Does the Spatial Proximity Between Legislators and
Voters Affect Voting Decisions in U.S. House Elections?∗
Chris Tausanovitch†
Department of Political Science
UCLA
Christopher Warshaw‡
Department of Political Science
Massachusetts Institute of Technology
February 2015
Abstract: The theory of spatial voting has dominated scholarship on voting and elections.
The spatial voting theory’s most important implication is that candidates’ ideological positions should influence voters’ decisions at the ballot box. A number of recent studies find that
the spatial voting theory’s predictions are borne out in U.S. House election. This suggests
that legislators should be highly responsive to the preferences of their constituents. However,
there is only a very modest relationship between district preferences and legislators’ roll call
votes. We explain this puzzle using a new dataset of over 75,000 voters in the 2006-2012
congressional elections. In contrast to previous studies, we find that the spatial positions of
legislators have a negligible impact on citizens’ voting behavior. Instead, our results suggest
that citizens cast their votes based on proximity to parties rather than individual legislators.
Our findings help explain the polarization and lack of responsiveness in the contemporary
Congress.
∗
We are grateful to Devin Caughey, Robert Erikson, Anthony Fowler, Justin Grimmer, Seth Hill, Stephen
Jessee, Jeffrey B. Lewis, Howard Rosenthal, and seminar participants at MIT’s American Politics Conference, Princeton University, the University of California-Berkeley, UCLA, and UCSD for feedback on previous
versions of this manuscript. We also thank Stephen Ansolabehere and Phil Jones for generously sharing data
on constituents’ perceptions of legislator positions, and Adam Bonica for sharing data on campaign contributions. This paper was previously circulated under the name “Electoral Accountability and Representation
in the U.S. House: 2004-2012.”
†
Assistant Professor, Department of Political Science, UCLA, [email protected].
‡
Assistant Professor, Department of Political Science, Massachusetts Institute of Technology, [email protected].
1
The theory of spatial or proximity voting (Black, 1948; Downs, 1957; Enelow and Hinich,
1984) is one of the central ideas in scholarship on voting and elections. The spatial voting
theory’s most important prediction is that the ideological positions of candidates and parties
should influence voters’ decisions at the ballot box. This electoral connection is said to help
ensure that legislators are responsive to the views of their constituents (Mayhew, 1974).
In recent years, a number of prominent papers have examined the implications of the
theory that citizens vote spatially based on candidate positions (e.g., Ansolabehere and
Jones, 2010; Jessee, 2009, 2012; Joesten and Stone, 2014; Nyhan et al., 2012; Shor and
Rogowski, 2010; Simas, 2013). These studies conclude that voting behavior in congressional
elections is associated with the spatial proximity between the policy views of legislators and
voters. However, this result leads to a puzzle. If citizens are voting spatially for individual
candidates, then legislators should have a strong incentive to cast votes that represent the
median voter in their districts (Black, 1948). As a result, there should be a very tight
relationship between the views of constituents in each district and the roll call voting behavior
of their representative. But a large body of work shows that legislators do not converge on the
position of the median voter (Ansolabehere, Snyder Jr, and Stewart III, 2001; Levitt, 1996).
In addition, there is only a modest relationship between district preferences and legislators’
roll call voting behavior (Clinton, 2006; Lee, Moretti, and Butler, 2004; Tausanovitch and
Warshaw, 2013).
Spatial voting with regard to parties has quite different implications from spatial voting
with regard to individual legislators. This theory suggests that voters use party labels to
figure out the policy positions of elected officials (Conover and Feldman, 1989; Popkin, 1991;
Snyder and Ting, 2002). This enables voters to make a Downsian calculation about which
candidate is better from a policy perspective. They can cast their ballots rationally without
knowing much about the positions of individual candidates by simply voting for the candidate
whose party reflects their policy views (Sniderman and Stiglitz, 2012).
We show that the implications of candidate and party-based theories of proximity voting
1
can be deduced theoretically and differentiated empirically. They can also be compared
to affective theories of partisan identification. In this paper, we use a novel survey-based
dataset with the ideal points1 of over 75,000 voters in 1,100 electoral contests between 2006
and 2012 to adjudicate between candidate and party-based theories of proximity voting. We
test the implications of each theory using a non-parametric model, which does not rely on
functional form assumptions. In order to make clear the connection between the theory and
our empirical analysis, we also use a parametric model that is derived explicitly from the
theory. Both approaches yield similar results.
In contrast to previous studies, we find that the roll call positions of legislators (as
measured by DW-Nominate scores) have almost no impact on the voting decisions of most
citizens.2 Instead, we find evidence that most citizens cast their votes based on their spatial
proximity with the parties: liberal voters almost always support Democrats and conservative
voters almost always support Republicans. Additional analysis indicates that one plausible
explanation for this finding is that voters appear to be only vaguely aware of the ideal point
of their individual legislator. Only high information voters seem to take legislators’ positions
into account in congressional elections, and these effects are small.
In the last section of the paper, we use our large sample of voters to show that legislators’
roll call positions have only a very modest impact on their vote-shares in each contest.
This suggests that the ideological characteristics of individual incumbents have a negligible
influence on electoral outcomes, net of party.
Our results have broad implications for representation and democratic accountability in
the United States. Most importantly, our results show that incumbent legislators face few
electoral consequences for unrepresentative positions (cf. Wilkins, 2012). Whatever leads
1
An “ideal point” in this context is a measure of an individuals’ ideal policy position in a liberalconservative spectrum.
2
Our study focuses on measures of legislative positions that are based on roll call voting. Our focus on roll
call votes follows from our interest in representation. It may be the case that voters are sophisticated enough
to differentiate legislators on some basis, such as their publicly stated positions or some other position-taking
behavior, and vote spatially on this basis (see for instance, Grimmer, 2013; Cormack, 2013; Henderson, 2013).
However, if voters do not condition their voting behavior on roll calls, then legislators may vote as they please.
Voter behavior could be quite sophisticated, but policy representation would not be achieved.
2
more extreme candidates to lose more often (Canes-Wrone, Brady, and Cogan, 2002; Hall,
2014) probably has more to do with the correlates of extremity than policy positions per se.
Legislators do not appear to be bound by the policy views of their particular constituents,
as long as they can claim to belong to the political party their constituents prefer.3 This
helps explain the broad patterns of divergence between the parties (Poole and Rosenthal,
2000; Lee, Moretti, and Butler, 2004), and very weak responsiveness to the preferences of
constituents (e.g., Clinton, 2006), that we observe in the contemporary Congress.
Theories of Proximity and Party Voting
In An Economic Theory of Democracy, Downs (1957) argues that vote choices are a function
of the spatial proximity between the ideal points of voters and parties. This spatial voting
model was easily extended to the proposition that citizens should be more likely to vote
for legislators and other elected officials that share their ideological preferences, spawning
a long literature in spatial voting theory (e.g., Enelow and Hinich, 1984). In a related line
of research, called directional voting theory, scholars argue that voters support candidates
whose spatial positions are on the same side of the political spectrum as their own positions
(Rabinowitz and Macdonald, 1989; Macdonald, Listhaug, and Rabinowitz, 1991).4 The
common element of both of these theories is that they imply that individual candidates’
positions should influence citizens’ voting decisions.
For many years, there was “surprisingly little direct evidence supporting [the spatial
voting model’s] main assumptions–namely, that voters have preferences over the issues before
the legislature, hold beliefs about their representatives’ decisions on those questions, and use
that information in deciding whether to support the legislator” (Ansolabehere and Jones,
2010, 583). However, the explosion of large-sample surveys in recent years has facilitated a
3
Note that our findings do not suggest that legislative candidates can take any position at all. For
instance, ideologically extreme candidates that take positions far outside the bounds of their party’s platform
may still face electoral consequences (Hall, 2014).
4
Tomz and Van Houweling (2008) use survey experiments to adjudicate between theories of spatial and
directional voting. They find that spatial voting is four times more common than directional voting.
3
renaissance in scholarship on voter behavior in congressional elections.
Over the past few years, a number of prominent studies have found support for the spatial
voting model. Jessee (2009, 2012) uses a novel survey design to estimate the ideology of voters
and the major party presidential candidates in 2004 and 2008 on the same spatial scale.
Jessee finds that liberals are more likely to support the Democratic presidential candidate
and conservatives are more likely to support the Republican presidential candidate. Shor
and Rogowski (2010) extend Jessee (2009)’s framework to the level of congressional elections.
They find evidence that vote choice in congressional elections is associated with the voters’
spatial proximity with congressional candidates. In two similar studies, Ansolabehere and
Jones (2010) and Jones (2011) evaluate the impact of incumbents’ issue positions on citizens’
voting behavior. They find that respondents are more likely to vote for incumbents that
share their issue positions. Simas (2013) finds evidence that voters consider their ideological
proximity to congressional candidates and “punish candidates who take positions that are
too far out of line.” Joesten and Stone (2014) use district experts to place candidates and
survey respondents onto the same ideological scale. They conclude that “proximity voting
is common” among voters in congressional elections. Finally, Nyhan et al. (2012) find that
legislators’ positions on health care reform and other salient votes affected voters’ decisions
in the 2010 congressional elections.
These papers revolutionized the study of spatial voting by bringing it into the arena
of empirical study. However, an important limitation of their findings is that they fail to
distinguish candidate-centered versus party-centered accounts of spatial voting. Political
scientists often forget that early spatial voting theorists such as Downs (1957) and Hotelling
(1929) focused on parties rather than individual candidates and legislators. In these theories,
voters took proximity into account, but only of the national political parties. The partyfocused perspective is not unique to spatial voting theory either. The traditional theory of
partisan identification holds that party is an enduring attachment, much like religion, with
primarily affective roots (Campbell et al., 1960; Lewis-Beck et al., 2008). Party attachments
4
are formed early in life and are very stable thereafter. Policy views and vote choices are both
determined by party identification, which is the dominant force in voters’ political lives.
Policy determinations are more or less epiphenomena of party, and voters are relatively
ignorant about policy and legislative activities. Green, Palmquist, and Schickler (2004)
ground the importance of party identification in a concept of social identification. People
identify with parties because they think of themselves as being similar to other people in
their party.
One element of early theories of party identification that has earned more emphasis over
time is the notion of party as a cue or heuristic. More recent work argues that party labels
help voters figure out the policy positions of elected officials (Conover and Feldman, 1989;
Popkin, 1991; Snyder and Ting, 2002). This enables voters to make a Downsian calculation
about which candidate is better from a policy perspective. They can cast their ballots
rationally without knowing much about the positions of individual candidates by simply
voting for the candidate whose party reflects their general policy views, rather than making
detailed evaluations of particular candidates (Sniderman and Stiglitz, 2012). Indeed, the
results of several of the spatial voting studies discussed earlier actually subtly support this
line of reasoning. For instance, the descriptive tables provided by Joesten and Stone (2014,
750) and Simas (2013, 712) examine how voters cast their ballots when their spatial positions
are closer to the other party’s candidate. These tables indicate that voters continue to cast
their ballots for their own party between 70-89% of the time when their spatial positions are
closer to the other party’s candidate.5
Both the affective theory of party identification, as well as the notion of party identification as a heuristic, imply that voters with more extreme policy views should be more reliable
5
Table 5 of Joesten and Stone (2014, 750) shows that 71% of Republicans and 89% of Democrats in 2006
House elections voted for their party’s candidate when their spatial positions were closer to the opposing
party’s candidate. Likewise, column 3 of Table 1 in Simas (2013, 712) shows that citizens voted for their
party’s candidate when their spatial positions were closer to the opposing party’s candidate roughly 80% of
the time. Put differently, these studies find that between 70-89% of partisan voters whose spatial positions
are closer to the opposing party’s candidate violate the prediction of candidate-centered spatial voting theory
when they cast their vote.
5
voters for their respective parties. From an affective perspective, stronger partisans are more
likely to have “correct” policy views, as passed down from their respective parties. From a
heuristic perspective, party is a more effective heuristic for those with more extreme policy
views. Therefore, both perspectives lead us to expect that more extreme ideologues will also
be more loyal partisans. On average, liberals and conservatives will vote for the legislator of
their party at higher rates than moderates.
Distinguishing Alternative Theories of Voting
How can we distinguish candidate and party-based theories of proximity voting? Theories of
proximity voting based on candidate positions have a simple premise: unless utility functions
are very flat or there is very little variation in legislator positions, then citizens’ votes must
depend strongly on legislator positions. If they do not, then either citizens are not voting
spatially/directionally or the role that these considerations play in their decision is small.6
Theories of party voting hold that there should be large cross-party differences in voting but
that these differences should not depend on the position of the particular legislator. Further,
if party attachment is primarily affective, voters’ policy positions should be largely irrelevant.
All of the previous studies, however, fail to distinguish candidate-centered versus partycentered accounts of spatial voting. For instance, Jessee (2009) shows that liberals are more
likely to vote Democrat and conservative are more likely to vote Republican, but fails to
demonstrate that spatial proximity is the feature that determines this relationship. Similarly,
many scholars use spatial proximity as their key independent variable without accounting for
the direct effect of voter ideology, thereby confounding these explanations (e.g., Joesten and
Stone, 2014; Shor and Rogowski, 2010; Simas, 2013). Finally, while many existing papers
control for the effect of party identification, they fail to account for party loyalty. The
literature indicates that partisanship is likely to interact with policy positions: more liberal
6
Of course, it is always possible that voters are capable of using a proximity voting rule, but that the use
of such voting rules is not prevalent enough to matter. It is also possible that they use a proximity voting
rule, but with respect to an orthogonal space or notion of position.
6
Democrats will tend to be more loyal Democrats, and more conservative Republicans will
tend to be more loyal Republicans. Rather than accounting for this possibility, past work has
tended to attribute all of the effect of ideology to the spatial proximity between voters and
candidates. In the next section, we derive a simple model of voting behavior in congressional
elections, and then discuss how we translate this model into an empirical model of voting
behavior.
A Model of Voting Behavior in Congressional Elections
Consider a voter i with ideal point xi , and election j where the incumbent has ideal point rj
and the challenger has ideal point rj . According to the candidate-centered notion of spatial
voting, voters should vote, with error, for the candidate who has an ideal point “closer” to
their own. Although voters cast votes with error, they are more likely to vote for their
favored candidate as the spatial advantage of their favored candidate grows. Equations 1
through 3 spell this out mathematically.
P (yij = 1) = f (d(xi , rj ) − d(xi , cj ))
(1)
= Λ((xi − rj )2 − (xi − cj )2 )
(2)
= Λ(−2xi rj + rj2 + 2xi cj − c2j )
(3)
This very simple algebra shows that the spatial voting logic leads directly to a logistic
regression model if we assume a logistic link function and quadratic utility. We exclude
challengers from our analysis and focus on the effects of xi rj and xi . We will return to the
consequences of excluding challengers shortly. For now, note that we can rewrite this as a
more general logistic regression equation, where c2j is subsumed in the constant term and cj
is assumed to be orthogonal to the other variables.
7
P (yij = 1) = Λ(β0 + β1 xi rj + β2 xi + β3 rj2 )
(4)
The most important prediction from this model is that β1 will be significantly different
from zero. If β1 is substantively and statistically significant, then the probability of supporting the incumbent depends on the distance between the incumbent’s ideal point and the
voter’s ideal point.
H1 (Candidate-Centered Spatial Voting): The distance between the voter and the incumbent’s
ideal point should have a substantive effect on the probability of supporting the incumbent.
Distance is captured by the interaction between the incumbents’ ideal point and the voter’s
ideal point. In terms of the model: β1 6= 0.
What does H1 imply in terms of the effect that different incumbent positions should have
on the likelihood that different constituents will support the incumbent? Empirically, we will
not be able to say whether particular voters are to the left or right of particular legislators.7
However, we can safely assume that Republicans in Congress are in the most conservative
third of the electorate and that Democrats in Congress are in the most liberal third. We
can classify voters as either liberals, moderates or conservatives based on their policy views,
with a third of the public in each group. If a Democratic member of Congress becomes
more moderate, then their conservative and moderate constituents should be more likely to
support them. Whether liberals support them more or less depends on exactly how liberal
the member is and how liberal those voters are – the prediction is ambiguous. If a Republican
member of Congress becomes more moderate, their moderate and liberal constituents should
be more likely to support them. In this case, the prediction for conservatives is ambiguous.
7
The task of estimating voter positions in the space of legislators is a difficult one. It requires assuming
equivalence between some set of behaviors that are driven by policy position: for instance, that casting roll
call votes in a legislature can be considered equivalent to answering survey questions about roll call votes,
or that campaign contributions are given to more spatially proximate candidates. Lewis and Tausanovitch
(2013) find that existing attempts to jointly scale voters and legislators in the same space fall short.
8
These theoretical predictions are displayed in Table 1.
Table 1: Theoretical Effect of Incumbent Moderating their Ideal Point on Pr(Vote for Incumbent)
Voter Ideology:
Theoretical
Prediction
Democratic Incumbent
Liberal
Moderate Conservative
ambiguous
+
+
Republican Incumbent
Liberal Moderate Conservative
+
+
ambiguous
The predictions yielded by a party-centered notion of spatial voting are different than the
predictions yielded by a candidate-centered model. Equations 5 through 7 show the algebra
for a party-centered notion of spatial voting, where p1 is the ideal point of the incumbent’s
party and p2 is the ideal point of the challenger’s party.
P (yij = 1) = f (d(xi , p1 ) − d(xi , p2 ))
(5)
= Λ((xi − p1 )2 − (xi − p2 )2 )
(6)
= Λ(−2xi p1 + p21 + 2xi p2 − p22 )
(7)
The most important difference between equations 5 through 7 and equations 1 through
3 is that in the former, the position of the two parties is a constant for all voters. As a
result, equation 7 can be simplified to a very parsimonious logistic regression. p21 and p22 are
subsumed by the constant, and xi is multiplied by the unknown constant −2p1 + 2p2 . We
can capture this with the following equation:
P (yij = 1) = Λ(β0 + β2 xi )
(8)
The party-centered notion of spatial voting yields a single important prediction. As long
as the two parties do not adopt the precise same location, the position of the voter should
affect the voter’s likelihood of voting for the incumbent.
9
H2 (Party-Centered Spatial Voting): The voter’s ideal point should have a substantive effect
on the probability of supporting the incumbent. In terms of the model: β2 6= 0.
Note that H2 and H1 are not the same. H1 says that the distance between the legislator
and the voter should matter. But according to H2, only the position of the voter matters.
Because voters all experience the same party locations, incumbent positions are immaterial.
To set these hypothesis against each other, we formulate a logistic regression specification
that includes both, as below. We control for contest-specific random effects, the direct
effect of the incumbent legislator’s ideal point, and we include quadratic terms for both the
incumbent and the voter.
P (yij = 1) = Λ(β0 + β1 xi rj + β2 xi + β3 rj + β4 rj2 + β5 x2i )
(9)
In order to capture the classic hypothesis that voting is based on an affective party
attachment, we will estimate equation 9 within-party for the six possible matches between
legislator and voter party. Legislators are either Democrats or Republicans, and voters are
either Democrats, Republicans, or Independents. The within-party specification ensures
that the incumbent party is the same for all voters, and allows the intercept to account for
traditional party voting.
Unlike some other recent studies (e.g., Joesten and Stone, 2014; Shor and Rogowski,
2010), we have chosen in this paper to focus explicitly on incumbent positions and eschew
any attempt to estimate the policy positions of challengers. As a result, we cannot rule out
the possibility that challengers position themselves in such a way as to nullify the effects of
incumbent positioning. We find such a possibility highly unlikely, because if true, it has a
counterintuitive implication: that voters reward candidates for moderation, but candidates
respond to extreme opponents by diverging further.8 We elaborate on this argument formally
8
We acknowledge that there are theories that rationalize candidate divergence in the face of candidatespecific spatial voting by voters. The most famous example is from Calvert (1985) and Wittman (1983).
10
in Appendix A. A substantial benefit of excluding challengers from the analysis is that it
enables us to avoid some stubborn methodological problems. We are interested in whether
voters constrain the roll call voting behavior of their representatives. For challengers who
fail to unseat the incumbent, roll call voting itself is a counterfactual, and estimating ideal
points is difficult.
Survey Data
In order to examine whether voters hold members of the House accountable for their roll call
behavior, we need surveys with information on voters’ policy preferences and vote choices
in congressional elections. To this end, we pool together the 2006, 2008, 2010, and 2012
Cooperative Congressional Election Surveys (Table 2).9 Each of these surveys asked between
14 and 32 policy questions to 30,000-55,000 Americans.10 This enables us to jointly scale
each respondents’ ideal point using an approach similar to the one taken by Tausanovitch
and Warshaw (2013). Moreover, each of these surveys include self-reported vote choice in
congressional elections as well as the party and congressional district of each respondent.
To estimate voters’ ideological positions, we assume that all survey respondents have a
quadratic utility function with normal errors (Clinton, Jackman, and Rivers, 2004). Each
item presents individual with a choice between a “Yes” position and a “No” position. We
use the two-parameter IRT model introduced to political science by Clinton, Jackman, and
Rivers (2004), which characterizes each response yij ∈ {0, 1} as a function of subject i’s
latent ability (xi ), the difficulty (αj ) and discrimination (βj ) of item j, and an error term
(eij ), where
Pr[yij = 1] = Φ(βj xi − αj )
(10)
9
We use the pre-election survey for respondents’ policy questions, and the post-election panel for their
vote choice. Note that each of these surveys name both the challenger and incumbent candidates in each
contest.
10
Appendix D shows all of the questions that we use in our model. We dichotomize each of the survey
questions for our scaling model.
11
where Φ is the standard normal CDF. βj is referred to as the “discrimination” parameter
because it captures the degree to which the latent trait affects the probability of a yes
answer. The “cut point” is the value of αj / βj at which the probabilities of answering yes
or no to a question are 50-50. We assume a one-dimensional policy space because a twodimensional model shows little improvement in terms of model fit. The ideal point, x, for
individual i signifies the “liberalness” or “conservativeness” of that individual. We orient our
values so that lower values are associated with politically left preferences and higher values
with politically right preferences. We approximate the joint posterior density of the model
parameters using a Markov chain Monte Carlo (MCMC) method. To validate our estimates,
Table 3 shows the strong relationship between symbolic ideology and our scaled measure of
citizens’ ideal points.
Table 2: Data Sources for Voters
Survey
CCES 2006
CCES 2008
CCES 2010
CCES 2012
Total
Total Sample Size Post-Election Sample of Respondents
that Indicated a Vote Choice
35,919
22,046
32,800
17,895
55,488
32,004
54,535
29,999
178,742
101,944
Post-Election Sample in
Contested Race
16,842
14,584
26,922
22,755
81,104
In all, we have information on the ideal points of 178,000 survey respondents (Table
1). We have information on self-reported vote choices for approximately 80,000 of these
respondents in contested races with incumbents running for re-election. Our estimates of
legislators’ party and roll call positions come from Poole and Rosenthal’s DW-NOMINATE
scores (Poole and Rosenthal, 2000). Data on legislators’ incumbency status are derived from
Gary Jacobsen’s data on congressional elections and research by the authors. Finally, we
classify “leaners” (those who identify themselves as Independents but say they lean towards
one party or the other) as partisans for all of the substantive analyses that follow. This
choice does not significantly affect the results.
12
Table 3: Symbolic Ideology and Citizen Ideal Points
Symbolic Ideology Mean Ideal Point
Very Liberal
-1.30
Liberal
-1.03
Moderate
-0.31
Conserative
.83
Very Conservative
1.34
Visualizing Legislators’ Positions & Constituent Voting
Do voters hold members of the U.S. House accountable for their roll call positions? As
a first-cut, we use a simple non-parametric framework. We separate our data into voterlegislator pairs, one for each combination of voter and legislator partisanship (DemocraticDemocrat, Independent-Democrat, Republican-Democrat and so on).11 For each pair, we
separate voters into five groups based on their ideology. In each of these categories, we
graph a loess curve of the percent voting for the incumbent across the range of incumbents’
ideal points (DW-Nominate scores).12 This is similar to simply graphing a point for each
category of voter ideology and each category of legislator ideology. Each of the panels in
Figure 1 subset our data based on respondent and legislator party identification. The second
row shows Democratic voters, the third row shows Independent voters, and the fourth row
shows Republican voters.
The theory of proximity voting based on the spatial distance between voters and candidates has a simple prediction: liberals should be more likely to vote for more liberal legislators
and conservatives should be more likely to vote for more conservative legislators. Moderates
should be more likely to vote for more moderate legislators. In other words, each of our lines
should have a slope representing the sensitivity of the vote choice to legislator opinion. In
the case of directional voting, the slope should be even steeper: as legislators go from the
11
All of the analyses that follow focus on contested races. But the results are the same if we analyze all
races.
12
All of the curves are weighted using respondents’ survey weights.
13
“wrong” side of the “neutral point” to the “right” one, the voters should switch en masse from
voting against them to for them. If the neutral point is between the two parties, then voters
should always vote for the party on their side of the neutral point (all lines should be at
100% or 0%), and voting should be completely determined by ideology, not party.
Looking first at the graphs for Democratic voters (top row), the most salient pattern is
that all of the curves are generally flat. Indeed, over 98% of liberal voters support Democratic
incumbents, and upwards of 90% oppose Republican incumbents, virtually regardless of the
legislators’ positions. Liberal Republicans are the only group in these graphs that appears to
be responsive to the position of legislators. For instance, there is roughly a 40% probability
that Liberal Republicans support liberal Democratic legislators, and a 75% chance that they
support the most moderate Democratic incumbents. However, we should keep in mind that
there are few liberal Republicans. This group represents only 2.3% of all Republicans in our
sample.
Next, we examine the graphs for Independent voters (2nd row). Several recent, prominent papers suggest that independents are highly responsive to legislators’ roll call positions
(Jessee, 2009, 2012; Shor and Rogowski, 2010). However, Figure 1 indicates there are only
very modest associations between the vote choices of independents and legislators’ roll call
positions in our data. Finally, the bottom row of Figure 1 shows the association between
legislators’ positions and constituents’ decisions on election day for Republican voters. The
plot shows that Republican voters are slightly more likely to support moderate Democratic
incumbents. However, there is no consistent association between the probability that Republican voters support Republican incumbents and the incumbents’ ideology. Over 97% of
Republicans support Republican incumbents, and over 90% oppose Democratic incumbents,
virtually regardless of the legislators’ positions.
Looking across the plots, a remarkable feature of these results is the strength of both respondents’ party and ideology as a predictor of vote choice. The effect of ideology is captured
by the differences in the levels of the lines within each panel, and the effect of party is cap-
14
tured by the differences in the lines going down the plots in each column of graphs. A cursory
glance shows that these effects are substantial. Even moderate Democrats overwhelmingly
support Democratic incumbents, and moderate Republicans overwhelmingly support Republican incumbents. These individuals have the same ideology and differ only in party
identification. However, individual ideology also has a substantial independent effect. For
instance, Democratic voters who are very conservative support Democratic incumbents less
than 60% of the time. Likewise, Republican voters who are very liberal support Republican
incumbents less than 65% of the time. Overall, Figure 1 indicates that the direct effects of
party and voter ideology dwarf the effect of legislator position. The difference in the levels
of the lines within and across panels is vastly greater than the difference in the slope of the
lines.
The fact that individual ideology has a strong independent effect on vote choice is not
evidence for the proximity model, because it contains no notion of distance. However, it
does provide evidence that party attachment may not be purely affective. If voters’ policy
positions drive the extent to which they reliably support their party, then the spatial distance
between the voter and the party is a sensible explanation. It may be the case that voters
think spatially with reference to parties, but not candidates.
15
Democratic Voter/Democratic Leg.
Pr(Vote for Incumbent)
Pr(Vote for Incumbent)
1.0
0.8
0.6
0.4
0.2
0.0
−0.6
−0.5
−0.4
−0.3
−0.2
1.0
Democratic Voter/Republican Leg.
0.8
0.6
0.4
0.2
0.0
−0.1
0.4
1.0
Independent Voter/Democratic Leg.
0.8
0.6
0.4
0.2
0.0
−0.6
−0.5
−0.4
−0.3
−0.2
1.0
Pr(Vote for Incumbent)
Pr(Vote for Incumbent)
0.4
0.2
0.0
−0.3
−0.2
0.9
0.4
0.2
0.0
0.5
0.6
0.7
0.8
0.9
Incumbent Position
0.6
−0.4
0.8
0.6
0.4
0.8
−0.5
0.7
0.8
−0.1
Republican Voter/Democratic Leg.
−0.6
0.6
Independent Voter/Republican Leg.
Incumbent Position
1.0
0.5
Incumbent Position
Pr(Vote for Incumbent)
Pr(Vote for Incumbent)
Incumbent Position
−0.1
1.0
Republican Voter/Republican Leg.
0.8
0.6
0.4
0.2
0.0
0.4
Incumbent Position
0.5
0.6
0.7
0.8
0.9
Incumbent Position
Figure 1: Spatial Voting in the U.S. House: 2006-2012 – This graph shows non-parametric loess
curves of the relationship between legislators’ DW-Nominate scores and the probability that respondents at various ideological levels support them on election-day. The y-axis is the probability
of voting for the incumbent and the x-axis is the incumbent’s DW-NOMINATE score. Each line
is a loess plot for a set of voters with a given quintile of ideology. The most liberal quintile is
shown using long-dashes, the next most liberal quintile is shown using dashes, moderate voters are
shown using a solid line, more conservative voters are shown using a dot-dash line, and the most
conservative voters are shown using a dotted line. The top row of the graph shows loess fits for
Democratic respondents, the second row is for Independent respondents, and the last row is for
Republican respondents. The first column is for Democratic legislators and the second column is
for Republican legislators.
16
Results: Legislators’ Positions and Constituent Voting
While our non-parametric analysis suggests little reason to believe that the roll call positions
of legislators influence voters decisions on election day, the link between the graphs and the
theoretical predictions are somewhat loose. To make a clearer connection between theory
and evidence, we next turn to our parametric framework.
Table 4 shows the results of a logistic regression model based on Equation 9. We control
for contest random effects (not shown). Each column of the table corresponds with a different
within-party specification. Column 1 shows the cases where all incumbents are Democrats
and all constituents are Democrats, column 2 shows the specification where all incumbents are
Democrats and all constituents are Independents, and so forth. Contrary to a purely affective
notion of party identification, the ideology of the voter is significant in every specification
in the predicted direction. More liberal voters are more likely to support Democrats and
more conservative voters are more likely to support Republicans. However, party has an
independent effect as well. The intercepts in columns 1 and 6 are substantially higher than
the models where the party of the legislator and the party of the voter do not match. As in
the non-parametric case, this is the biggest story our table has to tell: the partisanship and
ideology of voters matters.
Evidence in this table is very weak for the idea that citizens vote spatially based on their
proximity with individual legislators. Only one column, column 4, has significant coefficients
that correspond with Hypothesis 1. In particular, the key coefficient is the interaction
between legislator ideology and citizen ideology. This is the interaction that allows vote
to depend on the distance between citizens and legislators, not merely the position of the
citizen or legislator alone. This interaction is always substantively small, and only significant
in one out of our four cases: the case of Democratic constituents and Republican incumbents.
Instead, the coefficients for voters’ ideology are the most significant set of variables in each
regression, which lends further support to the notion that voters think spatially with reference
to parties rather than candidates.
17
Table 4: Proximity Behavior in Congressional Elections
Dependent variable:
Vote for Incumbent
(1)
(2)
(3)
(4)
(5)
(6)
Legislator:
Dem/
Dem/
Dem/
Rep/
Rep/
Rep/
Constituent:
Dem
Ind
Rep
Dem
Ind
Rep
Citizen Ideology
∗∗
0.83
(0.12)
∗∗
−1.62
(0.13)
−1.55
(0.18)
−1.33
(0.10)
1.19
(0.20)
1.30∗∗
(0.15)
Citizen Ideology Sq.
−0.11∗∗
(0.04)
−0.03
(0.05)
0.05
(0.03)
−0.06∗∗
(0.03)
0.05
(0.04)
−0.02
(0.03)
Legislator Ideology
−0.31
(1.84)
0.51
(2.16)
1.42
(1.69)
−4.33∗∗
(1.60)
1.56
(2.41)
−1.62
(2.03)
Legislator Ideology Sq.
1.48
(2.68)
−0.53
(3.10)
−0.69
(2.47)
2.74∗∗
(1.28)
−1.77
(1.92)
1.66
(1.63)
Leg. Dem x Citizen Ideology
−0.27
(0.36)
0.04
(0.47)
0.22
(0.28)
0.39∗∗
(0.19)
0.19
(0.32)
0.04
(0.25)
(Intercept)
2.11∗∗
(0.30)
0.76∗∗
(0.36)
−0.55∗
(0.28)
0.69
(0.48)
0.20
(0.73)
2.53∗∗
(0.61)
18,723
−2,422.36
4,858.72
4,913.58
3,105
−1,276.93
2,567.87
2,610.15
14,798
−3,398.16
6,810.32
6,863.54
16,296
−5,178.87
10,371.73
10,425.62
3,588
−1,572.98
3,159.95
3,203.25
21,285
−2,822.45
5,658.89
5,714.65
Observations
Log Likelihood
Akaike Inf. Crit.
Bayesian Inf. Crit.
∗∗
∗∗
∗∗
∗
p<0.1; ∗∗ p<0.05
Note: Coefficients are from the logistic regression model in equation 9, plus controls for contest-specific
random effects (not shown). Standard errors in parentheses. Each column restricts the set of observations
to only constituents and incumbents of a particular party. So column 1 is Democratic constituents who are
voting on Democratic incumbents and so on. The unit of observation is the constituent or citizen.
18
The Effect of Incumbent Positioning on Constituting Voting
It is difficult to interpret the magnitude of the effect of the spatial distance between voters and
candidates in our analysis, because distance is embedded in the specification but not included
as a single coefficient. In order to examine the effects of distance directly, we calculate the
predicted change in the probability that voters in a given group would support a legislator
that moderated their ideal point by one standard deviation. Table 5 shows these results. The
left side of the table shows the results for Democratic incumbents, and the right side of the
table shows the results for Republican incumbents. Each column shows the ideology of the
constituent, split into three equal groups (Liberal, Moderate, and Conservative) and each row
shows the party of the constituent. So for instance, for liberal Democrats with Democratic
incumbents, a one standard deviation move to the right by the incumbent is associated with
a -0.1 percentage point change in their probability of voting for the incumbent. This is a
percentage point change, so the vote probability is reduced by 0.1% out of 100%.
The top row of Table 5 provides a theoretical prediction based on spatial voting theory
for each group. We assume that moderate and conservative members of the public are to
the right of Democrats in the House of Representatives, and that liberals and moderates are
to the left of their Republican legislators. If this assumption is correct, then a one standard
deviation move towards the middle should always be a move closer to these constituents, and
therefore it should increase their probability of voting for the incumbent. This is reflected in
the theoretical prediction in the top row: for these rows, predicted changes in vote probability
should be positive. For liberals who have a Democratic incumbent and conservatives who
have a Republican incumbent, we are less certain of whom is to the right or left of whom,
and so our theoretical prediction is ambiguous.
Evidence for the candidate-centered spatial voting model is very weak. Of the twelve
cases where we have a theoretical prediction, the theoretical prediction of spatial voting
theory (H1) is only born out significantly in five. Of these cases, the greatest change in vote
probability is 5.4%, for moderate Republicans voting on a Democrat. The rest are smaller
19
than 3%. In one case, the coefficient is negative and significant. Indeed, moderate Democrats
appear to prefer liberal Democratic incumbents. Also, note that half of the estimated effects
across the entire table are less than 1% in magnitude.
Democratic Incumbent
Liberal voter Mod. voter Conserv. voter
Candidate-Centered
Theoretical
Prediction (H1)
Republican Incumbent
Liberal voter Mod. voter Conserv. voter
ambiguous
+
+
+
+
ambiguous
Democratic voter
-0.09%**
(.04)
-1.14%**
(.26)
-5.34%
(3.34)
1.10**
(.16)
3.18%**
(.80)
0.66%
(1.96)
Independent voter
.39%
(.31)
2.74%*
(1.57)
.76%
(.50)
1.85%
(1.26)
2.45%
(1.54)
0.12%
(.32)
Republican voter
2.87%*
(1.60)
5.36%**
(1.14)
0.72%**
(.12)
-1.20%
(3.46)
-0.68%
(.51)
-0.07%
(.06)
Table 5: Effect of incumbent moderating their ideal point by one standard deviation on the probability that citizens vote for the incumbent in congressional elections. Effects that are significant
at the .05 level are shown with two stars and effects that are significant at the .1 level are shown
with one star.
Two objections that may arise to this analysis are that (a) our results may be be driven by
the fact that we focus on overall ideological scores rather than important votes in Congress,
and (b) our results may be driven by districts where incumbents face weak or no challengers.
In Appendix B, we show that our substantive results are similar for important votes, and in
Appendix C we show that our results are similar in competitive and uncompetitive districts.
Finally, all our results are substantively similar using both static and dynamic ideal point
estimates (e.g., DW-Nominate vs W-Nominate scores).
Constituent Perceptions of Roll Call Positions
Spatial voting is a theoretically and intutively appealing idea that has motivated a wide body
of work in political science for decades. Why don’t voters make choices on the basis of policy
20
proximity with individual legislators? One answer comes from seminal research on representation in the 1960s, which found that citizens have only vague notions of legislators’ roll
call positions (Miller and Stokes, 1963). For instance, only 56 percent of ANES respondents
correctly identified their representative’s position on the resolution in 1991 authorizing the
first President Bush to conduct the Persian Gulf War (Alvarez and Gronke, 1996). Moreover,
only 63 percent of ANES respondents correctly identified their representatives’ votes on the
budget-balancing Budget Resolution of 1993 (Lipinski, 2001). However, more recent research
suggests that most contemporary voters may have accurate perceptions of legislators’ roll
call positions (Ansolabehere and Jones, 2010). To our knowledge, though, no study has
systematically evaluated how well citizens know the ideology of members of Congress using
a large sample of voters and legislators.
We take two approaches to examining whether citizens understand legislators’ roll call
positions. First, we examine the relationship between constituents’ perception of legislators’
symbolic ideology on a 7-point scale and their actual roll call positions (top-panel of Figure 2).
We have data for over 150,000 Americans’ perceptions of their legislators’ ideology from the
2006, 2008, 2010, and 2012 CCES. We find that citizens are very capable of differentiating
between Democrats and Republicans. Overall, there is a correlation of .71 between the
perceived ideology of legislators and their actual DW-Nominate scores. However, our findings
suggest that while voters are capable of differentiating between parties, they are less capable
of differentiating within parties. When we subset the data by legislators’ party, there is
only a correlation of .14 between the perceived ideology of Democratic legislators and their
actual DW-NOMINATE scores and an even weaker correlation of .09 between the perceived
ideology of Republican legislators and their actual DW-Nominate scores.
One possible explanation for the weak within-party correlations between actual and perceived legislator ideologies we observe is that the 7-point scale on the CCES is not granular
enough to distinguish legislators within each party. For instance, it’s possible that most
Democrats are a “2” or a “3”. To examine this possibility, we re-examined data on voters’
21
Perceived Legislator Ideology
7
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−0.5
0.0
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1.0
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Legislator Position (8 Roll Calls in CCES)
1
Figure 2: Perceived vs. Actual Ideal Points – The top panel in this graph shows the relationship
between the perceived ideology of House members on a 7-point scale and their actual ideal points.
The grey region in the middle of the top panel shows the ideological space where few legislators
reside. The bottom panel shows the relationship between the perceived ideology of House members
based on respondents’ perceptions of their votes on eight salient roll calls and their actual ideal
points based on their votes on these bills.
perceptions of legislators’ positions on eight roll call votes from the 2006 CCES (Ansolabehere
and Jones, 2010).13 We scaled both the perceived and actual positions of legislators on these
eight votes using an IRT model (Clinton, Jackman, and Rivers, 2004).14
The bottom panel of Figure 2 shows the results of this analysis. There is a clear rela13
The 2006 CCES asked about voters’ perceptions of legislators’ roll-call votes on (1) a ban on partial-birth
abortion, (2) federal funding for stem cell research, (3) extending capital gains tax cuts, (4) ratifying CAFTA,
(5) immigration reform, (6) bankruptcy reform, (7) tax breaks for energy companies, and (8) reauthorizing
the Patriot Act. See Ansolabehere and Jones (2010) for more details.
14
Note that given that we only have eight items in this scale, there is probably significant measurement
error in our estimates of both perceived and actual legislator positions. However, the measures of actual
positions are correlated with legislators’ DW-Nominate scores at .90.
22
tionship between the perceived and actual ideal points of legislators on these eight roll calls.
However, there is a considerable degree of error in respondents’ perceptions of legislator positions. The overall correlation between the perceived and actual positions of legislators on
these eight roll calls is .66. When we subset the data by legislators’ party, however, there
is only a correlation of .28 between the perceived positions of Democratic legislators and
their actual ideal points on these roll calls and a correlation of .10 for Republican legislators.
We should note that these 8 items distinguish more effectively between Democrats than
Republicans, possibly explaining the difference between these two correlations.
Overall, the evidence suggests that most voters understand the party label of their representative and they can effectively differentiate between Democrats and Republicans in
Congress. But voters only have a dim awareness of ideological differences between legislators
within each party. Condorcet’s jury theorem shows that sometimes only a dim awareness is
necessary for voters to make good decisions under some circumstances (Condorcet, 1785),
and it has been shown that these dim perceptions are good enough on average that political
scientists can use them to identify the actual positions of legislators (Aldrich and McKelvey,
1977; Ramey, 2014). Nonetheless, each individual is only slightly better than a coin flip at
telling whether one Democrat is more or less liberal than another. Even if individuals are trying to make proximity-based judgements, lack of knowledge adds noise to individual spatial
decision making, to the point where it may be easily swamped by other considerations.
Does Political Knowledge Affect the Relationship Between Legislators’ Positions and Constituent Voting?
Maybe, given the results in the last section, we should not expect most voters to vote based
on legislators’ spatial positions. Perhaps this requires too much information or cognitive capacity. Indeed, several previous studies have found evidence that politically knowledgeable
citizens are much more likely to vote based on legislators’ spatial positions than low information citizens (Jessee, 2009, 2012; Shor and Rogowski, 2010). To examine this possibility, we
23
used a battery of political knowledge questions to examine whether high information voters
were more likely to punish legislators that took extreme positions than low-information voters. We scaled the political knowledge of respondents to the 2006, 2008, 2010, and 2012 Cooperative Congressional Election Studies (CCES) using responses to eight knowledge items.15
On the basis of respondents’ answers, we characterized their political knowledge as “high”,
“medium”, and “low” (Shor and Rogowski, 2010). Not surprisingly, this measure is highly
correlated with respondents’ education levels.
Next, we ran models like the ones in Table 4 for voters at low and high levels of political
information. We used these models to predict the changes in the probability of voting for the
incumbent that would result from a one standard deviation move in the moderate direction
by Democratic and Republican legislators. The results are shown in Table 6.
Table 6 is laid out exactly as in Table 5, but with with an upper half for low information
voters and a lower half for high information voters. As in Table 5, many of the coefficients do
not bear out the predictions of the model of spatial voting based on candidate positions. This
is particularly true for low information voters. Only three cases are significantly positive,
and two are significantly negative. However, for high information voters the evidence is
somewhat more mixed. No cases that are predicted to be positive are significantly negative,
and seven out of twelve cases that are predicted to be positive are significantly so, some
with substantively large effects. High information voters may be somewhat attuned to the
policy distance between their own preferred policies and the votes cast by their incumbent
legislators.
15
To scale political knowledge, we use items on respondents’ knowledge about the majority party in the
House, the majority party in the Senate, the major party in their state house, and majority party in their
state senate, the party of their governor, the party of their incumbent representative, and the party of their
incumbent senators.
24
Democratic Incumbent
Liberal voter Mod. voter Conserv. voter
Candidate-Centered
Theoretical
Prediction (H1)
Low Information
Voters
Democratic voter
Republican Incumbent
Liberal voter Mod. voter Conserv. voter
ambiguous
+
+
+
+
ambiguous
-0.39%**
(.08)
-2.0%**
(.32)
-13.68%*
(8.09)
2.67%**
(.66)
3.47%
(1.23)
-2.44%
(6.11)
Independent voter
-0.27%
(0.83)
1.84%
(2.62)
2.61%
(2.03)
2.73%
(6.56)
4.29%
(3.02)
1.38%
(2.01)
Republican voter
14.97%*
(8.87)
7.05%**
(2.37)
-0.5%
(.84)
4.59%
(5.90)
-0.78%
(.44)
-0.52%**
(.12)
0.02%**
(0.01)
0.25%
(0.35)
-0.04%
(2.27)
0.34%**
(0.09)
3.87%**
(1.06)
6.03%*
(3.07)
Independent voter
.93%**
(0.23)
4.46%
(4.97)
-0.98%*
(0.51)
1.66%**
(0.56)
3.26%
(3.30)
-0.13%
(0.09)
Republican voter
2.34%*
(1.35)
7.43%**
(2.33)
0.54%**
(0.08)
1.15%
(3.41)
0.78%
(0.97)
0.05%
(0.04)
High Information
Voters
Democratic voter
Table 6: Effect of incumbent moderating their ideal point by one standard deviation on the probability that citizens vote for the incumbent in congressional elections. Effects that are significant
at the .05 level are shown with two stars and effects that are significant at the .1 level are shown
with one star.
Extrapolating Legislator Vote Shares
Having examined what these results mean for theories of electoral accountability and spatial
voting, what do they imply for representation in American politics? Our model enables
us to examine the relationship between legislator vote shares and legislator positions (cf.
Canes-Wrone, Brady, and Cogan, 2002; Wilkins, 2012). It may be the case, for instance,
that some small subset of the public votes according to the spatial theory of voting. If so,
this nonetheless will have very small incentive effects for legislators. In order to test this,
we simulate vote shares for each legislator from the sample of their actual electorate in our
25
dataset using a model derived from the models presented in Table 4.16 We produce predicted
vote shares for each district at different values of incumbent DW-NOMINATE scores.
Figure 3 show the results of our exercise in predicting vote shares. For each district, we
calculate the change in vote share that would result from a one standard deviation move
toward the center by the legislator. The left panel shows the kernel density plot of predicted
changes in vote share for all districts represented by Democrats, and the right panel shows this
density for all districts represented by Republicans. The mean and mode of these densities
are very close to 0. In every case, Democrats are projected to change their voteshares by
less than 3%, and in the large plurality less than 3%. These changes are only a little more
likely to be increases than decreases. Likewise, in every case Republicans would increase
their voteshares by less than 4%, and overwhelmingly these changes are less than 2%. Once
again, negative changes are common. The cumulative effect of our model of individual-level
voting is that legislator positions appear to have very small associations with their vote
shares. Far more important drivers of vote shares are the legislator’s party and the partisan
and ideological composition of the district.
Conclusion
The Founding Fathers thought that frequent elections were the key mechanism for ensuring
that the “will of the people” is carried out. This electoral connection provides the foundation
for the study of congressional behavior and lawmaking and for theories of representation
more broadly. A number of recent studies have provided an empirical foundation for the
assumption that voters hold their representatives accountable at the ballot box for their
roll call voting behavior (Ansolabehere and Jones, 2010; Jessee, 2009; Jones, 2011; Shor and
Rogowski, 2010). Yet these studies are puzzling considering the polarization and lack of
16
We used a combined, fully interactive version of the model where each model in Table 4 is embedded
in one specification. This is substantively the same as using each of these models separately. Note that due
to our large sample of voters’ ideal points, we have an average of roughly 350 people in every congressional
district.
26
50
60
40
30
40
20
20
10
0
0
−0.02
−0.01
0.00
0.01
Democrats' Vote Share Gain
0.02
−0.04
−0.02
0.00
0.02
Republicans' Vote Share Gain
0.04
Figure 3: Relationship between Representatives’ Ideal Point and Expected Vote Share in the U.S.
House: 2006-2012 – This graph shows the distribution of potential vote gains from legislators that
moderate their position by one standard deviation. The left column
responsiveness in the contemporary Congress.
In this study, we provide an individual-level explanation for the lack of support for the
spatial voting model’s core predictions in the modern House of Representatives. We find
that voters’ policy preferences are highly predictive of which party they will support: liberal
voters almost always support Democrats and conservative voters almost always support
Republicans. Unlike previous survey-based studies, however, we find that there is a negligible
association between legislators’ roll call voting behavior and the probability that most citizens
will support them on election-day. Indeed, most low knowledge voters do not take legislators’
voting position into account at all. We find that only high knowledge voters who are moderate
or cross-pressured take legislators’ positions into account in the ballot box, and that even
27
then the effect is small. This precludes a major role for all proximity-based accounts of voter
choice based on candidate positions, including both the classic spatial model as well as newer
variants of proximity voting models such as directional voting. It also provides few incentives
for legislators to take ideologically moderate positions in Congress.
We posit an explanation for our results grounded in the original applications of spatial
voting theory. While voters have difficulty differentiating liberal Democrats from moderate
Democrats, and conservative Republicans from moderate Republicans, they are quite capable
of distinguishing between the parties. They may be able to roughly observe the proximity of
their own desired policies to the policies supported by each party, and vote accordingly. This
party-centered notion of spatial voting is supported by our individual level evidence. Voter
ideology does appear to matter, it is just the individual incumbent’s ideal point which has
very little predictive power. Proximity to the candidate plays a tiny role in voter decisions,
if any, but proximity to the parties may well explain the importance of voter ideology.
While our findings clearly contradict candidate-centered accounts of spatial voting in
congressional elections (e.g., Ansolabehere and Jones, 2010; Nyhan et al., 2012; Shor and
Rogowski, 2010),17 they do not fit neatly into the traditional framework of party identification. Voters’ policy positions appear to be a strong driver of party loyalty in U.S. House
elections. This finding contrasts with the traditional view of party identification as primarily
affective. Our findings are consistent with work that attempts to incorporate spatial voting
in the context of party reputations (e.g., Sniderman and Stiglitz, 2012). This perspective
suggests that legislators have strong electoral incentives to work to maintain the brand of
their party (Aldrich, 1995; Cox and McCubbins, 2005). Indeed, our results suggest that
voters punish parties that grow out-of-step with their constituents (Kim and LeVeck, 2013).
The electoral connection in Congress may be alive and well, but at the level of parties rather
than individual legislators. Unlike the candidate-centered view, in this view the incentives
of individual legislators will often conflict with the incentives of parties.
17
It is worth noting however, that while the median voter theorem is strictly a phenomena of two-candidate
elections, Downs (1957) formulated his spatial voting theory in terms of parties, not individual candidates.
28
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33
Online Appendix A: Models of Spatial Voting
In this section, we elucidate the mathematical relationship between the logistic regression
equation estimated in the paper and the theory of spatial voting. Assume that voters have
quadratic utility functions over the distance between their ideal policy and the ideal policy
of the legislator they choose to vote for. Let xi be the position of voter i, rj be the position
of the incumbent representative in contest j, and cj be the position of the challenger. For
simplicity, we begin by assuming that cj takes on values as follows:
cj =



 µR if rj < 0
(11)


 µD if rj ≥ 0
Where µR is the position of the mean Republican and µD is the position of the mean
Democrat. In other words, for the purposes of this example, voters assume that the challenger
takes the mean position of the non-incumbent party.18
Let yij be voter i’s vote in contest j, where y is 1 if i votes for the incumbent and 0
otherwise. Voter i’s utility for voting for the incumbent and challenger is as follows:
uij (yij = 1) = −(xi − rj )2 + vj + ij
uij (yij = 0) = −(xi − cj )2
(12)
In this model, vj represents the valence attached to the incumbent, which we assume to
be a function of the correspondence between the partisan identification of legislators and
citizens. This allows us to capture the potential that voters may make judgments based on
18
We empirically examine this assumption using estimates of candidate positions derived from campaign
contributions (Bonica, 2013). We find that there is no correlation between the positions of candidates and
the positions of incumbents. Moreover, there is only a modest correlation between the positions of candidates
and the median voter in their district. Instead, consistent with our model, candidates’ party identification
is the most important predictor of their positions.
34
party rather than position. Finally, ij is an independent random shock to the utility for the
incumbent. For simplicity, we assume that utility for the challenger is errorless.
Assume that ij is standard logistically distributed. Then the probability of voter i voting
for the incumbent in contest j is:
P (yij = 1) = Λ((xi − rj )2 − (xi − cj )2 + vj )
= Λ(−2xi rj + rj2 + 2xi cj − c2j + vj )
(13)
(14)
Here Λ() is the cumulative standard logistic distribution function. Let β0∗ = −c2j , β1∗ =
2cj , β2∗ = 1 and β3∗ = −2. Then:
P (yij = 1) = Λ(β0∗ + β1∗ xi + β2∗ rj2 + β3∗ xi rj + β4∗ vj )
(15)
We do not assume that rj is in the same space as xi . Instead, we assume that these
spaces are linear transformations of one another, such that δxi + γ resides in the space of rj .
Then:
P (yij = 1) = Λ(β0∗ + β1∗ δxi + β1∗ γ + β2∗ rj2 + β3∗ δxi rj + β3∗ γrj + β4∗ vj + β5∗ vj )
(16)
We do not know a priori the values of δ and γ, so an empirical specification will be
consistent with any value for these variables. Let β0 = β0∗ + β1∗ γ, β1 = β1∗ δ, β2 = β2∗ ,
β3 = β3∗ δ and β4 = β3∗ γ. Then we have :
P (yij = 1) = Λ(β0 + β1 xi + β2 rj2 + β3 xi rj + β4 rj + β5∗ vj )
This simple logit equation includes equation 13 as a special case.
35
(17)
In the above model, we motivated our empirical specification by using the canonical
quadratic utility spatial voting model with fixed partisan challengers. However, there are
many other possible utility functions and many other possible challenger position functions.
Moreover, the findings in the paper can be made consistent with most types of spatial voting,
but only if voters display very high levels of indifference or low levels of acuity. Below we flesh
out the implications of some alternative models of voter decisionmaking and how they may
or may not be consistent with our findings. None of these alternatives can explain away the
basic finding that voter decisionmaking is only weakly contingent on incumbent positions.
Challenger Positioning
In the above model, we assumed that challengers adopt the following positions:


 µR if rj < 0
cj =

 µD if rj ≥ 0
Empirically, the relative positioning of challengers and incumbents is not well established.
Moreover, voters may view the challenger differently than they view the incumbent. One
possibility is that voters consider the challenger to be an unknown, voting retrospectively
based on the position of the incumbent only. In this case, utility for the challenger would be
fixed:
uij (yij = 0) = c
Where c is just a fixed utility for an “unknown quantity.” The resulting vote generating
function is:
P (yij = 1) = Λ((xi − rj )2 − c)
= Λ(x2i − 2xi rj + rj2 − c)
36
In this case, the x2i do not cancel. For this reason, we included x2i in our empirical specification, and this formulation is encapsulated by the model we estimated.
The “fixed-utility challenger” model assumes voters that are less sophisticated than the
“average partisan challenger” model that we used above. Under fixed challenger utility, voters
do not seem to be cognisant of the fact that challengers in general elections typically come
from the opposite party, and may represent positions that are highly dissonant with voter
preferences. Another alternative is to assume that voters are even smarter than the voters
in the “average partisan challenger” model, and that they know the exact position of the
challenger. In the absence of good information about challenger positions, we then need
to specify a positioning function for the challenger. A simple and plausible choice is that
challengers emerge as a random draw from the existing party. More specifically:


 N (µR , σR ) if rj < 0
cj =

 N (µD , σD ) if rj ≥ 0
In order to understand how this candidate generating function would affect our estimates,
we first simulate data from our original data generating function for comparison, separated
by legislator party. Rather than randomly generating candidates and voters, we use the
legislators and voters in our actual data- this ensures our simulations have the same statistical
power as our actual analysis.19 We z-score the positions of legislators and voters, and generate
data from the following vote generating function:
P (yij = 1) = Λ((xi − rj )2 − (xi − cj )2 )
= Λ(−2xi rj + rj2 + 2xi cj − c2j )
In the “random challenger” model, c is generated as above. We begin by simulating vote
probabilities from the standard model. The results are shown in Figure 4.
19
In particular, it would be very useful if we could randomly assign a different incumbent ideal point for
each voter, since this would give us much more variation in legislator ideal points than we actually have in
our data
37
Democratic Incumbents
Republican Incumbents
1.00
Projected Vote Share
0.75
0.50
0.25
0.00
−1.5
−1.2
−0.9
Incumbent Position
−0.6
0.6
0.9
1.2
Incumbent Position
1.5
Figure 4: Simulated Vote For The Data Generating Process Used in the Paper: The y-axis is
probability of voting for the incumbent, and the x-axis is the incumbent’s ideal point. Probabilities
are generated by a simulated quadratic utility model. Each line represents the vote probability for
a set of voters with a given ideology. Voter ideology ranges from -1.5 to 1.5, with voters at -1.5
in dark blue and voters at 1.5 in dark red. Each line in between represents one of 11 separate
categories of ideology, colored by closeness to 1.5 or -1.5.
Figure 5 shows a simulation based on the random-challenger model. The results are almost exactly identical to the results of the non-random challenger model. More importantly,
when we flip this process around and estimate our model on the simulated votes, we recover
almost exactly the correct estimates, thanks to the large size of our sample. In other words,
the random-challenger alternative is encapsulated by the model we used in the body of the
paper.
So far we have assumed that challenger positions do not depend on the position adopted
by the incumbent, but this may not be true. Consider an extreme case:
cj = −rj
38
Democratic Incumbents
Republican Incumbents
1.00
Projected Vote Share
0.75
0.50
0.25
0.00
−1.5
−1.2
−0.9
Incumbent Position
−0.6
0.6
0.9
1.2
Incumbent Position
1.5
Figure 5: Simulated Vote For The Data Generating Process Used with Random Challengers: The
y-axis is probability of voting for the incumbent, and the x-axis is the incumbent’s ideal point.
Probabilities are generated by a simulated quadratic utility model. Each line represents the vote
probability for a set of voters with a given ideology. Voter ideology ranges from -1.5 to 1.5, with
voters at -1.5 in dark blue and voters at 1.5 in dark red. Each line in between represents one of 11
separate categories of ideology, colored by closeness to 1.5 or -1.5.
Further assume that votes are generated by quadratic utility as follows:
P (yij = 1) = Λ((xi − rj )2 − (xi − cj )2 )
= Λ((xi − rj )2 − (xi + rj )2 )
substituting −rj for cj
= Λ(−4xi rj )
This possibility is encapsulated by our model, and contradicted by our results. In fact, it
can be shown that there is only one challenger strategy that will induce indifference over rj
for all x. Consider values of c that would satisfy:
(x − r)2 − (x − c)2 = 0
39
Doing the algebra:
(x − c)2 = (x − r)2
p
x − c = (x − r)2
p
c = x − (x − r)2
So c is either equal to r or equal to 2x − r. Since c cannot be equal to 2x − r for all x,
the unique solution that does not depend on x is c = r. In other words, the only way that
candidate strategy can induce spatial voters to vote independently of r for all possible voter
positions is for the challenger to always adopt the position of the incumbent. This strategy
is starkly contradicted by the lack of convergence in actual House elections.
Alternate Utility Functions
In the above model, we rely on quadratic utility as a theoretical motivation. However, there
are many other possibilities. Probably the most popular alternative is the normal utility
model. Normal utility has very different implications from quadratic utility for voter decisionmaking. If voter utility is normal, then very extreme voters may be closer to indifferent
between candidates than the average partisan is. This is due to the flat tails of the normal
function.
A normal utility model can be specified as follows:
1
uij (yij = 1) = exp(− (xi − rj )2 ) + ij
2
Where ij is an independent random shock to the utility for the incumbent. Once again, if
we assume that utility for the challenger is errorless:
1
uij (yij = 0) = exp(− (xi − cj )2 )
2
40
Asuming ij is distributed standard normal, the probability of voting for the incumbent is
equal to:
1
1
P (yij = 1) = Φ(exp(− (xi − rj )2 ) − exp(− (xi − cj )2 ))
2
2
Unlike the many possible assumptions about challenger location, not all data generating
processes based on normal utility are encapsulated by the empirical specification we use in
our parametric analysis in the main body of the paper. However, the kinds of voting curves
generated by normal utility are starkly inconsistent with our non-parametric results.
As an example that is not too far from what our empirical specification can estimate,
take the data generating function just considered, with challengers specified as in beginning
of this appendix. As above, we use the data from our paper to simulate vote probabilities,
but z-score the ideology measures. The result is shown in Figure 6.
Democratic Incumbents
1.00
Republican Incumbents
Projected Vote Share
0.75
0.50
0.25
0.00
−1.5
−1.2
−0.9
Incumbent Position
−0.6
−1.5
−1.2
−0.9
Incumbent Position
−0.6
Figure 6: Simulated Vote For The Incumbent Under Normal Utility: The y-axis is probability of
voting for the incumbent, and the x-axis is the incumbent’s ideal point. Probabilities are generated
by a simulated normal utility model. Each line represents the vote probability for a set of voters
with a given ideology. Voter ideology ranges from -1.5 to 1.5, with voters at -1.5 in dark blue and
voters at 1.5 in dark red. Each line in between represents one of 11 separate categories of ideology,
colored by closeness to 1.5 or -1.5.
Figure 6 is similar to some of the quadratic utility specifications that are encapsulated in
41
our model, particularly for voters in the middle of the ideological spectrum. However, due
to greater indifference at the extremes, extreme voters are “bunched” at the top and bottom
of the graph in a way that is not consistent with quadratic utility, although not far off.
In order to simulate a situation that is much more inconsistent with quadratic utility,
consider the following “high acuity” normal utility vote generating process:
1
1
P (yij = 1) = Φ(exp(− δ(xi − rj )2 ) − exp(− δ(xi − cj )2 ))
2
2
Here δ is an arbitrary multiplier. δ greater than 1 produces greater utility sensitivity to
different distances between x, r, and c. Hence the term “high acuity.” Figure 7 shows the
effect of simulating this model when δ = 2.
Democratic Incumbents
Republican Incumbents
1.00
Projected Vote Share
0.75
0.50
0.25
0.00
−1.5
−1.2
−0.9
Incumbent Position
−0.6
−1.5
−1.2
−0.9
Incumbent Position
−0.6
Figure 7: Simulated Vote For The Incumbent Under “High Acuity” Normal Utility: The y-axis is
probability of voting for the incumbent, and the x-axis is the incumbent’s ideal point. Probabilities
are generated by a simulated normal utility model with a “high acuity” multiplier. Each line
represents the vote probability for a set of voters with a given ideology. Voter ideology ranges from
-1.5 to 1.5, with voters at -1.5 in dark blue and voters at 1.5 in dark red. Each line in between
represents one of 11 separate categories of ideology, colored by closeness to 1.5 or -1.5.
The striking feature of Figure 7 is that the most conservative and most liberal voters
are actually more likely to cross party lines than more moderate voters. This is starkly
inconsistent with our non-parametric results. More generally, neither version of normal
42
utility simulated here is at all close to the results we present in the body of the paper.
Low Acuity or High Indifference
There is one way we can think of in which spatial voting can be reconciled with the results
presented in this paper. That is, if voters vote spatially, but with very low sensitivity to
legislator positions. This low sensitivity could be part of the utility function: voters may
merely be indifferent between many different possible positions. It could also be caused by
large errors, or an inability to distinguish different positions taken by legislators from one
another.
For instance, one possibility is that voters vote on the basis of their own ideology, but
also have a weak spatial voting component to their utility. Say for instance that votes are
generated by the following process:
1
1
(xi − rj )2 − (xi − cj )2 )
10
10
1
1 2 1
1
= Λ(xi Ia − xi (1 − Ia ) − xi rj + rj + xi cj − c2j )
5
10
5
10
P (yij = 1) = Λ(xi Ia − xi (1 − Ia ) +
Where Ia is an indicator for whether xi and rj have the same sign. The direct effect of xi
is a much larger component of this process than the effect of utility differentials between
challengers and incumbents.
Figure 8 shows the result of simulating this data generating process. The graphs are not
unlike the results in our paper, with big differences between voters at different positions,
and very little slope in the lines. This should not come as too much of a surprise, since the
parameters we have specified are similar to the parameters we estimate in the paper. The
point is that spatial voting is occuring in this example, it just plays a relatively small role
in utility. Therefore our results do not disprove the theory of spatial voting per se.
43
Democratic Incumbents
Republican Incumbents
1.00
Projected Vote Share
0.75
0.50
0.25
0.00
−1.5
−1.2
−0.9
Incumbent Position
−0.6
−1.5
−1.2
−0.9
Incumbent Position
−0.6
Figure 8: Simulated Vote For The Incumbent Under Very Low Acuity Quadratic Utility: The
y-axis is probability of voting for the incumbent, and the x-axis is the incumbent’s ideal point.
Probabilities are generated by a simulated quadratic utility model with a large direct effect of
ideology and a very small utility difference for spatial proximity. Each line represents the vote
probability for a set of voters with a given ideology. Voter ideology ranges from -1.5 to 1.5, with
voters at -1.5 in dark blue and voters at 1.5 in dark red. Each line in between represents one of 11
separate categories of ideology, colored by closeness to 1.5 or -1.5.
44
Online Appendix B: Electoral Accountability on Important Votes
The evidence in the body of the paper indicates that there is a very modest association
between the roll call positions of legislators and most citizens’ votes. However, it is possible
that we are looking in the wrong place. Perhaps there is a greater association between
legislators’ roll call positions and citizens’ decisions at the ballot box for roll call votes on
important roll calls. For instance, a number of recent papers suggest that Democrats lost
their majority in 2010 because of their votes on salient roll calls such as the Affordable Care
Act and the American Clean Energy and Security Act (Brady, Fiorina, and Wilkins, 2011;
Nyhan et al., 2012).
We examine this hypotheses using data on “Key Votes” from the Congressional Quarterly
to scale legislators’ ideal points on important votes using Simon Jackman’s IDEAL package
in R. There are between 20-30 Key Votes in each Congress (Table 7). The overall correlation
between these ideal points on important votes and DW-Nominate scores is approximately .9.
However, there is a weaker correlation with legislators’ Nominate scores within each party.
In any given year, the within-party correlation between legislators’ scaled roll call positions
on important votes and DW-Nominate scores varies between 0.3 and 0.7.
Congress
108
109
110
111
112
Key Votes
23
25
23
27
24
Table 7: Number of CQ Key Votes by Year
Next, we examine whether the roll call positions of legislators on important votes affect
whether survey respondents supported their incumbent legislator on election day. Similarly to
our analysis in the previous section, we find only limited evidence for electoral accountability
45
for roll call positions. Table 8 shows the implied change in the probability that constituents
will support a legislator that moderates their position by roughly one standard deviation
on important roll call votes. The figure shows that there is a relatively modest relationship
between the roll call positions of legislators on important votes and citizens’ vote choice.
More importantly, only six of twelve coefficients are statistically significant in the direction
predicted by spatial voting theory. Moreover, four coefficients are statistically significant in
the opposite direction predicted by spatial voting theory. As a result, there is no evidence
to support the hypothesis that voters hold legislators more accountable on important votes
than on other votes.
Democratic Incumbent
Liberal voter Mod. voter Conserv. voter
Candidate-Centered
Theoretical
Prediction (H1)
Republican Incumbent
Liberal voter Mod. voter Conserv. voter
ambiguous
+
+
+
+
ambiguous
Democratic voter
2.56%**
(.07)
-0.22%
(.37)
-27.03%**
(3.28)
-19.91**
(.29)
-2.21%*
(1.17)
21.97%**
(2.10)
Independent voter
-.37%
(.24)
2.51%**
(1.80)
1.97%**
(0.41)
-16.71%**
(1.28)
-1.31%
(2.01)
5.52%**
(0.28)
Republican voter
-3.25%*
(1.77)
2.64%*
(1.45)
1.32%**
(0.14)
9.09%**
(3.00)
1.92%**
(0.54)
-0.23%**
(0.04)
Table 8: Effect of incumbent moderating their ideal point by one standard deviation on important
votes on the probability that citizens vote for the incumbent in congressional elections. Effects that
are significant at the .05 level are shown with two stars and effects that are significant at the .1
level are shown with one star.
46
Online Appendix C: Electoral Accountability in Competitive Districts
Another possibility is that competitive elections are required to facilitate democratic accountability (Griffin, 2006). This hypothesis implies that we should see strong evidence of
spatial voting in electorally competitive districts. To examine whether voters hold legislators
accountable for their roll call positions in electorally competitive districts, we first narrow
the range of districts in our analysis using data from Bonica (2013). We define competitive
districts as those where the challenger raised at least $500,000 in campaign contributions.
Note, however, that our findings are not sensitive to our definition of competitive districts.
We find similar results using other definitions of competitive districts.
Democratic Incumbent
Liberal voter Mod. voter Conserv. voter
Candidate-Centered
Theoretical
Prediction (H1)
Republican Incumbent
Liberal voter Mod. voter Conserv. voter
ambiguous
+
+
+
+
ambiguous
Democratic voter
0.02%
(0.13)
-1.15%
(0.89)
-6.46%
(5.44)
0.55**
(0.22)
2.47%**
(1.15)
2.48%
(6.10)
Independent voter
2.65%*
(1.55)
9.27%**
(4.52)
0.83%
(0.59)
1.02%
(0.95)
0.51%
(3.57)
-0.68%
(0.70)
Republican voter
13.69%**
(6.21)
9.13%**
(2.01)
0.68%**
(0.14)
-11.01%*
(5.89)
-3.11%**
(1.29)
0.14%
(0.14)
Table 9: Effect of incumbents in competitive elections moderating their ideal point by one standard
deviation on the probability that citizens vote for the incumbent in congressional elections. Effects
that are significant at the .05 level are shown with two stars and effects that are significant at the
.1 level are shown with one star.
Do the roll call positions of legislators in competitive districts affect whether survey
respondents supported their incumbent legislator on election day? Similarly to our analysis
in the previous sections, we find only limited evidence of electoral accountability for roll call
positions. Table 9 shows the implied change in the probability that constituents will support
a legislator that moderates their position by roughly one standard deviation. Similar to our
47
main results, the figure shows that there is only a modest relationship between the roll call
positions of legislators in competitive races and citizens’ vote choice.
48
Online Appendix D: Survey Questions
Table 10 shows the complete list of survey questions that we used to jointly scale respondents
from the 2006-2013 Cooperative Congressional Election Studies. The questions are a mix
of items from the common content and modules that we created. Note that we only used
respondents from the 2006, 2008, 2010, and 2012 CCES surveys in this study because the
odd numbered year respondents do not indicate congressional vote choice.
Table 10: Survey Question Text
Variable
Survey
Question Text
v2072
v2092
v2103
v3019
v3022
v3024
v3027
v3060
v2102
v3063
v2101
v3069
v3072
v3075
v3066
v3078
q34
cc06 v2072
cc06 v2092
cc06 v2103
cc06 v3019
cc06 v3022
cc06 v3024
cc06 v3027
cc06 v3060
cc06 v3063
cc06 v3075
cc46
cc06 v3078
cc34
cc38
cc12x 5
cc310
cc311
cc312
cc313
cc316b
cc316c
cc316e
cc316a
cc316f
cc316g
cc316d
cc316h
cc316i
cc417
cc422
CCES
CCES
CCES
CCES
CCES
CCES
CCES
CCES
CCES
CCES
CCES
CCES
CCES
CCES
CCES
CCES
CCES
CCES
CCES
CCES
CCES
CCES
CCES
CCES
CCES
CCES
CCES
CCES
CCES
CCES
CCES
CCES
CCES
CCES
CCES
CCES
CCES
CCES
CCES
CCES
CCES
CCES
CCES
CCES
CCES
CCES
CCES
Raise minimum wage to $7.25
Should we take action on climate change?
Amendment banning gay marriage
When should abortions be allowed?
Climate change is real
Social security privatization
Affirmative action for discriminatory companies
Ban late-term abortion
Expand funding for stem cell research
Expand funding for stem cell research
Path to citizenship or strict enforcement
Citizenship opportunity for illegal immigrants
Favor/oppose raising minimum wage
Extend capital gains tax cuts
Withdrawing troops from Iraq
Free trade agreement with Central America
Support state voter ID laws
Raise minimum wage to $7.25
Should we take action on climate change?
Amendment banning gay marriage
When should abortions be allowed?
Protect environment over jobs/economy
Social security privatization
Affirmative action for discriminatory companies
Ban late-term abortion
Expand funding for stem cell research
Extend capital gains tax cuts
Withdrawing troops from Iraq
Free trade agreement with Central America
Expand SCHIP - health care for children
Surveillance of foreigners in US
Build a wall between US and Mexico
When should abortions be allowed?
Protect environment over jobs/economy
Social security privatization
Affirmative action for discriminatory companies
Raise minimum wage to $7.25
Expand funding for stem cell research
Fund health insurance for children
Withdrawing troops from Iraq
Support/oppose amendment banning gay marriage
Federal assistance for housing crisis
Eavesdrop overseas without court order
Extend NAFTA to Peru & Columbia
U.S. government bank bailout
Government guaranteed health insurance
Carbon tax to reduce emissions
2006
2006
2006
2006
2006
2006
2006
2006
2006
2006
2006
2006
2006
2006
2006
2006
2006
2007
2007
2007
2007
2007
2007
2007
2007
2007
2007
2007
2007
2007
2007
2007
2008
2008
2008
2008
2008
2008
2008
2008
2008
2008
2008
2008
2008
2008
2008
49
Survey Text 10 Continued from previous page
Variable
Survey
Question Text
cc419 6
cc09 51
cc09 54
cc09 53
cc09 55
cc09 59a
cc09 59b
cc09 59c
cc09 59d
cc09 59e
cc09 59f
cc09 59g
sta302 1
sta302 2
sta302 3
sta302 4
sta302 5
sta302 6
sta302 7
sta302 8
sta303 1
sta303 2
sta303 3
sta303 4
sta303 5
sta303 6
sta303 7
sta304a
sta304c
sta304d
sta304e
sta304f
sta305 1
sta305 2
sta305 3
sta305 4
sta305 5
sta306 1
sta306 2
sta306 3
sta306 4
307a
307b
307c
307d
sta312
sta314
sta315
sta317
sta319
sta320
sta321
sta322
sta360a
sta360b
sta360c
sta360d
sta360e
sta360f
sta360g
sta360h
sta360i
sta360j
sta361a
sta361b
CCES
CCES
CCES
CCES
CCES
CCES
CCES
CCES
CCES
CCES
CCES
CCES
CCES
CCES
CCES
CCES
CCES
CCES
CCES
CCES
CCES
CCES
CCES
CCES
CCES
CCES
CCES
CCES
CCES
CCES
CCES
CCES
CCES
CCES
CCES
CCES
CCES
CCES
CCES
CCES
CCES
CCES
CCES
CCES
CCES
CCES
CCES
CCES
CCES
CCES
CCES
CCES
CCES
CCES
CCES
CCES
CCES
CCES
CCES
CCES
CCES
CCES
CCES
CCES
CCES
Require photo ID to vote
Take action against global warming
Amendment banning gay marriage
When should abortions be allowed?
Affirmative action for discriminatory companies
Lilly Ledbetter Fair Pay Act
Hate Crimes Act - include LGBT
American Recovery & Reinvestment Act
Expand SCHIP - health care for children
Renewable energy funding, carbon caps
Require health insurance
Appoint Sotomayor to Supreme Court
Increase funding for job training programs
Reduce government regulation
Employers should offer childcare
Increase minimum wage
Support workers right to unionize
Eliminate federal unemployment programs
Include sexual orientation in anti-discrimination laws
Include gender in anti-discrimination laws
Universal healthcare
Expand tax-free medical savings accounts
Allow importation of prescription drugs
Expand Medicare prescription drug coverage
Tax credits to offset insurance costs
Expand child healthcare programs
Providing healthcare is not responsibility of government
Allow same-sex marriage
Funding for stem cell research (existing)
Funding for stem cell research (new embryos)
Affirmative action for federal contractors
Continue federal affirmative action programs
Private social security accounts
Increase payroll tax to ensure social security viability
Decrease benefits to retirees to ensure social security viability
Increase social security benefits with cost of living
Raise the retirement age to ensure social security viability
Require welfare recipients to work
Federal block grants for welfare
Housing assistance for welfare recipients
Abolish federal welfare programs
Public health insurance option
Monetary limits in malpractice lawsuits
Require balanced federal budget
Government funds to stimulate economy
Free trade agreement with Central America
Expand funding for stem cell research
Citizenship opportunity for illegal immigrants
Affirmative action for discriminatory companies
Path to citizenship or strict enforcement
Increase minimum wage
Extend capital gains tax cuts
Amendment banning gay marriage
Eliminate the minimum wage
Government guarantee standard of living
No taxes for low-income families
Prohibit incomes above $1 million
Eliminate food subsidies for children
Tax rate the same for rich and poor
No government assistance for low-income
Government should provide universal jobs
Rich should pay higher tax rate than poor
Minimum wage should be $15/hour
Ban some high-fat foods from restaurants
Government standards for prescription drugs
2008
2009
2009
2009
2009
2009
2009
2009
2009
2009
2009
2009
2010
2010
2010
2010
2010
2010
2010
2010
2010
2010
2010
2010
2010
2010
2010
2010
2010
2010
2010
2010
2010
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2010
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2010
Module
Module
Module
Module
Module
Module
Module
Module
Module
Module
Module
Module
Module
Module
Module
Module
Module
Module
Module
Module
Module
Module
Module
Module
Module
Module
Module
Module
Module
Module
Module
Module
Module
Module
Module
Module
Module
Module
Module
Module
Module
Module
Module
Module
Module
Module
Module
Module
Module
Module
Module
Module
Module
50
Survey Text 10 Continued from previous page
Variable
Survey
sta361c
sta361d
sta361e
sta361f
sta361g
sta361h
sta361j
sta361k
sta361l
sta361m
sta361n
sta362a
sta362b
sta362c
sta362d
sta362e
sta362f
sta362g
sta362h
sta362i
sta362j
sta362k
sta370a
sta370b
sta370c
sta370d
sta370e
sta370f
sta380a
sta380b
sta380c
sta380d
sta380e
sta380f
sta380g
sta380h
sta381a
sta381b
sta401a
sta401b
sta401c
sta401d
sta401e
sta401f
sta401g
sta402
sta403a
sta403b
sta403c
sta403d
sta403e
sta405a
sta405b
sta406a
sta406b
sta411c
sta411d
sta411e
sta411f
sta412
sta413
sta430a
sta430b
sta430c
sta430d
CCES
CCES
CCES
CCES
CCES
CCES
CCES
CCES
CCES
CCES
CCES
CCES
CCES
CCES
CCES
CCES
CCES
CCES
CCES
CCES
CCES
CCES
CCES
CCES
CCES
CCES
CCES
CCES
CCES
CCES
CCES
CCES
CCES
CCES
CCES
CCES
CCES
CCES
CCES
CCES
CCES
CCES
CCES
CCES
CCES
CCES
CCES
CCES
CCES
CCES
CCES
CCES
CCES
CCES
CCES
CCES
CCES
CCES
CCES
CCES
CCES
CCES
CCES
CCES
CCES
2010
2010
2010
2010
2010
2010
2010
2010
2010
2010
2010
2010
2010
2010
2010
2010
2010
2010
2010
2010
2010
2010
2010
2010
2010
2010
2010
2010
2010
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2010
Question Text
Module
Module
Module
Module
Module
Module
Module
Module
Module
Module
Module
Module
Module
Module
Module
Module
Module
Module
Module
Module
Module
Module
Module
Module
Module
Module
Module
Module
Module
Module
Module
Module
Module
Module
Module
Module
Module
Module
Module
Module
Module
Module
Module
Module
Module
Module
Module
Module
Module
Module
Module
Module
Module
Module
Module
Module
Module
Module
Module
Module
Module
Module
Module
Module
Module
All public buildings accessible to handicapped
Government-enforced nutrition standards
No limits on pollution from businesses
Government-enforced advertising standards
All motorcyclists required to wear helmets
Ban sale of energy-inefficient appliances
Privatize the Post Office
Military burden shifted to private contractors
Government takeover of bad companies
Require power plants to reduce emissions
Require residential carbon monoxide detectors
Hold BP executives liable for oil spill
Require public schools to teach creationism
Limit ATM fees to $1
Eliminate Environmental Protection Agency
Deport all illegal immigrants
Grant all illegal immigrants citizenship
End subsidies for green energy
Government-funded high-speed railroad
Felons should have right to vote
Prohibit construction of 9-11 site mosque
Ban late-term abortion procedures
Require business-provided health insurance
Require all people buy health insurance
Limit damages in malpractice lawsuits
Medical experts decide which tests insured
Patients pay more for “ineffective”” treatments
Public insurance entity for low-cost insurance
Government funds to insure all children
Right of patients to sue HMO
Make it harder to obtain abortion
Allow the death penalty for some crimes
Require license to purchase handgun
Allow gays to serve in military
Federal law to allow school prayer
Flat tax law for all Americans
Eliminate regulations for businesses
Protect environment/natural resources
Government help insure all children
Government help employers pay for insurance
Eliminate the estate tax
Social Security privitization
Easier for labor unions to organize
Federal funding for stem cell research
Extend federal ban on assault weapons
Same-sex marriage in your state
Increase the minimum wage
Government reduce income inequality
Government reduction of federal taxes
Government vouchers for private school
Amendment banning gay marriage
Increase federal funding to public school
Government-funded universal health care
Should the government restrict immigration?
Should the government restrict gun sales?
Health insurance for low-income children
Assist homeowners facing foreclosure
Extend NAFTA to Peru & Columbia
U.S. government bank bailout
Carbon tax to reduce emissions
Guaranteed universal health insurance
Housing vouchers for homeless
Maintain welfare-to-work requirements
Provide food stamps to legal immigrants
Continue Medicaid for welfare-to-work
51
Survey Text 10 Continued from previous page
Variable
Survey
sta430e
sta430f
sta430g
sta430h
sta430i
sta430j
sta430k
sta450
sta451
sta460a
sta460b
sta460c
cc324
cc325
cc326
cc327
cc332a
cc332b
cc332c
cc332d
cc332e
cc332f
cc332g
cc332h
cc332i
cc332j
cc321
cc341a
cc341b
cc341c
cc341d
cc341e
cc341f
cc341g
cc341h
cc354
cc353
cc352
cc351 1
cc351 2
cc351 3
hsu301
hsu302
hsu303
hsu304
hsu305
hsu306
hsu310
hsu311
hsu312
hsu313
hsu314
hsu320
hsu321
hsu322
hsu323
hsu324
hsu325
hsu326
hsu327
hsu328
hsu329
hsu330
hsu360
hsu361
CCES
CCES
CCES
CCES
CCES
CCES
CCES
CCES
CCES
CCES
CCES
CCES
CCES
CCES
CCES
CCES
CCES
CCES
CCES
CCES
CCES
CCES
CCES
CCES
CCES
CCES
CCES
CCES
CCES
CCES
CCES
CCES
CCES
CCES
CCES
CCES
CCES
CCES
CCES
CCES
CCES
CCES
CCES
CCES
CCES
CCES
CCES
CCES
CCES
CCES
CCES
CCES
CCES
CCES
CCES
CCES
CCES
CCES
CCES
CCES
CCES
CCES
CCES
CCES
CCES
2010
2010
2010
2010
2010
2010
2010
2010
2010
2010
2010
2010
2010
2010
2010
2010
2010
2010
2010
2010
2010
2010
2010
2010
2010
2010
2010
2011
2011
2011
2011
2011
2011
2011
2011
2011
2011
2011
2011
2011
2011
2011
2011
2011
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2011
2011
2011
2011
2011
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2011
2011
2011
2011
2011
2011
2011
2011
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2011
2011
2011
2011
2011
Question Text
Module
Module
Module
Module
Module
Module
Module
Module
Module
Module
Module
Module
Module
Module
Module
Module
Module
Module
Module
Module
Module
Module
Module
Module
Module
Module
Module
Module
Module
Module
Module
Module
Module
Module
Module
Module
Federal poverty aid through religious orgs.
Additional funding for state Medicaid
Tax credits for businesses with childcare
Federal aid for states with more immigrants
Prohibit state laws denying immigrations services
Increase quota for skilled immigrants
Collect fingerprint data from visa applicants
Federal income tax level
Support same-sex marriage
Path to citizenship for immigrants
Increase border security with Mexico
Drivers licenses for undocumented immigrants
When should abortions be allowed?
Protect environment over jobs/economy
Amendment banning gay marriage
Affirmative action for discriminatory companies
American Recovery & Reinvestment Act
Expand SCHIP - health care for children
Renewable energy funding, carbon caps
Require health insurance
Appoint Kagan to Supreme Court
Financial Reform Bill
End Don’t Ask, Don’t Tell
Overseas surveillance of foreigners
Federal funding for stem cell research
U.S. government bank bailout
Belief in climate change
American Recovery & Reinvestment Act
Expand SCHIP - health care for children
Renewable energy funding, carbon caps
Require health insurance
End Don’t Ask, Don’t Tell
Overseas surveillance of foreigners
Federal funding for stem cell research
U.S. government bank bailout
Affirmative action for discriminatory companies
Amendment banning gay marriage
When should abortions be allowed?
Citizenship opportunity for illegal immigrants
Increase patrols of U.S.-Mexico border
Allow police to question suspected immigrants
Guaranteed universal health insurance
Protect right of workers to unionize
Government reduce income inequality
Reduce regulation of private sector
Raise minimum wage to $10
Allow corporations unlimited campaign contributions
Allow same-sex marriage
Allow LGBT to legally form civil unions
Ban or limit contraceptive use
Ban sex between persons of same gender
Require 24-hour waiting period for abortion
Raise taxes a few hundred dollars
Raise taxes on rich ($250,000+/year)
Reduce tax break for homeowners
Make retirees pay for Medicare
Increase capital gains taxes
Increase taxes on corporations
Reduce Medicaid benefits for low-income
Eliminate student loan subsidies
Reduce federal worker pensions
Make deep cuts in defense spending
Increase retirement age to 68
Fine businesses that hire illegal immigrants
Allow states to deport illegal immigrants
52
Survey Text 10 Continued from previous page
Variable
Survey
hsu362
hsu363
hsu364
hsu365
hsu367
hsu370
hsu371
hsu372
hsu373
hsu374
hsu375
hsu376
hsu377
hsu378
hsu379
hsu380
hsu381
cc350
ucm301
ucm302
ucm303
ucm304
ucm305
ucm306
ucm307
ucm321
ucm322
ucm323
ucm324
ucm325
ucm326
ucm327
ucm328
ucm329
ucm330
ucm331
ucm332
ucm333
ucm370
ucm371
ucm372
ucm373
ucm374
ucm375
ucm376
ucm377
ucm378
ucm379
ucm380
ucm381
ucm382
ucm401
ucm402
ucm403
ucm404
ucm405
ucm406
ucm407
ucm408
cc321
cc324
cc325
cc327
cc326
cc332a
CCES
CCES
CCES
CCES
CCES
CCES
CCES
CCES
CCES
CCES
CCES
CCES
CCES
CCES
CCES
CCES
CCES
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CCES
CCES
CCES
CCES
CCES
CCES
CCES
CCES
CCES
CCES
CCES
CCES
CCES
CCES
CCES
CCES
2011
2011
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2011
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2011
2011
2011
2011
2011
2011
2011
2011
2011
2011
2011
2011
2011
2012
2012
2012
2012
2012
2012
2012
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Allow police to ask for immigration documents
Deport all illegal immigrants
Remove fence on border with Mexico
Same treatment of Mexican & Canadian immigrants
Allow states to admit immigrants
Federal government should protect environment
Require power plants to reduce emissions
Eliminate the Environmental Protection Agency
Require 10% electricity renewable statewide
Require 25% electricity renewable statewide
Government should protect endangered species
States should set pollution limits
States should keep waterways clean
Support coal plant within 25 miles of home
Support wind power plant within 25 miles of home
Support oil/gas drilling within 25 miles of home
Power plants near home should be regulated
Should we take action on climate change?
Guaranteed universal health insurance
Protect worker right to unionize
Government reduce income inequality
Reduce regulation of private sector
Raise the minimum wage to $10
Allow corporations unlimited campaign contributions
Allow drilling in Alaskan Wildlife Refuge
City should provide health benefits to same-sex partners
Reduce greenhouse gas emissions in city
Subsidize mass transit for low-income in city
Subsidies for residential solar energy in city
Ban smoking in local bars/restaurants in city
Require local residents to recycle in city
Reduce pension for government employees in city
Tax breaks to incentivize businesses to move in city
Limit how much landlords can raise rent in city
Offer subsidized housing to homeless in city
Eliminate tenure for school teachers in city
Close city parks to save money
Close city libraries to save money
Require parental permission for teen abortion
Require 24-hour waiting period for abortion
Require photo ID to vote
Legalize casino gambling in states
State law capping property taxes
Take away union right to bargain
Allow LGBT to legally form civil unions
Allow same-sex marriage
In-state tuition for illegal immigrant graduates
If your state opted out of Medicaid expansion
Allow death penalty for convicted murderers
Require waiting period for gun purchases
Raise the minimum wage to $8
Set limits on CO2 emissions
Require 10% electricity renewable statewide
Require 25% electricity renewable statewide
State gasoline tax less than $0.25/gallon
Renewable energy tax on electricity bill
Require more efficient use of electricity
Set limits on CO2 emissions
State should prepare for climate change
Should we take action on climate change?
When should abortions be allowed?
Protect environment over jobs/economy
Affirmative action for discriminatory companies
Amendment banning gay marriage
House Budget plan - cut Medicare/Medicaid
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Simpson-Bowles plan - 15% cuts
Middle Class Tax Cut Act
Tax Hike Prevent Act
Religious exemption for birth control coverage
Free trade agreement with Korea
Repeal Affordable Care Act
Approve Keystone XL pipeline
Support ACA - required health insurance
Allow Gays in the Military
Citizenship opportunity for illegal immigrants
Increase patrols of U.S.-Mexico border
Allow police to question suspected immigrants
Fine businesses that hire illegal immigrants
Allow same-sex marriage
Prohibit abortions after 22nd week
Simpson-Bowles plan - 15% cuts
Repeal Affordable Care Act
Approve Keystone XL pipeline
Allow internet sales to be taxed
Violence Against Women Act
Block NSA collection of phone records
Decentralize education decision-making
When should abortions be allowed?
Protect environment over jobs/economy
Affirmative action for discriminatory companies
Should we take action on climate change?
Background check for all gun sales
Prohibit publication of names of gun owners
Ban high-capacity gun magazines
Ban assault rifles
Easier to apply for concealed-carry permit
Citizenship opportunity for illegal immigrants
Increase patrols of U.S.-Mexico border
Allow police to question suspected immigrants
Fine businesses that hire illegal immigrants
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