Embed Size (px)
Citation preview
ORI GIN AL PA PER
Risk Attitudes and the Incumbency Advantage
David L. Eckles • Cindy D. Kam •
Cherie L. Maestas • Brian F. Schaffner
� Springer Science+Business Media New York 2013
Abstract Explanations for the incumbency advantage in American elections have
typically pointed to the institutional advantages that incumbents enjoy over chal-
lengers but overlook the role of individual traits that reinforce this bias. The
institutional advantages enjoyed by incumbents give voters more certainty about
who incumbents are and what they might do when (and if) they assume office. We
argue that these institutional advantages make incumbents particularly attractive to
risk-averse individuals, who shy away from uncertainty and embrace choices that
provide more certainty. Using data from 2008 and 2010 Cooperative Congressional
Election Study, we show that citizens who are more risk averse are more likely to
support incumbent candidates, while citizens who are more risk accepting are more
likely to vote for challengers. The foundations of the incumbency advantage, we
find, lie not only in the institutional perks of office but also in the individual minds
of voters.
Keywords Incumbency advantage � Risk aversion � Voter choice �Elections � Prospect theory
D. L. Eckles
Department of Risk Management and Insurance, University of Georgia, Athens, GA 30602, USA
e-mail: [email protected]
C. D. Kam
Department of Political Science, Vanderbilt University, Nashville, TN 37203, USA
C. L. Maestas
Department of Political Science, Florida State University, Tallahassee, FL 32306, USA
B. F. Schaffner (&)
Department of Political Science, University of Massachusetts, Amherst, Amherst, MA 01002, USA
e-mail: [email protected]
123
Polit Behav
DOI 10.1007/s11109-013-9258-9
Incumbency advantage is the established rule in US elections. A substantial body of
research has developed to help explain why this incumbency advantage exists. Most
explanations center on institutions and elite behavior in the US House. Incumbents,
the scholarship suggests, are re-elected because they work hard to satisfy their
constituents while in office (Fenno 1977, 1978; Cain et al. 1987), because they
enjoy franking privileges (Cover and Brumberg 1982), because they can more
effectively raise funds (Jacobson and Kernell 1983; Abramowitz 1991), or because
they can deter high-quality challengers (Stone et al. 2004, 2010). However,
incumbency advantages have grown over the past decades for executive office-
holders as well, which suggests that legislative institutions alone are not a sufficient
explanation for the increased incumbent vote shares and reduced competition for
office (Ansolabehere and Snyder 2002). Further, scholars note that while
incumbents are ‘‘advantaged’’ in terms of vote margin, the margins do not
necessarily translate to a greater probability of winning because they are
accompanied by greater vote volatility (Ansolabehere and Snyder 2002; Jacobson
1987a). The incumbency advantage in US elections may be the norm, but such
advantage is neither inevitable nor guaranteed.
How might we understand the perplexing dual phenomenon of increased vote
margins with punctuations of highly volatile elections? The most plausible elite-side
explanation centers on candidate entry decisions, where strong challengers respond
to national and local conditions, thereby creating volatility in otherwise placid
districts (Jacobson and Kernell 1983; Jacobson 1989). Voters, from this perspective,
respond to the slate of candidates presented to them by elites but little attention is
paid to how mechanisms internal to voters might promote stasis or volatility, or to
how voters may differ amongst themselves in their reactions to these slates. We
suggest that understanding modern era incumbency vote patterns requires unpack-
ing the individual characteristics of voters to understand who contributes to support
for the incumbent and who might be responsive to the entry of a challenger. In
contrast to the elite-centered literature, we highlight the extent to which
heterogeneity in the electorate’s willingness to tolerate risk can help explain some
portion of incumbency advantage. Incumbents receive a greater share of media
attention compared to challengers (Prior 2006) and rely on risk-averting messages
about their past experience, governing ability, and district service (e.g., Druckman
et al. 2009). Challengers offer a risky alternative to an experienced incumbent, but
we argue that for some voters—particularly those who are risk tolerant—a risky
choice may be more appealing than a safe one.
Incumbents, Challengers, and Uncertainty in Political Decision-Making
Since Erikson’s seminal article in 1971, political scientists have amassed a
substantial body of research studying the incumbency advantage (see Carson and
Roberts 2011 for a review). Early explanations of the incumbency advantage
focused on the incumbent’s use of office to maximize the likelihood of re-election
through assisting constituents (i.e., pork barrel politics), generating increased
general visibility, and better fundraising opportunities (Erikson 1971). Further,
Polit Behav
123
Erikson (1971) points out that the incumbent, by definition, is likely to be a high
quality candidate, and therefore more likely to be re-elected. Since Erikson (1971),
numerous scholars have considered these rationales, and have put forth alternative
explanations for the incumbency advantage. Hood and McKee (2010, p. 346)
provide a useful summary of these explanations:
There are numerous (often complementary) explanations for the incumbency
advantage: credit claiming, position taking, and advertising (Mayhew 1974),
constituency service (Fiorina 1977), declining party attachments (Ferejohn
1977), strategic retirements (Cox and Katz 2002) and strategic challenger
entry (Cox and Katz 1996; Jacobson and Kernell 1983), and the declining
ability of challengers to raise enough money for competitive campaigns
(Abramowitz 1991).
In addition to candidate-specific explanations, several system-level explanations
have been offered for the incumbency advantage. McKelvey and Riezman (1992)
suggest that the incumbency advantage is an artifact of the seniority systems
employed in legislatures. Recently, scholars have also examined general changes in
long-term (Ansolabehere et al. 2000) and short-term (Ansolabehere and Snyder
2002; Desposato and Petrocik 2003) conditions (e.g., economic events) and
redistricting (Cox and Katz 2002; Hood and McKee 2010) as components of
incumbency advantage. Evidence that incumbency has grown similarly in executive
offices and in sub-national legislatures suggests there might be reasons beyond
redistricting or other institutional perquisites enjoyed by legislators (Ansolabehere
and Snyder 2002).
Largely missing from the study of incumbency advantage are voter-level
explanations. By this, we mean specifically that relatively little work has sought to
understand the psychology of the decision to vote for an incumbent rather than a
challenger. Instead, early analyses of congressional voters explored trends in
defections of partisan voters (e.g., Ferejohn 1977; Jacobson 1987b) and tested the
incumbent-level and system-level explanations using data from individual voters
(e.g., Abramowitz 1980; Fiorina 1977, 1981). Scholars found evidence in individual
and aggregate data that the importance of party in vote choice declined over time,
with challenger partisans defecting to vote for incumbents (Ferejohn 1977; Jacobson
1987b). Individual voters preferred incumbents to challengers because they were
more familiar and visible (Abramowitz 1980; Campbell 1983; Jacobson 1981; Mann
and Wolfinger 1980), had positive reputations (Abramowitz 1975; Campbell 1983;
Jacobson 1981), and were attentive to constituents’ needs (Abramowitz 1980; Cover
and Brumberg 1982; Cain et al. 1984; Fiorina 1977, 1981; Yiannakis 1981).1 Overall,
it appeared that voters generally lacked knowledge of congressional candidates and
their positions but whatever limited knowledge they held centered on incumbents.
Later scholarship tied individual-level awareness and assessment of congressio-
nal incumbents to the presence of quality challengers, thus challenger entry patterns
were central to explaining the individual-level findings (Jacobson and Kernell 1983
1 There is some debate as to the size and source of individual level evidence of incumbency advantage in
the early literature (see Born 1986; Eubanks 1985; Fiorina 1981; Johannes and McAdams 1981).
Polit Behav
123
Jacobson 1981; Mann and Wolfinger 1980; Ragsdale 1981).2 Prior (2006) argued
that the spread of local television stations in the 1960s that offered favorable media
coverage to incumbents increased the incumbency advantage. Television expansion
opened up opportunities for incumbents to credit claim via televised media (Cook
1989) which in turn translated into greater voter support (Prior 2006). In these and
in earlier voter studies, the source of incumbency advantage could be seen through
attitudes and behaviors of voters; the aggregate patters arose as, en masse, voters
responded to the choices presented to them by elites.
With a few notable exceptions, there has been a marked absence of studies that
consider whether attributes internal to the voter shape the importance of incumbency.
Early studies suggested that patterns of aggregate dealignment might be the source of
incumbency advantage (Erikson 1972; Cover 1977) and subsequent tests at the
individual-level found support for this argument as incumbency cues were more
important for weak partisans than strong partisans (Nelson 1978; Cain et al. 1984,
Romero and Sanders 1994). These findings were certainly consistent with Zaller’s (1992)
evidence suggesting that individual-level political awareness influences both receipt and
acceptance of countervailing information from the challenger in congressional elections.
In a similar vein, Prior’s (2006) work suggests a complementary individual-level attribute
that shapes reception and acceptance of campaign information: education. Exposure to
pro-incumbent messaging on local television stations was much more influential for
voters with little education than for voters highly educated. The results suggest the
incumbency advantage might arise not only from biases in the availability of information
but also on individual susceptibility to the information at hand. We argue there has been
little systematic investigation of voter-level explanations beyond these three factors
(partisanship, awareness, and education). The paucity of voter-level psychological
explanations of the incumbency advantage is an important lacuna to address because
incumbency cues may mean different things to different people.
One voter-level explanation mentioned in the existing literature points to a
general level of risk aversion among the mass public.3 As Shepsle (1972) argues,
‘‘the act of voting, like that of gambling or purchasing insurance, is one involving
‘risky’ alternatives’’ (p. 560). In races between incumbents and challengers,
incumbents are generally better known and thus viewed with more certainty
compared with challengers. The voter has experience with how the incumbent
operates when holding that office, but the voter generally must make an uncertain
prospective judgment about how the challenger will behave if elected. And, if voters
uniformly are assumed to be risk averse, then voters will support incumbents over
challengers, all else equal. Shepsle’s (1972) theoretical model suggests that in
conditions under which all voters are risk averse or the majority of voters is risk
2 See Born (1986) for an alternative perspective.3 Notably, risk aversion has been promoted as an explanation for an ‘‘incumbency advantage’’ in non-
political decisions. For example, Muthukrishnan (1995) conducted a series of experiments to determine
the factors that led people to choose a new consumer product over the one they were currently using. The
study found that nearly 40 % of subjects who stayed with their incumbent brand did so despite reporting
that they thought the challenging brand was superior. The author cites risk aversion as the likely reason
that subjects would maintain loyalty to their brand despite recognizing that there appeared to be a better
alternative.
Polit Behav
123
averse, then incumbency advantage will prevail.4 Following up on Shepsle’s work,
scholars have built in uncertainty into a number of models, with the predominant
assumption being that voters are uniformly risk averse (Davis et al. 1970; Enelow
and Hinich 1981; Bartels 1986; Alvarez 1997).
The risk aversion accounts of voter decision-making portray the public as a
homogeneous mass, cringing from the uncertain and flocking to the certain. But voters
are not uniformly risk averse, as recent empirical research utilizing a variety of
measures has identified clear variation in citizens’ risk attitudes and their consequences
for political behavior and political choices (e.g., Berinsky and Lewis 2007; Eckles and
Schaffner 2011; Ehrlich and Maestas 2010; Kam 2012; Kam and Simas 2010, 2012).
As such, we argue that there is theoretically interesting and important heterogeneity
within the public, particularly with respect to their risk attitudes and the consequences
of those attitudes for electoral decision-making. Our focus on individual-level risk
attitudes fits into a growing literature within political science that examines the effect
of risk attitudes on political choices and behaviors and pushes it further by focusing on
candidate choice within congressional elections in the US.
We expect that risk attitudes will influence an individual’s propensity to vote for an
incumbent, independent of other influences such as partisanship and retrospective
evaluations. Voters generally know more about the incumbent’s positions and
capabilities than they do about the challenger, making them a more certain and, hence,
less risky choice. For example, respondents to the Cooperative Congressional
Election Surveys in 2010 were almost twice as likely to place the incumbent on the
ideological scale as they were to place the challenger, providing support for the notion
that they were generally less certain about where the challenger stood. Though voters
may not share the same ideological beliefs as the incumbent, voters are more likely to
be certain about where the incumbent fits on the ideological spectrum and whether
they can and will carry out campaign promises while in office. Voters can also more
easily assess an incumbent’s ability to bring other sorts of benefits to the district such
as pork projects and constituent services. Projecting a future stream of benefits from a
challenger is much more difficult and much less certain. Although a voter may be
well-informed about which policies a challenger proposes to support once in office,
there is still some risk that the challenger will not carry through on those promises
once elected or will not be effective in pursuing those policies. Moreover, the ability
of a challenger to provide non-policy benefits to the district as effectively as a sitting
incumbent is unknown. Since a voter has already had experience with the incumbent,
they will likely feel more confident about projecting what that candidate will actually
do if she is re-elected. Morgenstern and Zechmeister (2001) refer to this concept as
governing capability, and note that even if voters are unaware of the incumbent
candidate’s issue position, the incumbent may be seen as having a greater capacity to
govern. Thus, even controlling for voter proximity to candidates’ positions, the fact
4 Prospect Theory offers one conditionality to this general argument. According to Prospect Theory,
features of the contextual environment will make decision-makers more or less likely to engage in risky
choice. Within the domain of losses, decision-makers are risk-seeking. Within the domain of gains,
decision-makers are risk-averse. Quattrone and Tversky (2000) argue that voters will stick with the
incumbent (the less risky candidate) in the region of gains (during good economic times) and will take the
‘‘political gamble’’ by backing challengers in the region of losses (during bad economic times).
Polit Behav
123
that voters will be less certain about the challenger’s positions, traits, and capabilities
than the incumbent’s leads to our expectation that that risk-averse citizens will prefer
incumbents more than risk-tolerant citizens.
Risk Tolerance and Congressional Vote Choice
To assess the relationship between citizens’ risk attitudes and vote choice in
congressional elections, we analyze data from the 2008 Cooperative Congressional
Election Study (CCES).5 The dependent variable, Vote for Incumbent, takes a value
of 1 if an individual voted for the incumbent and 0 if an individual voted for a
challenger. Within the 2008 CCES module that we are analyzing, 49 % of
respondents lived in districts with a Republican incumbent and 51 % lived in districts
with a Democratic incumbent. Among self-reported voters, 61 % voted for an
incumbent and 39 % for a challenger. Our analysis is limited to the 673 voters living
in districts featuring an incumbent and challenger representing the two major parties.
To tap risk attitudes, we utilize a two-question battery pioneered by Barsky et al.
(1997). A version of it appeared on the 1996 Panel Study of Income Dynamics, and
the measure has been validated for political decision-making by Eckles and
Schaffner (2011). The two-question battery first provides respondents with a scenario
in which they currently have a job that offers a stable and certain income every year
for life. Then respondents are asked whether they would take a risky job offer that has
a 50–50 chance of doubling the respondent’s current income and a 50–50 chance of
lowering the respondent’s current income by 30 %. A follow-up question provides
those who were initially risk averse with an alternative job offer that offers a 50–50
chance of doubling the respondent’s current income and a 50–50 chance of lowering
the respondent’s current income by 20 %. A follow-up question provides those who
were initially risk accepting with an alternative job offer that again offers a 50–50
chance of doubling the respondent’s current income and a 50–50 chance of halving
the respondent’s current income. Figure 1 provides the complete question text.
Combining responses to these two items enables us to create a 4-point measure of
Risk Tolerance, ranging from risk intolerant to risk tolerant. Figure 2 displays the
distribution in the 2008 CCES. A little over half of the sample chooses the least risk
tolerant option, always preferring the guaranteed income every year for life over the
two risky options. About 20 % of the sample is maximally risk-tolerant, willing to
accept some possibility of loss in order to maximize possible gain; the remainder of
the sample is distributed in-between.
The measure of risk attitudes consists of a willingness to take a hypothetical
financial gamble relative to keeping a certain financial gain. The advantage to this
5 The CCES is a cooperative survey project that allows teams to purchase individual module surveys.
The survey was conducted via the Internet by YouGov/Polimetrix using a matched random sample
design. A subset of respondents recruited for online surveys were selected by matching them on a set of
demographic characteristics to a randomly selected set of individuals from the population of American
adults. Propensity score weights for the samples were developed so as to ensure that the sample represents
the demographic characteristics of the adult population as reflected in the 2004 and 2008 Current
Population Survey.
Polit Behav
123
measure is that we have a more or less direct measure of the willingness to take
risks: rather than requiring citizens to engage in an abstract self-reported assessment
of their risk orientation, citizens are given a more or less concrete scenario in which
they make a decision. This measure is limited to one domain of risk-taking: financial
risk. The narrowness of our measure makes it all the more difficult to gain leverage
on political decision-making, if risk attitudes have a certain degree of domain
specificity (e.g., Blais and Weber 2006; Weber et al. 2002). However, the payoff is
that it allows us to say with confidence that the independent variable is quite distinct
from the dependent variable (congressional vote choice): there is no worry that we
are essentially measuring the same thing on both sides of the equation.6
We can address questions of criterion validity by examining whether our measure
has properties that are generally consistent with other measures that have been used
in previous research. We find, consistent with existing literature (e.g., Ehrlich and
Maestas 2010; Kam and Simas 2010; Weber et al. 2002), that women are
significantly less risk tolerant than men (r = 0.11), older people are less risk
tolerant than younger people (r = 0.08), the educated are more risk tolerant,
married people are less risk tolerant, and conservatives are less risk tolerant than
liberals (all pairwise correlations significant at p \ 0.05). Notably, income was
uncorrelated with our risk tolerance scale, indicating that responses to the question
about taking a financial risk were not influenced by the respondent’s current
financial situation. And, Eckles and Schaffner (2011) have demonstrated the
predictive validity of the Risk Tolerance measure for foreign policy opinions.
To make our estimates of the effect of Risk Tolerance more credible, we also
control for a number of factors that are likely to influence an individual’s vote for
the incumbent. Although incumbent status operates as an important determinant of
Fig. 1 Risk tolerance battery
6 The distribution of Risk Tolerance does not appear to be context-dependent. The distribution of Risk
Tolerance in the 1996 PSID (during a relatively strong economy) is strikingly similar to that uncovered in
the 2008 CCES (and in the 2010 CCES as well).
Polit Behav
123
congressional vote, we also suspect partisanship will be important. We expect that
partisans are much more likely to vote for a candidate from their own party and
substantially less likely to vote for a candidate from the other party. As such, we
include two dummy variables that indicate whether or not the respondent shares the
incumbent candidate’s party affiliation: In-partisan is coded 1 when the respondent
shares the party affiliation of the incumbent; Out-partisan is coded 0 when the
respondent shares the party affiliation of the challenger. The suppressed reference
group thus consists of Independents.7 Because we know that economic conditions
figure heavily into the voter’s decision (Kinder and Kiewiet 1979), we include a
measure of retrospective economic evaluations.8 In addition, we include variables to
capture contextual effects: a dummy variable indicating whether the sitting
incumbent House member is a Republican, to account for general partisan tides of
the year, and a measure of Challenger Quality, on the idea that high-quality
challengers are capable of chipping away at incumbents’ advantage.9
The results in Table 1 demonstrate that Risk Tolerance significantly predicts
willingness to support a challenger: the less risk tolerant an individual is, the more
0%
10%
20%
30%
40%
50%
60%
Least risk tolerant Somewhat risk tolerant Moderately risk tolerant Most risk tolerant
Risk Tolerance, CCES 2008
Fig. 2 Distribution of Risk Tolerance, CCES 2008
7 Here, we utilize the three category summary measure (cc307) to ensure that we have enough
respondents in the Independent category.8 The measure is based on cc302 and ranges from 0 (the economy has gotten ‘‘much better’’) to 1 (the
economy has gotten ‘‘much worse’’). In 2008, over a majority of respondents though the economy had
become ‘‘much worse’’ in the past year.9 Challenger quality is a dummy variable that takes on the value of 1 if a challenger held elective office
previously and 0 otherwise. Data on challenger quality and candidate expenditures for 2008 and 2010
were compiled and generously shared by Gary Jacobson.
Polit Behav
123
likely she is to support the incumbent; the more risk tolerant an individual is, the
more likely she is to support the challenger. Risk Tolerance is not the only
explanation for congressional vote choice. As we can see from the coefficients on
In-Partisan and Out-Partisan, partisanship plays a massive role in explaining vote
choice as well. Voters line up behind members of their own party, and they avoid
members of the opposing party. Risk Tolerance plays an additional, though
supplementary role in decision-making on congressional candidates.
The predicted probabilities in Fig. 3 illustrate the magnitude of the relationship
between Risk Tolerance and Vote for the Incumbent, among out-partisans, indepen-
dents, and in-partisans.10 The predicted probability of voting for the incumbent
declines as Risk Tolerance rises. This effect is most pronounced among Independents
(the middle panel): at the lowest levels of Risk Tolerance, the predicted probability of
voting for the incumbent is about 0.68. At the highest levels of Risk Tolerance, this
predicted probability declines to 0.42. When the incumbent is from the other party, the
predicted probability of supporting the incumbent drops from 0.20 among the least
risk tolerant to 0.06 among the most risk tolerant, as shown in the left panel. When the
incumbent is from the voter’s party, the predicted probability of supporting the
incumbent declines from 0.95 among the least risk tolerant to 0.83 among the most
risk tolerant, as shown in the right panel. Our data suggest that Risk Tolerance matters
for congressional decision-making, but, as demonstrated by the intercept shifts in
Fig. 3, it is not the most important criteria upon which voters rely: partisanship is
massively important. However, finding even some support for a preference for
incumbents not from the voter’s party is consistent with our hypothesis that
incumbents benefit from an individual’s tolerance for risk (or lack thereof).
The basic model that we estimate in Table 1 is just that; it controls for some of
the major explanations typically used in explaining voting in congressional
elections, but it is not comprehensive. More importantly, it does not control for
some potentially important individual-level covariates that could be lurking in the
background. The second column of results in Table 1 come from a model that adds
several individual-level measures to the basic model. These individual-level
measures are important, because they may be correlated with Risk Tolerance and
also may predict willingness to vote for the incumbent. However, we see from the
results in Table 1 that adding in these measures of sex, age, education, income,
marital status, partisanship, and ideology makes almost no difference to the
estimated effect of Risk Tolerance.11 The effect of Risk Tolerance is still strong and
significant and of comparable magnitude.
10 We plot predicted probabilities for respondents in a Democratically held district who hold average
economic assessments.11 Female is a dummy for female respondents (v208). Age is coded 0 (youngest) to 1 (oldest), based on
v207. Education is a six category variable ranging from 0 (no high school degree) to 1 (advanced degree),
based on v213. Income is a 14 category variable ranging from lowest (0) to highest (1), with refusals set to
zero. Income Refused is a dummy for those who refused to report income. Married is a dummy for
married respondents (v214). Partisanship is based on cc307a and consists of the seven-category measure,
ranging from 0 (strong Democrat) to 1 (strong Republican). Ideology is a five-category self-placement
measure based on v243, ranging from 0 (very liberal) to 1 (very conservative).
Polit Behav
123
Table 1 Risk tolerance and vote for congressional incumbent, 2008
Basic model Covariates Spending Distance
Risk tolerance -0.68***
0.18
-0.60***
0.18
-0.64***
0.19
-0.62***
0.22
In-partisan 1.16***
0.19
1.12***
0.18
1.21***
0.18
0.84***
0.21
Out-partisan -1.32***
0.19
-1.44***
0.20
-1.44***
0.19
-0.89***
0.24
National economic evaluations -0.20
0.46
-0.29
0.48
-0.23
0.48
-0.40
0.62
Republican incumbent -0.06
0.15
-0.12
0.15
-0.02
0.15
-0.11
0.18
Challenger quality 0.03
0.16
-0.09
0.16
0.05
0.16
0.04
0.18
Female respondent 0.24
0.16
0.19
0.16
0.01
0.19
Age of respondent 0.77**
0.30
0.72**
0.29
0.77**
0.35
Education of respondent -0.11
0.23
-0.10
0.24
0.29
0.28
Income of respondent -0.30
0.32
-0.27
0.32
-0.43
0.37
Income refused -0.76**
0.30
-0.72**
0.31
-0.41
0.40
Married respondent 0.16
0.16
0.13
0.16
0.11
0.19
Partisanship of respondent -0.06
0.27
0.04
0.29
0.12
0.32
Ideology of respondent -0.08
0.39
-0.17
0.40
-0.22
0.45
Ln (incumbent spending) 0.19
0.14
0.21
0.17
Ln (challenger spending) -0.09**
0.04
-0.09*
0.04
Ideological distance from incumbent -2.57***
0.37
Intercept 0.64
0.46
0.60
0.54
-1.17
1.92
-0.55
2.23
lnL (pseudo) -271.95 -258.91 -249.08 -172.38
J (districts) 279 279 278 245
N (respondents) 660 660 659 506
Table entry is the weighted probit coefficient with standard error clustered by district below
* p \ 0.10, ** p \ 0.05, *** p \ 0.01, two-tailed
Polit Behav
123
Our model of voting in congressional elections may be leaving out important
aspects of the candidates and the nature of the election within the district. Our basic
model includes a control for challenger quality, but it is insignificant, perhaps
because it is too coarse a measure.12 As such, our next model includes measures that
should more directly be predictive of voting in congressional elections: spending.
When we include the natural log of incumbent spending and the natural log of
0.2
.4.6
.81
Pre
dict
ed in
cum
bent
vot
e
0 1
Risk Tolerance
Out-Partisans
0.2
.4.6
.81
Pre
dict
ed in
cum
bent
vot
e
0 1
Risk Tolerance
Independents
0.2
.4.6
.81
Pre
dict
ed in
cum
bent
vot
e0 1
Risk Tolerance
In-Partisans
Fig. 3 Predicted vote for the incumbent in 2008, by partisan standing. Predicted probabilities based onbasic model, Table 1
12 We also reanalyzed the models using a variation of the challenger quality measure which
differentiated the prior offices held by challengers (state legislators, other elective office holders, and
former members of the House) with no substantive difference in results. We offer a handful of additional
explanations for the insignificant results on challenger quality. The first has to do with the bluntness of the
dependent measure. Our dependent measure is the dichotomous vote choice at the individual-level.
Challenger quality is a significant predictor of incumbent vote share at the district-level, but its predictive
power is fragile when it comes to the dichotomous measure of district-level incumbent victory. The
second has to do with the merging of individual respondents to districts: we lose about 60 districts in the
merge, given the design of the CCES and the voting patterns of the respondents. Third, the peculiarities of
sampling anywhere between one and seven individual voters per district introduces more imprecision in
estimating district-level effects compared with the aggregate election outcomes. Finally, weighting the
individual-level survey data to be nationally representative allows us to make inferences about
individuals, but results in weighting some districts more than others (in district-level, aggregate analysis,
all districts are equally weighted). For these various reasons, we note that our results speak more
persuasively to individual-level factors than district-level factors. We control for district-level factors to
make our individual-level estimates more credible, but our dataset is not optimally crafted to adjudicate
district-level effects.
Polit Behav
123
challenger spending, we see that challenger spending significantly predicts voting
for incumbents in the expected direction: the more challengers spend, the less likely
voters are to support the incumbent. More importantly, the introduction of these
campaign-level variables does nothing to the estimated effect of Risk Tolerance.
Still, congressional elections may be determined by more than partisanship and
Risk Tolerance. Ideology—in particular, the ideological distance from the
incumbent—might be an important determinant of how voters decide. The 2008
CCES asked respondents to place themselves and the two house candidates on a
0–100 ideological scale. We generate a measure of ideological distance from the
incumbent that ranges from 0 (respondents place themselves and the incumbent at
the same location) to ?1 (respondents place themselves and the incumbent at
opposite ends of the spectrum).13 When we include this measure of ideological
distance in our model, we see that it does significantly predict voting for the
incumbent: respondents who see a greater distance between themselves and the
incumbent are significantly less likely to support the incumbent. Importantly,
inclusion of this measure makes no difference to the effect of Risk Tolerance.
Thus far, we have shown that citizens who are more risk tolerant are more willing
to entertain a congressional challenger than citizens who are less risk tolerant. We
have established that these basic patterns are robust to the inclusion of a suite of
individual-level characteristics, campaign-level factors, and the intersection of the
two in ideological proximity. We have shown that Risk Tolerance may account for
some willingness to withstand incumbency advantage in 2008. But, are these results
limited to 2008? Was there something unique about the political environment in
2008 in particular that activated risk attitudes in electoral decision-making, or are
these results representative of a more general phenomenon?
The 2008 presidential race pitted the nation’s first serious African-American
presidential hopeful, Barack Obama, against a well-known and well-venerated war
hero, Republican John McCain. The Obama team campaigned on a message of
change, attempting to portray a McCain presidency as simply another four years of
Bush policies and Washington politics, issuing campaign mottos such as ‘‘We are
the change we’ve been waiting for.’’ McCain campaign’s strategy emphasized his
lengthy public service, experience, and established record as a bipartisan statesman,
framing the race as one between a proven veteran and an untested unknown. The
high visibility, high stakes presidential campaign combined with severe economic
turbulence served as a backdrop to the 2008 House races throughout the nation.
Such considerations could have created an environment that was unusually
conducive to the application of risk attitudes to vote choice. Hence, we now
examine whether similar effects manifest themselves two years later, in 2010.
13 We construct our measure using variables cc317a, cc317k, and cc317l. Note that we lose about a
quarter of our respondents, because they are unable or unwilling to place the incumbent on the ideological
scale. Missingness is even more severe when it comes to the challenger: two-thirds of respondents fail to
place the challenger on the ideological scale. The basic result holds when we only include a measure of
challenger proximity: challenger proximity is highly significant and in the expected direction (b = 2.52,
SE = 0.51, p \ 0.01) and more importantly, for Risk Tolerance, b = -0.91, SE = 0.31, p \ 0.01, but N
plummets to 262.
Polit Behav
123
The 2010 congressional elections provide an important contrast to the 2008
congressional elections. Where 2008 was dominated by the high profile presidential
election, 2010 was a midterm election. The two election years also differ in the
prevailing partisan tides: 2008 was a modestly successful year for the Democrats
where they picked up 21 seats. Election 2010 was most decisively a Republican
victory: the GOP picked up 63 seats in the House and the Democrats suffered their
worst midterm losses in over 70 years.
To analyze the effects of risk attitudes on congressional vote choice in 2010, we
utilize data from the 2010 installment of the CCES. We limit our analysis to the
1,409 voters living in districts featuring an incumbent and challenger representing
the two major parties. Of these respondents, 39 % lived in districts with a
Republican incumbent and 61 % lived in districts with a Democratic incumbent.
Among self-reported voters, 53 % voted for an incumbent and 47 % for a
challenger. The 2010 CCES module contained the identical risk instrumentation,
and the distributions of responses to the risk tolerance battery in 2008 and 2010
were virtually indistinguishable.14 When we re-estimate the relationship in 2010,
using an identical specification, we obtain very similar results, as shown in Table 2.
We see that the effect of Risk Tolerance in 2010 operates as expected: the risk
tolerant are less likely to vote for an incumbent and more likely to vote for a
challenger. We also see that Risk Tolerance is not the primary determinant of
congressional voting: partisanship matters strongly. In-partisans support their
incumbent candidate, and out-partisans are more likely to defect from the
incumbent. As in 2008, Risk Tolerance plays a supplementary, but significant role
in predicting the congressional vote decision. We illustrate these effects in Fig. 4.
As in 2008, we find that the effects are strongest among Independents, among
whom the predicted probability of supporting the incumbent drops from 0.52 among
the least risk tolerant to 0.37 among the most risk tolerant. Among in-partisans, the
predicted probability of voting for the incumbent falls from 0.89 among the least
risk tolerant to 0.80 among the most risk tolerant; among out-partisans, the predicted
probability of voting for the incumbent falls from 0.08 among the least risk tolerant
to 0.04 among the most risk tolerant. And, as in 2008, these results stand up to
inclusion of additional individual-level and campaign-level covariates as shown in
the succeeding columns of Table 2.
For robustness, we also investigated several second-order hypotheses. Of primary
interest, we considered the case that voters make distinctions among challengers –
that challengers who are more experienced seem less ‘‘risky’’ than challengers who
are less experienced. This line of theorizing would suggest an interaction between
risk tolerance and challenger quality, such that the effect of risk tolerance would
emerge more strongly in races featuring less experienced challengers. This is
precisely what we find in 2008, where we uncover a statistically significant
interaction between risk tolerance and challenger quality, such that the effect of risk
tolerance essentially disappears when experienced challengers face incumbents.
However, we uncover statistically insignificant results in 2010, but these results
14 Specifically, 52.3 % of respondents are least risk tolerant, 16.7 % are somewhat risk intolerant, 11.6 %
are more risk tolerant, and 19.3 % are maximally risk tolerant.
Polit Behav
123
Table 2 Risk tolerance and vote for congressional incumbent, 2010
Basic model Covariates Spending Distance
Risk tolerance -0.38**
0.16
-0.34**
0.15
-0.34***
0.15
-0.49***
0.18
In-partisan 1.17***
0.15
1.24***
0.14
1.22***
0.14
0.74***
0.16
Out-partisan -1.44***
0.21
-1.49***
0.17
-1.49***
0.17
-1.17***
0.20
National economic evaluations -0.23
0.22
-0.02
0.26
-0.01
0.26
0.26
0.30
Republican incumbent 0.19
0.14
0.25*
0.13
0.21
0.17
0.18
0.19
Challenger quality 0.06
0.14
0.06
0.14
0.11
0.15
0.27
0.18
Female respondent 0.02
0.14
0.03
0.14
-0.22
0.16
Age of respondent -0.85**
0.34
-0.83 **
0.33
-0.67*
0.36
Education of respondent 0.19
0.23
0.19
0.23
0.34
0.25
Income of respondent -0.09
0.30
-0.07
0.30
-0.32
0.33
Income refused -0.03
0.24
-0.01
0.24
-0.01
0.27
Married respondent 0.04
0.14
-0.04
0.15
0.01
0.16
Partisanship of respondent -0.31
0.26
-0.30
0.26
-0.08
0.26
Ideology of respondent -0.49
0.30
-0.49
0.30
-0.60*
0.34
Ln (incumbent spending) -0.08
0.15
0.11
0.15
Ln (challenger spending) -0.01
0.06
0.02
0.05
Ideological distance from incumbent -3.76***
0.33
Intercept 0.18
0.21
0.75
0.49
1.99
1.71
3.08
1.96
lnL (pseudo) -450.68 -439.03 -438.38 -268.70
J (districts) 353 353 353 279
N (respondents) 1,367 1,366 1,366 1,184
Table entry is the weighted probit coefficient with standard error clustered by district below
* p \ 0.10, ** p \ 0.05, *** p \ 0.01, two-tailed
Polit Behav
123
could have been, in part, a function of the peculiarities of the 2010 election. Further
analysis, based upon data from more election years, is warranted.15
Discussion
Explanations for the incumbency advantage have traditionally focused on the
institutional advantages that incumbents hold over challengers: that is, what
contributes to incumbency advantage. We supplement this existing work by
identifying who contributes to incumbency advantage. Our empirical results
demonstrate the important role that risk attitudes play in bolstering incumbent office
holders and in opening the door to challengers. We have shown that tolerance for
risk significantly predicts a willingness to entertain challengers and stray from
incumbents. This effect appears to be strongest among Independents and is
0.2
.4.6
.81
Pre
dict
ed in
cum
bent
vot
e
0 1
Risk Tolerance
Out-Partisans
0.2
.4.6
.81
Pre
dict
ed in
cum
bent
vot
e
0 1
Risk Tolerance
Independents
0.2
.4.6
.81
Pre
dict
ed in
cum
bent
vot
e0 1
Risk Tolerance
In-Partisans
Fig. 4 Predicted vote for the incumbent in 2010, by partisan standing. Predicted probabilities based onbasic model, Table 2
15 It may also be the case that the effect of Risk Tolerance is conditioned by in-party versus out-party
status. Our analyses provide little evidence to support this speculation. Finally, we examined whether the
effect of Risk Tolerance differs across House and Senate elections. We suspect the effect would be
weaker in higher-profile elections, such as senate and presidential races, where citizens might have other
criteria upon which to base their votes. This is essentially what we found in our analysis of the 2008 and
2010 senate vote: a negative but statistically insignificant effect of Risk Tolerance on the likelihood of
voting for the senatorial incumbent. In the interest of conserving space, the tables representing the
analyses described above are omitted. They are available from the authors’ upon request.
Polit Behav
123
relatively weaker (but still demonstrable) among in-partisans and out-partisans.
These results are not sensitive to model specification, as the results are robust to
inclusion a suite of potentially confounding individual-level characteristics and
contextual-level factors. Finally, we have shown that these results all hold in two
very different electoral contexts, the presidential election year 2008 and the midterm
elections of 2010.
We argue that incumbency advantage might arise, in part, from risk aversion on
the part of voters. A necessary condition for that explanation to work is that risk
aversion affects vote choice in a way that favors incumbents. We explicitly test and
find evidence consistent with that condition. Of course, various factors can interfere
with the link from the individual-level result we have uncovered to aggregate
election outcomes. Indeed, we think it possible that heterogeneity in risk attitudes
might help explain why sometimes the relatively stable, high margins for
incumbents suddenly become volatile. Eligible voters from different ends of the
risk-tolerance scale might, in the aggregate, be differentially mobilized by electoral
context. In ordinary times in ordinary districts, incumbents engage in activities that
capture the interest and attention of local media who, in turn, highlight the positive
and low risk attributes of incumbents—experience, attentiveness, and serving the
interests of the district. Indeed, incumbents themselves work hard to present
themselves as low risk representatives. They steer clear of issues, negativity, and
focus on the uncontroversial work they do for constituents (Druckman et al. 2009).
Challengers to most incumbents are typically too weak and underfunded to reach the
radars of most voters, so in most elections incumbency is one of the only relevant
cues, and a cue that is highly appealing to the risk averse. When national conditions
are ripe, though, challengers, particularly strong challengers, can gain traction in the
media. They often ‘‘go negative’’ and focus on needed changes in government
(Kahn and Kennedy 1999). Change, however, is likely to be more appealing to the
risk tolerant than to the risk averse.
Our results suggest that such challengers may be particularly appealing to the risk
tolerant. And, risk tolerant individuals are more politically active than the risk
averse: they are more likely to engage in costly political activities such as donating
money, working on a campaign, distributing information, or attending political
meetings (Kam 2012). A ready pool of activists willing to participate beyond voting
is necessary for a challenger to mount an effective campaign, and risk tolerant
individuals in the district provide a potential supply this important campaign input.
Since risk tolerant voters are more likely to favor challengers over incumbents,
broader economic or political tides could mobilize risk-tolerant voters in support of
challengers and produce a considerable vote swing.16
In a recent review of the literature on congressional elections, Carson and
Roberts (2011) note that ‘‘the idea that incumbent office-holders have an advantage
16 Our discussion is based on decision-making in single-shot elections, but the shadow of future elections
is worth discussion. If a voter is forward-thinking, she may recognize that today’s challenger (if
successfully elected) will be tomorrow’s incumbent, and her risk attitude should be weighted against
future options and the her discount factor for the future. Such a rationale requires that voters be future-
oriented, an assumption that may rely upon an overly optimistic view of myopic voters (e.g., Healy and
Malhotra 2009).
Polit Behav
123
over their would-be challengers runs afoul of many tenants of democratic theory
[…]. If incumbent legislators are gaining an electoral advantage due to the rules of
the game being skewed in their favor, then this would be especially problematic and
require some form of change’’ (p. 147). Indeed, by focusing mostly on elite-centered
explanations for the incumbency advantage, the institutional advantages enjoyed by
incumbents become a frequent target for reformers seeking to make elections more
competitive. Specifically, the incumbency advantage has been cited as an impetus
for reforms like term limits, public funding of campaigns, and campaign finance
regulations. Yet, our findings suggest that incumbents, simply because they have
held office, represent what is known versus what is unknown, could still hold an
edge in their reelection campaigns because of widespread risk aversion in the
electorate. While this may give incumbents an advantage, it is one that might
comport with the risk profiles of the mass electorate—suggesting that incumbency
advantage is actually a symptom of citizens voting ‘‘correctly’’—that is, to protect
themselves from uncertainty. Our results also suggest that strategic challengers may
be able to position themselves to chip away at incumbency advantage and mobilize
the risk tolerant into their cause.
Acknowledgments The authors would like to acknowledge the organizations that supported the 2008
and 2010 Cooperative Congressional Election Study data upon which this paper is based. The 2008 data
was funded by the Center for Congressional and Presidential Studies at American University. The 2010
data was funded by the National Science Foundation along with contributions from our departments and
colleges at the Florida State University, the University of Massachusetts, Amherst, and Vanderbilt
University.
References
Abramowitz, A. I. (1975). Name familiarity, reputation, and the incumbency effect in a congressional
election. The Western Political Quarterly, 28(4), 668–684.
Abramowitz, A. I. (1980). A comparison of voting for U.S. senator and representative in 1978. American
Political Science Review, 74, 633–640.
Abramowitz, A. I. (1991). Incumbency, campaign spending, and the decline of competition in U.S. House
elections. Journal of politics, 53(1), 34–56.
Alvarez, R. M. (1997). Information and elections. Ann Arbor, MI: University of Michigan Press.
Ansolabehere, S., & Snyder, J. M., Jr. (2002). The incumbency advantage in U.S. elections: An analysis
of state and federal offices, 1942–2000. Election Law Journal: Rules, Politics, and Policy, 1(3),
315–338.
Ansolabehere, S., Snyder, J. M., Jr, & Charles, S., I. I. I. (2000). Old voters, new voters, and the personal
vote: Using redistricting to measure the incumbency advantage. American Journal of Political
Science, 44(1), 17–34.
Barsky R. B., Juster F. T., Kimball M. S., Shapiro M. D. (1997). Preference parameters and behavioral
heterogeneity: An experimental approach in the Health and Retirement Study. Quarterly Journal of
Economics, 112, 537–579.
Bartels, L. M. (1986). Issue voting under uncertainty: An empirical test. American Journal of Political
Science, 30(4), 709–728.
Berinsky, A. J., & Lewis, J. B. (2007). An estimate of risk aversion in the U.S. electorate. Quarterly
Journal of Political Science, 2, 139–154.
Blais, A.-R., & Weber, E. U. (2006). A domain-specific risk-taking (DOSPERT) scale for adult
populations. Judgment and Decision Making, 1(1), 33–47.
Born, R. (1986). Strategic politicians and unresponsive voters. American Political Science Review, 80(2),
599–612.
Polit Behav
123
Cain, B., Ferejohn, J. A., & Fiorina, M. P. (1984). The constituency basis of the personal vote for U.S.
representatives and British members of parliament. American Political Science Review, 78(1),
110–125.
Cain, B., Ferejohn, J. A., & Fiorina, M. P. (1987). The personal vote. Cambridge: Harvard University
Press.
Campbell, J. E. (1983). The return of the incumbents: The nature of incumbency advantage. The Western
Political Quarterly, 36(3), 434–444.
Carson, J. M., & Roberts, J. (2011). House and Senate elections. In E. Schickler & F. Lee (Eds.), Oxford
handbook of the American Congress. Oxford: New York.
Cook, T. E. (1989). Making laws and making news: Media strategies in the U.S. House of
Representatives. Brookings Institution Press.
Cover, A. D. (1977). One good term deserves another: The advantage of incumbency in congressional
elections. American Journal of Political Science, 21(3), 523–541.
Cover, A. D., & Brumberg, B. S. (1982). Baby books and ballots: The impact of congressional mail on
constituent opinion. American Political Science Review, 76(2), 347–359.
Cox, G. W., & Katz, J. N. (1996). Why did the incumbency advantage in U.S. House elections grow?
American Journal of Political Science, 40(2), 478–497.
Cox, G. W., & Katz, J. (2002). Elbridge Gerry’s salamander: The electoral consequences of the
reapportionment revolution. Cambridge: Cambridge University Press.
Davis, O., Hinich, M., & Ordeshook, P. (1970). An expository development of a mathematical model of
the electoral process. American Political Science Review, 64, 426–448.
Desposato, S. W., & Petrocik, J. R. (2003). The variable incumbency advantage: new voters, redistricting,
and the personal vote. American Journal of Political Science, 47(1), 18–32.
Druckman, J. N., Kifer, M. J., & Parkin, M. (2009). Campaign communications in U.S. congressional
elections. American Political Science Review, 103(3), 343–366.
Eckles, D. L., & Schaffner, B. F. (2011). Risk tolerance and support for potential military interventions.
Public Opinion Quarterly, 75(3), 533–544.
Ehrlich, S., & Maestas, C. (2010). Risk exposure, risk orientation, and policy opinions: The case of free
trade. Political Psychology, 31(5), 657–684.
Enelow, J., & Hinich, M. (1981). A new approach to voter uncertainty in the Downsian spatial model.
American Journal of Political Science, 25, 483–493.
Erikson, R. (1971). The advantage of incumbency in congressional elections. Polity, 3(3), 395–405.
Erikson, R. (1972). Malapportionment, gerrymandering, and party fortunes in congressional elections.
American Political Science Review, 66(4), 1234–1335.
Eubank, R. B. (1985). Incumbent effects on individual-level voting behavior in congressional elections: A
decade of exaggeration. The Journal of Politics, 47(3), 958–967.
Fenno, R. F. (1977). U.S. House members in their constituencies: An exploration. American Political
Science Review, 71(3), 883–917.
Fenno, R. F., Jr. (1978). Home style: House members in their districts. New York: Harper Collins.
Ferejohn, J. (1977). On the decline of competition in congressional elections. American Political Science
Review, 71(1), 166–176.
Fiorina, M. P. (1977). The case of the vanishing marginals: The bureaucracy did it. American Political
Science Review, 71(1), 177–181.
Fiorina, M. P. (1981). Some problems in studying the effects of resource allocation in congressional
elections. American Journal of Political Science, 25(3), 543–567.
Healy, A., & Malhotra, N. (2009). Myopic voters and natural disaster policy. American Political Science
Review, 103(3), 387–406.
Hood, M. V., I. I. I., & McKee, S. C. (2010). Stranger danger: Redistricting, incumbent recognition, and
vote choice. Social Science Quarterly, 91(2), 344–358.
Jacobson, G. C. (1981). Incumbents’ advantages in the 1978 U.S. congressional elections. Legislative
Studies Quarterly, 6(2), 183–200.
Jacobson, G. C. (1987a). The marginals never vanished: incumbency and competition in elections to the
U.S. house of representatives, 1952–82. American Journal of Political Science, 31(1), 126–141.
Jacobson, G. C. (1987b). The politics of congressional elections. Boston, MA: Little Brown.
Jacobson, G. C. (1989). Strategic politicians and the dynamics of U.S. house elections, 1946–86. The
American Political Science Review, 83(3), 773–793.
Jacobson, G. C., & Kernell, S. (1983). Strategy and choice in congressional elections. New Haven: Yale
University Press.
Polit Behav
123
Johannes, J. R., & McAdams, J. C. (1981). The congressional incumbency effect: Is it casework, policy
compatibility, or something else? An examination of the 1978 election. American Journal of
Political Science, 25(3), 512–542.
Kahn, K. F., & Kennedy, P. J. (1999). The spectacle of U.S. senate campaigns. Princeton, NJ: Princeton
University Press.
Kam, C. D. (2012). Risk attitudes and political participation. American Journal of Political Science,
56(4), 817–836.
Kam, C. D., & Simas, E. N. (2010). Risk orientations and policy frames. Journal of Politics, 72(2), 1–16.
Kam, C. D., & Simas, E. N. (2012). Risk attitudes, candidate characteristics, and vote choice. Public
Opinion Quarterly, 76(4), 747–760.
Kinder, D. R., & Kiewiet, R. D. (1979). Economic discontent and political behavior: The role of personal
grievances and collective economic judgments in congressional voting. American Journal of
Political Science, 23(3), 495–527.
Mann, T., & Wolfinger, R. (1980). Candidates and parties in congressional elections. American Political
Science Review, 71(3), 177–181.
McKelvey, R. D., & Riezman, R. (1992). Seniority in legislatures. American Political Science Review,
86(4), 951–965.
Morgenstern, S., & Zechmeister, E. (2001). Better the devil you know than the saint you don’t? Risk
propensity and vote choice in Mexico. Journal of Politics, 63(1), 93–119.
Muthukrishnan, A. V. (1995). Decision ambiguity and incumbent brand advantage. Journal of Consumer
Research, 22(1), 98–109.
Nelson, C. J. (1978). The effect of incumbency on voting in congressional elections, 1964–1974. Political
Science Quarterly, 3(4), 665–678.
Prior, M. (2006). The incumbent in the living room: The rise of television and the incumbency advantage
in U.S. House elections. Journal of Politics, 68(3), 657–673.
Quattrone, G. A., & Tversky, A. (2000). Contrasting rational and psychological analyses of political
choice. In K. Daniel & T. Amos (Eds.), Choices, values, frames. Cambridge: Cambridge University
Press.
Ragsdale, L. (1981). Incumbent popularity, challenger invisibility, and congressional voters. Legislative
Studies Quarterly, 6(2), 201–218.
Romero, D. W., & Sanders, F. (1994). Loosened partisan attachments and receptivity to incumbent
behaviors: A panel analysis. Political Research Quarterly, 47(1), 177–192.
Shepsle, K. (1972). The strategy of ambiguity: Uncertainty and electoral competition. American Political
Science Review, 66, 555–568.
Stone, W. J., Fulton, S., Maestas, C., & Maisel, L. S. (2010). Incumbency reconsidered: Prospects,
strategic entry, and incumbent quality in U.S. House elections. Journal of Politics, 72(1), 178–190.
Stone, W. J., Maisel, L. S., & Maestas, C. (2004). Quality counts: Extending the strategic polician model
of incumbent deterrence. American Journal of Political Science, 48(3), 479–495.
Weber, E. U., Blais, A.-R., & Betz, N. E. (2002). A domain-specific risk-attitude scale: Measuring risk
perceptions and risk behaviors. Journal of Behavioral Decision Making, 15, 263–290.
Yiannakis, D. E. (1981). The grateful electorate: Casework and congressional elections. American
Journal of Political Science, 25(3), 569–580.
Zaller, J. (1992). The nature and origins of mass opinion. Cambridge: Cambridge university press.
Polit Behav
123
