender bias, candidate evaluations, and electoral decision-making are discussed.
... Multiple regression analysis estimates the sizes of causal variables' effects from a given data set, a procedure that will fail to produce good forecasting models in many situations. As summarized by Graefe (2013), the accuracy of forecasts of election results from regression models is adversely affected by the use of non-experimental data, small sample sizes, the exclusion of many important variables, measurement errors, variables that correlate with one another, omitted variables, and important variables do not vary much in the estimation sample. Under such conditions, which are common for social science problems, regression is best confined to estimating models for situations in which there are only a few causal variables that are important (Armstrong, 2012). ...
... The gains from combining forecasts and using all important variables were achieved for election forecasting models that, for the most part, used similar variables. We expect that further gains in accuracy could be achieved by incorporating information from other important variables, such as biographical information (Armstrong and Graefe, 2011) about the candidates or perceptions of their issue-handling competence and leadership skills (Graefe, 2013). ...
... If one has good prior knowledge about the relative importance of variables, differential weights obtained from a priori analysis might improve the predictive validity of an index model. For example, weighting the importance of issues based on information from issue-salience polls reduced error by 28% relative to the error of forecasts from an equal-weights issue-index model (Graefe, 2013). ...
... When estimating variable weights, multiple regression analysis cannot account for uncertainty arising from sources including biases in the data, use of proxy variables, omission of important variables, inclusion of irrelevant variables, lack of variation in variable values in the estimation sample, and error in predicting or controlling causal variables in the future. As a result, multiple regression models are insufficiently conservative for forecasting as they tend to overfit an incomplete model specification to inadequate estimation data (see, e.g., Graefe, 2013). Armstrong, Green and Graefe (2015) provide four conservative guidelines for causal models. ...
... The gains from combining forecasts and using more of the important variables were achieved for election forecasting models that, for the most part, used similar variables. We expect that further gains in accuracy could be achieved by incorporating variables that measure other important effects, such as candidates' prior experience (Armstrong and Graefe 2011) as well as their issue-handling competence and leadership skills (Graefe 2013). ...
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Problem Do conservative econometric models that comply with the Golden Rule of Forecasting pro- vide more accurate forecasts? Methods To test the effects of forecast accuracy, we applied three evidence-based guidelines to 19 published regression models used for forecasting 154 elections in Australia, Canada, Italy, Japan, Netherlands, Portugal, Spain, Turkey, U.K., and the U.S. The guidelines direct fore- casters using causal models to be conservative to account for uncertainty by (I) modifying effect estimates to reflect uncertainty either by damping coefficients towards no effect or equalizing coefficients, (II) combining forecasts from diverse models, and (III) incorporating more knowledge by including more variables with known important effects. Findings Modifying the econometric models to make them more conservative reduced forecast errors compared to forecasts from the original models: (I) Damping coefficients by 10% reduced error by 2% on average, although further damping generally harmed accuracy; modifying coefficients by equalizing coefficients consistently reduced errors with average error reduc- tions between 2% and 8% depending on the level of equalizing. Averaging the original regression model forecast with an equal-weights model forecast reduced error by 7%. (II) Combining forecasts from two Australian models and from eight U.S. models reduced error by 14% and 36%, respectively. (III) Using more knowledge by including all six unique vari- ables from the Australian models and all 24 unique variables from the U.S. models in equal- weight “knowledge models” reduced error by 10% and 43%, respectively. Originality This paper provides the first test of applying guidelines for conservative forecasting to estab- lished election forecasting models. Usefulness Election forecasters can substantially improve the accuracy of forecasts from econometric models by following simple guidelines for conservative forecasting. Decision-makers can make better decisions when they are provided with models that are more realistic and fore- casts that are more accurate.
... This study showed that local leadership affected voting behavior. Furthermore, the results of this study also support the results of Graefe (2013) in the election of the President of the United States, which show that the ability to overcome crises and leadership abilities shown during the campaign affect voting behavior. Another study conducted by Garzia & Ferreira da Silva (2021), a comparative study in several Western democracies, shows that political party leaders who have dominant power in political parties strongly influence voting behavior. ...
... Voters' judgements on a leader's traits mediate support for leaders (Graefe 2013;Funk 1997). Furthermore, gender stereotypes can mediate this relationship. ...
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There has been praise of how female leaders have handled the Coronavirus pandemic relative to their male counterparts by presenting a more “caring” leadership. Of similar coverage has been the role of public trust for how successful governments have been in containing outbreaks. In this paper, we build on these two literatures to understand different determinants of trust during the pandemic between men and women. Following social role theory, we argue that female citizens’ trust judgements are more likely to be driven by the perception that leaders are more caring than are men, whilst men's judgements are more likely to be driven by competence judgements than women's. We test this argument using original survey data from three countries. We find that this relationship holds in the United States, but not the United Kingdom or Italy. This adds to variation in gender gaps in the USA and Europe; at the same time, it also suggests that the propensity for women to be less trusting than men is not down to (perceived) leadership traits.
... verstärken sie eine glaubhafte Verbindung zu aktuellen Themenbereichen und gelingt es, sich in den Massenmedien in Bezug auf diese Themen als Problemlöser zu präsentieren, so entsteht daraus ein Wettbewerbsvorteil bei der Wahl (vgl. Petrocik, 1996;Fournier, Blais, Nadeau, Gidengil, & Nevitte, 2003;Bélanger & Meguid, 2008;Graefe, 2013; einen Überblick über die Forschung bietet Dahlem 2001). Die grundlegende Voraussetzung dafür besteht jedoch darin, überhaupt erst in den Medien genannt zu werden. ...
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... As shown in Figure 8, the five available index models overestimated Clinton's support by an average of 2.6 percentage points, primarily due to the large error of two models, the bio-index (Armstrong and Graefe 2011) and the issue-index (Graefe and Armstrong 2013). In comparison, the three remaining models--including the big-issue model (Graefe and Armstrong 2012), the Issues and Leaders model (Graefe 2013), and the Keys to the White House (Lichtman 2008) 10 In other words, it is extremely difficult to foresee which method will be the most (or least) accurate in a given election. This is, of course, one of the major reasons why combining forecasts is such a useful strategy. ...
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The PollyVote uses evidence-based techniques for forecasting the popular vote in presidential elections. The forecasts are derived by averaging existing forecasts generated by six different forecasting methods. In 2016, the PollyVote correctly predicted that Hillary Clinton would win the popular vote. The 1.9 percentage-point error across the last 100 days before the election was lower than the average error for the six component forecasts from which it was calculated (2.3 percentage points). The gains in forecast accuracy from combining are best demonstrated by comparing the error of PollyVote forecasts with the average error of the component methods across the seven elections from 1992 to 2016. The average errors for last 100 days prior to the election were: public opinion polls (2.6 percentage points), econometric models (2.4), betting markets (1.8), and citizens’ expectations (1.2); for expert opinions (1.6) and index models (1.8), data were only available since 2004 and 2008, respectively. The average error for PollyVote forecasts was 1.1 percentage points, lower than the error for even the most accurate component method.
... Across a broad range of behavioral contexts, warmth and competence judgments operate orthogonally and correspond with perceptions of the targets' intentions and ability to execute those intentions, respectively (Fiske, Cuddy, and Glick 2007). Consistent with this, in political contexts, incumbent party vote share rises when the candidate is perceived as more likeable (e.g., Lewis-Beck and Stegmaier 2000) and perceived issue-handling competence is an important predictor of electoral success (e.g., Graefe 2013). ...
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While the study of political attitudes has incorporated implicit processes in its theoretical models, the predominant approach to candidate-trait perception focuses exclusively on explicit processes. Our novel, dual-process approach to candidate perception sees voters as holding both conscious, explicit impressions of candidate traits and automatic, implicit candidate-trait associations that cannot be measured using traditional self-report techniques. We examine implicit candidate-trait associations for the first time using data from a three-wave online panel conducted in the last month of the 2012 U.S. presidential election. First, we demonstrate that implicit candidate-trait associations exist. Second, we show that implicit associations of warmth and competence with the candidates predict explicit candidate evaluations, economic evaluations, and vote choice, above and beyond conventional political science controls and explicit trait perceptions. Finally, we find that these effects are strongest among nonpartisans and partisans with conflicted feelings about their party's nominee. We suggest future directions for implicit political cognition research, including trait perception.
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Theory: This paper develops and applies an issue ownership theory of voting that emphasizes the role of campaigns in setting the criteria for voters to choose between candidates. It expects candidates to emphasize issues on which they are advantaged and their opponents are less well regarded. It explains the structural factors and party system variables which lead candidates to differentially emphasize issues. It invokes theories of priming and framing to explain the electorate's response. Hypotheses: Issue emphases are specific to candidates; voters support candidates with a party and performance based reputation for greater competence on handling the issues about which the voter is concerned. Aggregate election outcomes and individual votes follow the problem agenda. Method: Content analysis of news reports, open-ended voter reports of important problems, and the vote are analyzed with graphic displays and logistic regression analysis for presidential elections between 1960 and 1992. Results: Candidates do have distinctive patterns of problem emphases in their campaigns; election outcomes do follow the problem concerns of voters; the individual vote is significantly influenced by these problem concerns above and beyond the effects of the standard predictors.
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Theory: In terms of economic voting, voters' perceptions of economic indicators can be more important than the statistics themselves. This distinction is particularly important in understanding George Bush's defeat in 1992. Hypothesis: Relentlessly negative reporting on economic performance during the election year negatively affected voters' perceptions of the economy. These altered perceptions influenced voting behavior. Methods: Ordinary least squares regression is used to demonstrate the media's impact on economic evaluations. Logistic regression is used to demonstrate the importance of economic evaluations in vote choice. Results: Media consumption and attention to the presidential campaign through the mass media negatively shaped voters' retrospective economic assessments. These assessments were significantly related to vote choice. This suggests an explanation for why George Bush lost reelection despite an economy that had rebounded from recession well in advance of election day.