Income and Representation in The United States Congress
Chris Tausanovitch
ctausanovitch@ucla.edu
Abstract: Are the rich better represented than the poor, and if so, how does this underrepresentation affect policy outcomes in the United States Congress? In this paper, I
combine data from the National Annenberg Election Studies (2004 & 2008), the
Cooperative Congressional Election Studies (2006, 2008, & 2010), and a unique survey
combining the policy questions from both to scale voters the way Congressional scholars
scale members of Congress. The data cover 246,000 respondents in 435 Congressional
Districts and 50 states. I leverage the power and coverage of these data to show that the
expressed preferences of the rich are better represented in both chambers than the
expressed preferences of the poor. “Expressed preference representation,” however, is
only one form of representation. I introduce the concept of “income group representation,”
in which legislators represent their constituents with regard to income groupings without
taking constituent's expressed preferences into account. I find that legislators in districts
with larger proportions of poor constituents tend to be more liberal, controlling for poor
constituents' expressed preferences. This pattern offsets the under-representation of the
expressed preferences of the poor. I show that legislators' positions are not substantially
different from what they would be if the expressed preferences of the poor were equally
represented in the House. In the Senate, the policy effects of unequal expressed
preference representation are larger, but they are offset by income representation to an
even greater degree than in the House. The rich are better represented than the poor, but
only by the most obvious form of representation; and the policy implications of this
differential representation are minimal.
Introduction
If you want your views represented in Congress, does it help to be rich? Consider
two districts, Iowa's 3rd, which leans conservative and California's 24th, which leans
liberal. Despite the fact that, on average, Iowa's 3rd leans conservative, the high-income
people in this district are more liberal than most rich people in America. Leonard Boswell,
their member of congress, is a Democrat who supported Obama on all of his major
initiatives. Similarly, CA-24 leans liberal, but the rich in the district are more
conservative than most rich people in America, and their member of Congress, Elton
Gallegly, is a Republican with a strong conservative record. Bartels (2009) claims that
this trend is more general. As he writes, “affluent people have considerable clout, while
the preferences of people in the bottom third of the income distribution have no apparent
impact on the behavior of their elected officials.”
However, that is not to say that poor people don’t matter. West Virginia’s third
district is much more conservative than IA-3 or CA-24. Even the poor in this West
Virginia district are conservative. But, WV-3 has more poor people than 98 percent of the
other districts. Its median household income is $25,630. That may explain why
Representative Nick Rahall is arguably even more liberal than IA-3’s Boswell. Even
though the poor in Rahall’s district express conservative positions generally, he seems to
believe that they will reward him at the ballot box if he protects their short-term
economic interests. In a district as poor as his, he cannot afford to ignore them. I
hypothesize that WV-3 is an extreme example of a broader trend: that districts with
greater numbers of poor citizens are more likely to elect liberals than ideologically
similar districts with fewer poor residents.
2 In this paper, I confirm that legislators over-represent the views of rich
constituents and under-represent the views of poor constituents. Previous evidence for
this hypothesis is provided by Bartels (2009), although it is called into question by
Erikson and Bhatti (2010). Although I confirm that the expressed political preferences of
richer voters are better represented than the expressed political preferences of poorer
voters, representation of expressed political preferences is not the only type of
representation. I expect legislators to respond to the expressed political preferences of
their constituents when constituents vote on the basis of these views. To the extent that
constituents vote on the basis of other considerations, legislators might ignore their
expressed views, but may still be said to “represent” them. Legislators may believe that
taking certain positions will help them appeal to certain income groups regardless of the
actual policy preferences expressed by those groups. When the positions of legislators
covary with the proportion of constituents in an income category, I call this “income
group representation.” When the positions of legislators covary with the expressed
positions of constituents, I call this “expressed preference representation.” I will show
that when it comes to income group representation, the poor are actually better
represented than the rich.
Figure 1: Types of Representation Income Group Representation
Voters’
Income
Voters’
Political
Preferences
Voters’
Expressed
Political
Preferences
Expressed
Preference
Representation
Legislator
Political
Position
3 Figure 1 shows the distinction between income group representation and
expressed preference representation. The size of government and the extent of
redistribution are the central debates in American politics, so it should be no surprise that
income is an important determinant of political preferences (Poole and Rosenthal 1997,
McCarty, Poole and Rosenthal 2006, Gelman et al 2007). I conceptualize political
preferences in terms of voters’ utility over the actions of their legislators. Although
individual legislators rarely have a decisive effect on policy outcomes, voters’ prefer that
legislators vote in favor of certain policies and against others, even if the outcome of
those votes is predetermined. Political preferences are an important determinant of how
voters respond to survey questions about policy- their expressed political preferences.
However, this relationship may be very noisy (Zaller 1992). Legislators cannot observe
the political preferences of their constituents, but they can observe their income and their
expressed political preferences. The choice facing legislators is whether to take the
expressed preferences of their constituents at face value, or to infer their preferences from
income instead. I argue that legislators defer for the most part to income in the case of the
poor and expressed preferences in the case of the rich.
Bartels (2009) makes the first to attempt to answer the question of whether
legislators over-represent the rich on a district-by-district basis. He argues that United
States senators respond more to the views of their rich constituents than they do to the
views of their middle income and poor constituents. My analysis builds on Bartels’s
framework with an improved methodology, answering a methodological critique leveled
by Erikson and Bhatti (2010). By drawing data from a variety of sources, I create a data
set with more then 20 times the observations Bartels uses, allowing me to extend this
4 analysis to all 435 districts of the House of Representatives. The ability to extend this
analysis to the House of Representatives is useful not only because it expands the scope
of knowledge to another chamber, but because the larger number of districts in the House
allows greater statistical power. My approach is the first to use a measurement of
constituent political preferences based on answers to policy questions, and the result is
the largest political preference scaling to date in terms of the number of observations. I
affirm Bartels’s finding, but with a twist: that the poor may not be less represented, but
only differently represented.
In this paper, I use a continuous measure of political preferences that puts citizens
and legislators in the same political preference dimension (Tausanovitch and Warshaw
2011). This measure not only puts citizens and legislators in the same scale, but also
establishes a common metric for citizens from different public opinion surveys, allowing
existing large-sample surveys to be combined, for a sample size of 246,839, of whom
226,046 report registering to vote. This sample size is much larger than the sample of
155,000 used by Erikson and Bhatti (2011), or the sample of 9,253 used by Bartels
(2009). Moreover, the use of joint scaling solves a problem pointed out by Erikson and
Bhatti. It is difficult to differentiate between the preferences of the poor and the rich
when the measure used is ideological self-placement1. My measure is based directly on
responses to policy questions, avoiding many of the other pitfalls of ideological selfplacement as well2.
1
This is one of the most frequently used measures of political preferences in political science. Respondents
are asked to place themselves in one of seven categories: extremely liberal, liberal, slightly liberal,
moderate/middle of the road, slightly conservative, conservative, or extremely conservative. Erikson and
Bhatti use a five-category version that excludes the “slightly” options.
2
Hillygus and Treier (2009) show that there is great variation in political views within each ideological
category. The relationship between ideological self-placement and views on policies is a noisy one. Stiglitz
5 Although the poor may be represented in some fashion, they are still not well
represented in terms of their expressed policy preferences. However, the fact that this
representation is unequal does not necessarily imply that this inequality has a large effect
on policy. Past work does little to assess the policy implications of differential
representation. Legislators may respond more to the rich than to the poor, but if the
covariance between these groups across districts is high then it may make little difference
in terms of legislator positions and the policy outcomes these positions produce. I find
that the policy implications of unequal representation are very small in the House. In the
Senate unequal expressed preference representation has a large effect, but this effect is
more than offset by the effect of income group representation in favor of poorer
constituents.
In the following section, I will explain my theory and elaborate “income group
representation” and “expressed preference representation”. In the methodology section, I
describe the model that will be used to test the theory. The next section details the
measures and data used. In the section after that, I present the results, followed by a
discussion of the implications and the conclusion.
Two Modes of Representation
Although I have provided some intuition for the results to come in the
introduction, some links in the causal chain are left out. How do the poor condition their
behavior on policy positions if they can’t accurately report their own preferences? Why
(2009) demonstrates that use of self-placement scales varies across states and districts. Jessee (2009)
suggests that use of self-placement scales may vary in idiosyncratic ways across individuals.
6 don’t legislators take different positions on different issues in order to please both
groups? I propose a simple theory to justify my hypothesis. Although I will not be able to
test each link in the causal chain separately, an implication is that the rich should
experience better expressed preference representation and the poor should experience
better income group representation. The theory is as follows:
1. Voters vote on the basis of two considerations: legislator positions, and the
economic effects of these positions.
2. Voters are in general poorly informed about both considerations, but they are
informed about them during campaigns.
3. Rich voters are sophisticated and ideological. They primarily care about
legislator positions.
4. Poor voters are relatively unsophisticated and apolitical. They primarily care
about their own pocketbook.
5. As a result, politicians take positions that balance two competing
considerations:
a. First, they want to choose positions that are desirable to the rich.
b. Second, they want to choose positions that will be associated with
good economic outcomes for the poor.
6. The relative proportions of rich and poor voters determine the balance of these
considerations.
7. Legislators are constrained by parties to take positions in a one-dimensional
space, so they cannot simultaneously satisfy both the rich and the poor.
The implication of this theory is that legislators will respond to the preferences of
rich voters, but the number of poor voters. Poor voters vote on the basis of personal
economic considerations. This begs the question of how poor voters attribute blame for
changes in economic circumstances. I hypothesize that poor voters are able to understand
and evaluate the blame attributions of others, which they are exposed to during
campaigns. Although attributing blame for economic outcomes may seem to require
sophistication, it should be easier for the poor than for the rich. Almost all of the
individuals in the group I classify as “poor” are direct beneficiaries of government
programs (Mettler 2011). Changes in these government programs directly alter the
7 economic fortunes of the poor. In poor districts, politicians have a lot to gain by taking
credit for positive changes, but they can only do this if they are on the “right side” of the
issue. If they are caught on the wrong side of the issue, they have a lot to lose.
Rich voters, on the other hand, may be recipients of particularistic tax breaks, and
may be strongly affected by changes in tax policy, but the number of programs that have
a direct and salient impact on their well being is more limited. The rich tend to be more
sophisticated and more committed to political ideas (Verba, Schlozman and Brady 1995,
Tausanovitch 2010). They are able to understand where they stand vis-a-vis their elected
officials, and vote accordingly. Legislators know this, and so they pay attention to their
political preferences.
To draw on a familiar distinction, when legislators are responding to the particular
expressed policy preferences of their rich constituents may be said to act as “delegates,”
making decisions almost as if instructed by voters (Eulau et al 1959). However, it is not
necessarily the case that when legislators do not respond to the expressed policy
preferences of their poor constituents they are acting as “free agents” or “trustees,” and
voting on the basis of their own judgments alone (Rehfeld 2009). We expect that when
voters vote on the basis of outcomes, legislators will act differently than if voters vote on
the basis of policy decisions (Fiorina 1981). If the poor are ignorant about the probable
results of different policies for the state of the economy, but will vote retrospectively on
the basis of economic outcomes, then legislators should vote in a way that helps them
claim credit for good economic outcomes and avoid blame for bad ones. This may entail
a very different voting pattern than if legislators simply enacted the policies that poor
voters say they prefer on surveys.
8 I emphasize the pocketbook voting behavior of the poor. However, it is also the
case that the poor are less able to articulate their political preferences coherently, even if
they have some underlying values that they wish to see represented in politics. Poor
voters may sometimes vote on the basis of policy, but only salient policies, and their
expressed policy preferences may change significantly when a policy becomes salient,
just as preferences over candidates often crystallize over the course of a campaign
(Gelman and King 1993)3. This gives legislators an incentive not to take poor voters’
preferences at face value. Rather, they may try to infer what these voters’ preferences
would be if they were more informed. In Arnold’s (1992) formulation, legislators may be
more concerned with the “potential preferences” of the poor than the preferences that
they express at a given moment. Poorer voters tend to be more liberal than rich ones
(Gelman et al 2007). However, they tend to be more conservative than one would expect
given the fact that conservative policies often have direct negative effects on their
economic well-being (Bartels 2009, Frank 2004). The distinction between representing
constituents by doing what they say and representing constituents by doing what they
want is not only a distinction between policies and outcomes4.
Jessee (2010) shows that more informed voters vote on the basis of their policy
beliefs, whereas unsophisticated voters are more likely to vote on the basis of their
partisan identity. Shor and Rogowski (2010) replicate Jessee’s finding for Congressional
election contests, using education instead of political information. This gives legislators
3
Stromberg (1994) shows that greater information increases the responsiveness of representatives to
constituents. 4
This distinction is not captured by the delegate/trustee distinction and is also not captured by Rehfeld’s
(2009) finer grained typology. Rehfeld distinguishes between whether constituents are the source of
judgment or the legislator is the source of judgment. I assume as a starting point that legislators are trying
to obey the wishes of their constituents as strictly as possible, but that legislators must divine what these
wishes are, and which of them are the most important. Legislators may wish to follow the instructions of
their constituents, but no instructions are forthcoming.
9 an incentive to respond to the broader interests of less sophisticated voters rather than
their particular expressed policy beliefs. Other scholars argue that the expressed
preferences of uninformed or unsophisticated voters differ systematically from the
expressed preferences of more informed voters, all other things being equal (e.g. Althaus
1998, Bartels 1996, Delli Carpini and Keeter 1997, Luskin and Fishkin 1998). If this is
the case, then income itself may be a better measure of the preferences of the poor than
expressed political preferences are. I will show that legislators tend to take more liberal
positions when the proportion of poor voters in their district is higher, holding the
political preferences of those voters constant.
Methodology
The baseline model of representation I will use is the mean voter model5. Take a
1-dimensional continuous policy space where each constituent has a most preferred point
in this space. The mean voter model holds that legislators in two-party contests will
position themselves at the point that is the mean of their constituents’ preferences. It is
justified by abstention in a rational choice model (e.g. Enelow and Hinich 1989, Ledyard
1984), and by a behavioral model with probabilistic voting (e.g. Erikson and Romero
1990). The mean voter model has the property that movement in the position of any
individual constituent in a finite constituency will affect the position of the legislator, but
that if any two individuals were to trade ideal points, the position of the legislator would
5
The main results here have all been replicated using a median voter model. Testing these hypotheses is
somewhat subtler using a median voter model, because medians cannot be represented as weighted
combinations of sub-group medians. I have excluded this analysis because it sheds no more light on the
question at hand.
10 not move. This model is particularly amenable to assessing representation, because the
“weight” that legislators place on different groups can be represented as a decomposition
of the mean. If there are three groups, the rich, R, middle-income people, M, and poor
people, P, then the mean position can be represented in the form
(1) ! = !! !! + !! !! + !! !!
Where ! is the position of the legislator, !! is the mean position of the rich, !! is the
mean position of middle-income people, and !! is the mean position of the poor. !! is
the proportion of rich people in the legislator’s district, !! is the proportion of middleincome people, and !! is the proportion of poor people. Following Bartels (2009),
Clinton (2006) and others, we can turn this formal model into a statistical model that
allows the weights placed on the groups to vary:
(2) ! = ℬ! + ℬ! !! !! + ℬ! !! !! + ℬ! !! !! + !
Here ℬ! is an intercept, ℬ! is the estimated weight placed on the rich, ℬ! is the estimated
weight placed on middle-income people, and ℬ! is the weight placed on the poor. Erikson
and Bhatti (2010) point out that the proportions may have independent effects of their
own, and thus we should control for these proportions as well. !! is excluded because
!! = 1 − !! − !! . This leads us to the following linear regression specification, which
matches the one in Erikson and Bhatti:
(3) ! = ℬ! + ℬ! !! !! + ℬ! !! !! + ℬ! !! !! + ℬ! !! + ℬ! !! + !
We do not expect ℬ! to be 0 and the other coefficients to be 1, because L is not
necessarily measured in the space of !! , !! and !! 6 . However, if the mean voter
6 Using a joint scaling model helps ensure that the legislator and constituent positions are measured in the same dimension. Some recent work (e.g. Bafumi and Herron 2010 and to a lesser extent Jessee 2009) has made the claim that these positions are in the same space and can be compared directly, 11 theorem is correct, then it should be the case that ℬ! = ℬ! = ℬ! . If, on the other hand,
the rich are better represented than the poor, then we will find ℬ! > ℬ! . The poor are
ignored entirely when ℬ! = 0. We cannot prove a point hypothesis, but we may be able
to show that ℬ! is small.
There are three hypotheses of interest. Firstly, are the poor represented at all?
Mathematically, is ℬ! close to 0? Are the rich better represented than the poor, ℬ! > ℬ! ?
And finally, are the middle class better represented than the poor, ℬ! > ℬ! ? The income
groups above will be defined in detail in the data section.
In addition to testing hypotheses about preferences representation, I will also test
for “income group representation”. In equation 3, there is evidence of income group
representation of a given group if the coefficient on the proportion of constituents in that
group is positive and significant. Although the proportion of people in each group sums
to one, a positive coefficient on one group is evidence that the threshold separating that
group from the others is relevant for legislator positioning. These proportions are not
merely “controls” but covariates that may be important in their own right.
Data
The tests I have laid out require a measure of political preferences in a common
dimension for legislators and voters, and aggregates of this measure at the district and
state levels. I use a measure based on policy preferences from the “super survey”
approach of Tausanovitch and Warshaw (2011). This measure is a joint estimate of the
however, this relies on much stronger assumptions than I make here. For some examples of where this stronger assumption might go wrong, see Jessee (2011). 12 preferences of respondents to five large-N surveys and their legislators, based on a
Bayesian item response model, using questions on a wide array of economic and social
policies. A smaller “super survey” is used to “bridge” respondents between surveys as
well as legislators, allowing me to obtain comparable preferences estimates for people
answering different sets of political questions. This is achieved by asking the “super
survey” group questions from all of the other surveys as well as questions from surveys
asked to legislators.
I employ a Bayesian item response model identical to the one from Clinton,
Jackman and Rivers (2004) to estimate citizen and legislator preferences. The model is as
follows:
(4)
Pr(yij = 1) = !(xi ! j " " j )
i = 1, . . . , n indexes individuals and j = 1, . . . , m index issues. y ij is the i-th
respondent’s answer to question j, xi is the ideal point for respondent i, ! j is the
!
“discrimination” parameter for item j, ! j is the “difficulty” parameter for item j, and
!(•) denotes the standard normal cumulative distribution function.
The items include all roll calls taken by House members and Senators in the 108th
to the 111th Congresses, all of the Project Vote Smart survey questions answered by
federal legislators during that time, and all of the survey questions answered by
respondents to the 2000 and 2004 National Annenberg Election Survey and the 2006,
2008 and 2010 Cooperative Congressional Election Study, as well as respondents to a
1,300-person module places on the 2010 CCES7. The 1,300-person module is the “super
7
These surveys are not designed to be balanced at the congressional district level. In future work, I hope to
achieve greater accuracy using a multilevel regression and post-stratification framework that informs
estimates using the known district-level population totals of demographic and other information. In the
13 survey” that allows the model to estimate jointly scaled preferences for legislators and
respondents across data sources. On this module, I ask questions from all of the other
surveys, including the Project Vote Smart surveys, which are asked to legislators. The
responses to these items are treated as responses to the same question across groups.
Questions that each respondent did not answer (i.e. survey respondents did not vote in
Congress) are treated as missing at random.
The model is estimated using a Bayesian slice sampling method with vague priors.
Due to the very large size of the y matrix, it is necessary to use specialized software by
Lewis, Lo, and Tausanovitch (2011) that parallelizes the estimation and distributes
computing tasks onto graphics processing units (GPUs). Validation of these estimates can
be found in Tausanovitch and Warshaw (2011).
A complication arises in estimating the uncertainty in district-level measures.
Although the model used to estimate respondent ideal points is Bayesian, the specialized
software used is not yet able to estimate district-level hyperparameters. My solution is to
treat the estimates as fixed quantities, and use a non-parametric bootstrap to estimate their
sampling uncertainty (Efron and Tibshirani 1993). I report this uncertainty using
bootstrap percentile intervals. Ideally, the best procedure would be a fully Bayesian
model that incorporates measurement uncertainty and sampling uncertainty. However,
given the current unfeasibility of such a model, it is much more important to capture the
sampling uncertainty. The measurement uncertainty that remains in the mean of a large
number of estimates is very small.
present work the results are robust to using the given survey weights or a custom post-stratification weight
based on race, gender, and education.
14 There are numerous advantages to using a continuous measure of political
preferences based on responses to policy questions, but the most important one is that a
continuous measure of preferences simply gives us more information about the location
of individuals in the policy space. This may be the reason that Erikson and Bhatti are able
to find differential representation using the 9,253 respondents to the NES Senate study
with a 7-point measure of ideological self-placement, but are unable to find differential
representation using the 155,00 respondents to the NAES with a 5-point measure. The
less granular measure does not distinguish as well. Ideological self-placement is
particularly poorly suited to studying differences between representation among income
groups because income groups are not likely to have a shared understanding of the
question.
To separate respondents into income categories, I choose a somewhat arbitrary
threshold. Respondents who report household income of less than $25,000 are considered
poor. Respondents who report household incomes of over $100,000 are considered rich.
These boundaries are chosen mainly because they can be consistently matched across the
surveys used. Both Bartels and Erikson and Bhatti find that their results are substantially
unchanged when they use different income thresholds. I have chosen a threshold that puts
a slightly higher number of poor people than rich people in each district on average in
order to make sure that a lower number of poor people is not driving the results. The
average congressional district is 20% poor and 16% rich. Household income is not the
ideal measure, as I am unable to adjust for the number of people in the household.
However, the thresholds I have chosen are sufficiently extreme that it is unlikely that a
15 poor individual is being misclassified as rich or a rich individual is being misclassified as
poor.
Observations are legislator-districts, pooled over the 108th to the 111th Congresses.
Each unique legislator is a separate observation. Since the measure of ideology is based
on pooling surveys over multiple years, I do not create additional observations for the
same legislator in different years. The timeframe under study has the advantage that it
spans periods of different party control, both of Congress and the Presidency, so the
results should not reflect a pattern of representation that obtains only under control by
one party or the other.
In the following section, I will report results using only respondents who claimed
to be registered to vote. In the sample, 91.6 percent report being registered to vote at the
time of interview, a much higher percentage than is actually the case in a typical year.
The approximately 8 percent of the sample that admits to not being registered is a
particularly disengaged group. I chose to exclude self-reported non-voters for two reasons.
Firstly, this choice eliminates the possibility that this disengaged group accounts for the
result that the poor are under-represented in the analysis to follow. Secondly, excluding
this group leads to more accurate results. In almost every case, excluding self-reported
non-voters increases model fit. For ease of exposition, I will call people who report being
registered to vote “voters” even though many of them did not actually vote. The results
here cannot determine whether non-voting accounts for differential representation.
Analysis
16 State
Figure 2: Mean Political Preferences By Income Group For Each State
OK
ID
UT
SD
WY
MS
AL
TN
AR
LA
NE
TX
MT
ND
GA
NV
MO
KY
KS
AZ
IN
SC
IA
WV
OH
NC
WI
FL
CO
VA
PA
MI
MN
NM
NH
ME
OR
DE
IL
WA
CA
NJ
MD
CT
RI
HI
NY
MA
VT
(613,1713,268)
(242,774,96)
(289,1191,202)
(183,462,43)
(92,275,60)
(561,1021,172)
(853,2001,335)
(975,2826,480)
(559,1395,180)
(792,1725,315)
(297,813,109)
(2557,9389,2497)
(217,575,64)
(127,335,35)
(1096,4146,1136)
(266,1270,321)
(1078,3404,595)
(805,2046,324)
(469,1607,313)
(721,3139,795)
(1010,3361,551)
(734,1881,325)
(496,1785,228)
(493,935,96)
(1969,6272,1172)
(1328,4045,786)
(789,3175,554)
(2616,9109,1968)
(573,2444,679)
(860,3488,1209)
(2112,7095,1384)
(1686,5181,904)
(724,2840,665)
(342,1011,243)
(162,849,225)
(324,991,142)
(692,2329,434)
(111,466,119)
(1541,5678,1483)
(875,3636,908)
(3008,12259,4652)
(679,3346,1443)
(546,2389,961)
(314,1535,613)
(122,445,121)
(39,199,71)
(2250,7328,2092)
(660,2590,865)
(121,376,53)
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
●
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●
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●
●
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●
●
●
●
●
●
●
●
●
●
●
●
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−1.25
−1.00
−0.75
−0.50
−0.25
0.00
0.25
0.50
0.75
1.00
1.25
Political Preferences
This graph shows the mean estimated political preferences of income groups by state. The square
represents the poor, the circle represents middle-income voters, and the triangle represents the rich.
Bootstrap 90% confidence intervals are shown. The numbers on the right are the sample sizes for the poor,
rich, and middle income respectively in each state.
17 District
Figure 3: Mean Political Preferences By Income Group for CDs in California
CA 22
CA 21
CA 02
CA 40
CA 19
CA 25
CA 49
CA 20
CA 11
CA 52
CA 44
CA 48
CA 47
CA 39
CA 50
CA 04
CA 03
CA 26
CA 42
CA 45
CA 24
CA 18
CA 32
CA 51
CA 38
CA 16
CA 10
CA 34
CA 23
CA 36
CA 01
CA 27
CA 15
CA 07
CA 17
CA 13
CA 35
CA 05
CA 37
CA 30
CA 28
CA 14
CA 53
CA 12
CA 06
CA 33
CA 09
CA 08
(66,256,102)
(37,162,39)
(88,251,30)
(71,268,65)
(61,211,55)
(36,218,76)
(35,238,91)
(29,82,14)
(37,200,153)
(34,260,111)
(33,170,77)
(29,177,168)
(12,74,21)
(20,122,28)
(41,210,122)
(57,294,104)
(49,300,94)
(25,204,103)
(8,91,17)
(22,171,74)
(48,249,103)
(57,164,24)
(29,132,20)
(27,135,30)
(26,103,11)
(26,151,83)
(33,221,115)
(35,74,20)
(31,230,50)
(28,167,95)
(76,257,69)
(30,157,64)
(21,151,121)
(25,207,74)
(42,164,77)
(22,164,68)
(42,121,27)
(48,227,41)
(39,150,27)
(16,84,20)
(33,176,43)
(37,152,155)
(77,219,85)
(21,182,92)
(54,238,159)
(46,159,25)
(70,193,92)
(72,219,117)
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−1.25
−1.00
−0.75
−0.50
−0.25
0.00
0.25
0.50
0.75
1.00
1.25
Political Preferences
This graph shows the mean estimated political preferences of income groups by congressional district in the
state of California. The square represents the poor, the circle represents middle-income voters, and the
triangle represents the rich. Bootstrap 90% confidence intervals are shown. The numbers on the right are
the sample sizes for the poor, rich, and middle income respectively in each district. 18 Figure 2 shows mean political preference estimates for poor, middle income, and
rich voters by state. The square represents the mean preferences of the poor, the circle
represents the mean preferences of middle-income people, and the triangle represents the
mean preferences of the rich. 90% bootstrap confidence intervals are shown, and the
sample size for each group is to the right of the respective state. In more than 75% of
cases we can distinguish statistically between the mean ideal points of the poor and the
rich, and in 84% of cases we can distinguish between the mean ideal points of poor and
middle-income voters. We can only distinguish between the ideal points of the rich and
the middle income in about 16% of cases. However, we can say with a high degree of
certainty that the mean of the state means for the poor is less than the mean of the state
means for the middle income, which is less than the mean of the state means for the rich.
In other words, within a particular state we may be uncertain over which group is the
most conservative, but we are confident that the order, from most liberal to most
conservative, is poor-middle-rich in the average state.
Figure 3 shows the means for each group within congressional districts in the
state of California, and is similarly arranged. Due to smaller sample sizes, the confidence
intervals for mean preferences within districts are much higher than those for means
within states. We can distinguish the poor mean from the others only 10% of the time
within districts. However, we can distinguish the mean of the means with a high degree
of certainty, as before. We can also distinguish many of the groups from each other
across districts. The estimated ordering of income groups is remarkably constant, with the
poor on the left and the rich on the right.
19 State
Figure 4: Mean Self-Identified Ideology By Income Group In Each State
WY
AL
OK
MS
LA
TN
UT
ID
AR
TX
SD
SC
GA
IN
KS
KY
ND
MO
NC
NE
AZ
WV
MT
NV
IA
FL
VA
OH
NH
PA
WI
CO
MI
DE
ME
NM
MN
IL
NJ
RI
WA
CA
MD
OR
CT
HI
NY
MA
VT
(92,275,60)
(853,2001,335)
(613,1713,268)
(561,1021,172)
(792,1725,315)
(975,2826,480)
(289,1191,202)
(242,774,96)
(559,1395,180)
(2557,9389,2497)
(183,462,43)
(734,1881,325)
(1096,4146,1136)
(1010,3361,551)
(469,1607,313)
(805,2046,324)
(127,335,35)
(1078,3404,595)
(1328,4045,786)
(297,813,109)
(721,3139,795)
(493,935,96)
(217,575,64)
(266,1270,321)
(496,1785,228)
(2616,9109,1968)
(860,3488,1209)
(1969,6272,1172)
(162,849,225)
(2112,7095,1384)
(789,3175,554)
(573,2444,679)
(1686,5181,904)
(111,466,119)
(324,991,142)
(342,1011,243)
(724,2840,665)
(1541,5678,1483)
(679,3346,1443)
(122,445,121)
(875,3636,908)
(3008,12259,4652)
(546,2389,961)
(692,2329,434)
(314,1535,613)
(39,199,71)
(2250,7328,2092)
(660,2590,865)
(121,376,53)
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2
3
4
Self−Identified Ideology
This graph shows the mean self-identified ideology of income groups by state. Ideology is measured on a
5-point scale. The square represents the poor, the circle represents middle-income voters, and the triangle
represents the rich. Bootstrap 90% confidence intervals are shown. The numbers on the right are the sample
sizes for the poor, rich, and middle income respectively in each state. 20 District
Figure 5: Mean Self-Identified Ideology By Income Group In CDs in California
CA 22
CA 02
CA 21
CA 40
CA 25
CA 19
CA 20
CA 03
CA 11
CA 49
CA 44
CA 18
CA 52
CA 50
CA 42
CA 04
CA 48
CA 47
CA 45
CA 26
CA 24
CA 51
CA 34
CA 32
CA 39
CA 35
CA 38
CA 10
CA 13
CA 15
CA 16
CA 07
CA 23
CA 27
CA 01
CA 37
CA 36
CA 17
CA 05
CA 30
CA 53
CA 28
CA 12
CA 14
CA 33
CA 06
CA 09
CA 08
(66,256,102)
(88,251,30)
(37,162,39)
(71,268,65)
(36,218,76)
(61,211,55)
(29,82,14)
(49,300,94)
(37,200,153)
(35,238,91)
(33,170,77)
(57,164,24)
(34,260,111)
(41,210,122)
(8,91,17)
(57,294,104)
(29,177,168)
(12,74,21)
(22,171,74)
(25,204,103)
(48,249,103)
(27,135,30)
(35,74,20)
(29,132,20)
(20,122,28)
(42,121,27)
(26,103,11)
(33,221,115)
(22,164,68)
(21,151,121)
(26,151,83)
(25,207,74)
(31,230,50)
(30,157,64)
(76,257,69)
(39,150,27)
(28,167,95)
(42,164,77)
(48,227,41)
(16,84,20)
(77,219,85)
(33,176,43)
(21,182,92)
(37,152,155)
(46,159,25)
(54,238,159)
(70,193,92)
(72,219,117)
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2
3
4
Self−Identified Ideology
This graph shows the mean self-identified ideology of income groups by congressional district in California.
Ideology is measured on a 5-point scale. The square represents the poor, the circle represents middleincome voters, and the triangle represents the rich. Bootstrap 90% confidence intervals are shown. The
numbers on the right are the sample sizes for the poor, rich, and middle income respectively in each district. 21 Contrast Figures 2 and 3 with Figures 4 and 5, which show means of 5-point
ideological self-placement. In states, we can distinguish between the poor and the middle
income only half the time, and between the poor and the rich in less than 40% of cases. In
congressional districts, not only do we distinguish less well between the groups within
districts, but we cannot significantly differentiate between the mean of the means for the
rich and the mean of the means for middle income people. These differences speak to the
measurement advantages of a policy-based measure of preferences. These advantages are
in addition to the advantages of interpretation and face validity.
Table 1: Relationship Between House and Senate Ideal Points and Income Group Preferences
(108th-111th Congress)
House
Senate
Rich Mean * Proportion Rich
3.94**
8.84**
(0.70)
(3.92)
Middle Mean * Proportion
3.01**
6.40**
Middle
(0.30)
(1.79)
Poor Mean * Proportion Poor
1.65*
-11.04**
(0.97)
(4.88)
Proportion Rich
1.00*
-1.36
(0.55)
(2.39)
Proportion Poor
-2.2**
-4.91*
(0.73)
(2.86)
Constant
0.28
0.44
(0.21)
(0.82)
N
602
143
R-squared
0.53
0.41
** Significant at the .05 level, * Significant at the .1 level
Table 1 shows results based on the specification in (1). The House results show
that members of Congress are more responsive to middle income and rich voters than to
poor voters. A Wald test confirms this difference. However, House members appear to be
no more responsive to the rich than they are to middle income constituents. Legislator
positions have a small but positive correlation with the mean position of poor voters. In
the Senate we see more dramatic underrepresentation. The positions of Senators are
negatively correlated with the preferences of poor constituents, even controlling for the
22 proportion of poor constituents. This fact that this coefficient is large and negative seems
implausible, and will merit further analysis. For now, we reject the hypothesis that there
is a positive relationship between the positions of poor constituents and the positions of
their Senators.
The fact that the views of low-income constituents are under-represented (in the
House) or ignored (in the Senate) does not necessarily mean that the short-run interests of
the poor are also ignored. Referring to Table 1 again, notice that the coefficient on the
proportion of poor constituents is negative and significant. The position of legislators is
more liberal on average when the proportion of poor constituents increases, holding the
preferences of the poor constant. In the House, a similar, but weaker, relationship holds
for the rich: if the proportion of rich constituents is higher, the legislator will tend to be
more conservative. If it is the case, as commonly supposed, that liberal policies tend to
favor the short-run economic and political interests of the poor and conservative policies
tend to favor the wealthy, then these effects are consistent with income group
representation. There is an interesting asymmetry here. Where the poor are less well
represented in terms of policy, they are actually better represented in terms of interests. In
other words, they experience greater income group representation and less expressed
preference representation.
Although the coefficient on the expressed preferences of the poor strongly
supports the hypothesis that the poor are unrepresented, the negative sign of this
coefficient is a priori implausible. Although one could concoct stories in which
legislators take more liberal positions when the poor are more conservative, there is no
well-established theoretical basis for such a story. However, there is an econometric
23 explanation for how such an unexpected result can come about. Estimates of regression
coefficients can be unstable in the presence of high multicollinearity (Greene 2000). I test
the robustness of these results under the restriction that the coefficient on the preferences
of each group must be non-negative. In order to do so, I estimate a Bayesian regression in
which the priors for these coefficients are distributed half-normal, truncated below at zero.
All other coefficients are given normal priors. Besides the truncation, all priors are highly
uninformative, with variance 10,000. The results are reported in Table 2.
Table 2: Relationship Between House and Senate Ideal Points and Income Group Preferences
(108th-111th Congress), Constrained Bayesian Regression
House
Senate
Rich Mean * Proportion Rich
3.94
10.73
[2.55,3.94]
[3.20,18.12]
Middle Mean * Proportion
3.00
2.89
Middle
[2.41,3.56]
[0.57,5.30]
Poor Mean * Proportion Poor
1.71
1.27
[0.21,3.58]
[0.05,5.80]
Proportion Rich
1.00
0.02
[-0.07,2.08]
[-4.69,4.74]
Proportion Poor
-2.18
-1.44
[-3.62,-0.76]
[-6.52,3.76]
Constant
0.28
0.01
[-0.13,0.69]
[-1.62,1.63]
N
602
143
R-squared
0.52
0.36
95% Credible intervals in brackets
I re-estimate the model for both chambers, even though there were no suspect
coefficients in the House. The House results are almost identical to what they were before.
Note that the credible regions for each coefficient should not be used to conduct
hypothesis tests of differences between coefficients, because they do not reflect the joint
distribution of the estimates. Although the 95% credible interval for the coefficient on the
proportion rich overlaps zero, the 90% credible interval does not. In the Senate, the
results on expressed preference representation are similar as well, but without the
negative coefficient for the preferences of the poor. The rich are better represented than
24 the poor with about 97% probability8. Although the posterior credible interval of the
coefficient on the preferences of the poor does not overlap 0, this is an artifact of the nonnegativity restriction. We should not reject the hypothesis that the poor are entirely
unrepresented. In general, when we add the non-negativity restriction to the coefficients
on group preferences, the results in the Senate converge towards the results in the House,
with two exceptions. The rich are still given much greater weight. And although the
coefficient on the proportion poor is still negative, it is only below zero with 71%
certainty. In part, this speaks to the value of the much greater statistical power offered by
examining representation in the House. Multicollinearity is a pervasive problem when
examining questions of representation (Romer and Rosenthal 1979).
The effect sizes for the expressed preferences of each group are straightforward to
interpret. In a hypothetical district where each group makes up one third of the population,
the coefficients represent the difference in legislator position that results from a 1/3-unit
change in the mean preference of a given group. The direct effects of the proportions in
each group are more difficult to interpret, because these proportions necessarily add to 1.
To aid in interpretation, consider the case of a person who moves to a congressional
district where one third of the population is poor, one third are middle income, and one
third are rich. If the political preferences of this person are exactly the same as the mean
political preferences of people in the district, then her preferences have no effect on her
new legislator’s position. However, she may have an impact on the legislator’s position
via her income. If she were poor, this would increase the proportion of poor constituents
and decrease the proportion of rich and middle-income constituents, which according to
8
In a Bayesian context, we can make direct probability statements about the likelihood of estimates taking
on certain values based on their posterior distribution.
25 the model would move the legislator to the left. If she were rich or middle income, this
would move policy to the right9. However, the move to the left resulting from adding a
poor constituent would be fifty percent larger than the move to the right from adding a
rich constituent. In the Senate this effect would be roughly four times as large, using the
estimates from Table 1.
To see how income group representation has a leveling effect on representation,
consider again a district where one third of the constituents are in each income group. Say,
for example, that the mean preferences of each group are 0. Now consider two possible
immigrants to a district, one who is rich and conservative, and one who is poor and
liberal. Assume both have preferences the same distance from 0. The marginal effect of
adding the rich conservative to the district is only 17% greater in magnitude than the
marginal effect of adding the poor liberal. Even though the preferences of the rich
conservative hold greater sway, the poor liberal pulls policy to the left by merit of being
poor.
So far, I have run my analysis pooled with legislators of both parties, without
controlling for the party of the legislator. It is also important to run the analysis within
parties, because within-party representation is fundamentally different from betweenparty representation in recent years. Due to the polarization in Congress, most of the
variation in Congressional positions is between-party. Indeed, a party dummy explains
90% of the variance in party positions. It is reasonable to believe that the representational
process that determines whether a Democrat or a Republican represents a district is
different from the representational process that determines whether a particular legislator
9
In the Senate the coefficient on the proportion of rich is negative, although it does not come close to
significance. However, even if we take this coefficient at face value, increasing the proportion rich will
move policy to the right because it corresponds to a decrease in the proportion of poor constituents.
26 is liberal or conservative for their party. Separating the regression by party may be more
informative for determining the variance explained (Clinton 2006).
Table 3: Relationship Between House and Senate Ideal Points and Means– Within Parties
(108th-111th Congress)
House
Senate
Democrats
Republicans
Democrats
Republicans
Rich Mean * Proportion Rich
0.90**
2.14**
3.44
0.03
(0.43)
(0.41)
(2.11)
(2.60)
Middle Mean * Proportion
1.52**
0.70**
2.00*
2.25**
Middle
(0.18)
(0.17)
(1.06)
(1.08)
Poor Mean * Proportion Poor
0.82
0.51
-2.71
1.71
(0.56)
(0.50)
(2.71)
(2.84)
Proportion Rich
0.45
0.21
1.24
2.06
(0.35)
(0.28)
(1.22)
(1.71)
Proportion Poor
0.22
-0.31
0.95
0.88
(0.42)
(0.38)
(1.44)
(1.76)
Constant
-0.77**
0.89**
-1.42
0.18
(0.33)
(0.11)
(0.43)
(0.50)
N
305
297
76
68
R-squared
0.53
0.29
0.42
0.32
** Significant at the .05 level, * Significant at the .1 level
In the models that are broken down by party in Table 3, the results are more
mixed than the across-party results. Wald tests find that the rich are overrepresented
relative to the poor by House Republicans and Senate Democrats. For Democrats in the
House, the largest coefficient is for middle-income constituents, but none of the
coefficients are statistically distinguishable from the others. All of the coefficients are
positive, but the coefficient for the poor is not statistically significant. For the
Republicans in the Senate, only the coefficient for middle-income people is significant,
and the coefficient for the rich is close to 0. None of the coefficients are statistically
distinguishable. There is no evidence of income group representation within parties.
In summary, there is evidence of under-representation of the poor relative to the
rich in both chambers, but this under-representation is especially severe in the Senate.
However, there is also evidence that the poor are better represented in terms of their
27 short-run interests. The positions of Democratic Senators within the Democratic Party
favor the rich, as do the positions of Republican House members within the Republican
Party. For House Republicans, the rich are over-represented even more than the middle
class.
Policy Implications
I have shown that the rich are over-represented relative to the poor in terms of
expressed policy preferences. However, the policy preferences of income groups within
districts are correlated. It remains to be seen whether differences in representation have a
substantive effect on policy. One way to gauge the effect that unequal representation has
on policy is to compare the distribution of legislator positions to the distribution that the
model would predict if the coefficients on each group were equal. Figure 6 graphs this
counterfactual for the House using the estimates from Tables 1 and 3. The left panel
graphs predictions from the across-party model in Table 1 and the right panel graphs
predictions from the two within-party models in Table 3.
Equal representation of expressed political preferences corresponds to a situation
where ℬ! = ℬ! = ℬ! in equation 1. In order to simulate this hypothetical case, I
calculate the legislator positions that would result if these coefficients were made equal,
but all other coefficients remained as estimated in Table 1. I preserve the sum of
ℬ! , ℬ! , and ℬ! to maintain the same linear mapping. The kernel density estimate of the
distribution of these predictions for the House is represented by the dotted line in Figure
6.
28 Figure 6: Predicted versus Observed Distribution of Ideal Points in the House
Observed
Predicted
Equal Representation
Equal w/o Proportions
−2
−1
0
1
Legislator Position
Observed
Predicted
Equal Representation
Equal w/o Proportions
2
−2
−1
0
1
2
Legislator Position
The left pane of this figure corresponds to an across-party model of House Member positions. The right
pane corresponds to a within-party model. In each graph, the solid line is a kernel density estimate of the
distribution of the observed data. The dashed line represents the distribution of the predicted values from
the model. The dotted line is the distribution of predicted values from a model in which the weight placed
on the preferences of each income group within each district is assumed to be equal. The distribution that
alternates dots and dashes represents the predicted values from a model in which the income group
preferences are weighted equally and the direct effect of the proportion of constituents in any group is
assumed to be 0.
Compare the dotted line in the left panel of Figure 6 to the dashed line, which
represents the kernel density estimate of the unaltered model predictions using the
estimates from Table 1. The difference between these two densities is minimal. Unequal
expressed preference representation does not noticeably affect polarization or overall
29 liberalness compared to a baseline of equal representation10. The same holds for the
within-party models in the right panel of Figure 6.
What about income group representation? Consider a world in which income is
irrelevant to legislator positions. I predict values for legislator positions where ℬ! =
ℬ! = 0 and all other coefficients are as in Table 1. The resulting kernel density is shown
as the distribution that alternates dots and dashes in Figure 6. Once again, there is very
little effect in both the across-party model and the within-party model.
Figure 7 shows the across-party and within-party densities for the Senate. The
differences between the predicted values for the Senate are much greater than they are for
the House, particularly in the across-party (left) pane. If it were the case that the
preferences of the poor were given equal weight to the preferences of the rich and middle
class (ℬ! = ℬ! = ℬ! from (1), where the sum of the coefficients is the same as the
estimated sum), then almost all senators would have predicted preferences to the left of 0.
In the modern Congress, they would almost all be Democrats. However, if we remove the
liberal effect of the proportion of poor constituents, this more than offsets the effect of
unequal representation, and the Senators almost all have predicted preferences to the right
of 0. In this accounting, unequal representation has a large substantive effect, but unequal
income group representation has a larger effect.
10
Both distributions look quite different from the observed distribution of legislator positions. This is due
to the fact that most of the variation in legislator positions is across-parties. In other words, the dependent
variable is “almost dichotomous.” The results are similar to the results obtained by a linear probability
model and a logistic regression. Predicted values to the left of 0 can be considered “Democrat” predictions,
and values to the right of 0 can be considered “Republican” predictions.
30 Figure 7: Predicted versus Observed Distribution of Ideal Points in the Senate
Observed
Predicted
Equal Representation
Equal w/o Proportions
−2
−1
0
1
Legislator Position
2
Observed
Predicted
Equal Representation
Equal w/o Proportions
−2
−1
0
1
2
Legislator Position
The left pane of this figure corresponds to an across-party model of Senator positions. The right pane
corresponds to a within-party model. In each graph, the solid line is a kernel density estimate of the
distribution of the observed data. The dashed line represents the distribution of the predicted values from
the model. The dotted line is the distribution of predicted values from a model in which the weight placed
on the preferences of each income group within each district is assumed to be equal. The distribution that
alternates dots and dashes represents the predicted values from a model in which the income group
preferences are weighted equally and the direct effect of the proportion of constituents in any group is
assumed to be 0.
The distributions in the right panel of Figure 7 show little effect of unequal
representation. However, removing the effect of the proportion of constituents in each
income group creates a large leftwards shift in the distribution for the within-party model.
31 This should not be over-interpreted, because the coefficients that are being altered in this
case were not significant to begin with.
It may be the case that representation of the preferences of the poor is important
for its own sake. Equal representation enhances the legitimacy of government, involves
citizens in democracy and creates incentive for them to become more informed. However,
policymakers wishing to improve responsiveness of policy outcomes to the preferences of
the poor would be better advised to improve overall responsiveness than to equalize
responsiveness within districts, at least in the House of Representatives. In the Senate,
equalizing representation may have a substantial effect. However, we should not assume
that income group representation and expressed preference representation are
independent. It is more likely that they are substitutes. If increasing expressed preference
representation decreases income group representation, than these effects may cancel one
another out.
Conclusion
I have shown that under-representation of the poor is prevalent, not only in the
Senate, as past work as argued, but in the House of Representatives as well. The
magnitude of the inequality is greater in the Senate. I have also established unequal
representation within parties for House Republicans and Senate Democrats. A larger
sample size and better measure of constituent ideology allows the hypothesis of equal
representation to be rejected where recent work failed to reject it. However, I also
emphasize a channel of representation for poor voters that has been ignored in past work:
32 the fact that the positions of legislators are related to the number of poor voters in their
districts. This “income group representation” favors the poor over the rich.
Although unequal representation is interesting on its own, its importance to policy
is ambiguous. In the House, the distribution of legislator positions is hardly affected by
unequal representation. In the Senate, unequal representation has a substantial affect, but
this effect is more than made up for by unequal income group representation in favor of
the poor. If income group representation and expressed preference representation are
substitutes, then it is not clear what would happen to policy if an intervention
successfully equalized representation of income groups in terms of policy preferences.
Although the current work has greater statistical power than past work in two
important respects- sample size and measurement- the total number of observations is
similar to the number of observations in past work11. This is because our observations are
district-years, not individuals. This is the primary limitation on more powerful tests, such
as simultaneously testing for unequal representation under multiple competing models of
representation, or including cross-cutting groups such as racial groups and groups by
education. Having more district-years would also allow us to investigate over-time
components of representation and to increase confidence in the results presented here.
Fortunately, the measurement of preferences presented here is highly scalable, and will
be able to incorporate new survey data as it becomes available.
Although this paper has resolved a major dispute over the existence of unequal
representation, the causes of this phenomenon have barely been mentioned. This is a live
research area, and an important subject for future work. Answering questions about
11
The exception here is that there are many more observations available in the House than there are in past
work that studies the Senate. However, the larger point is that the continued use of cross-sectional data has
constrained the number of observations.
33 causes will require combining macro-level representation studies with insights from
models of voting and elections. It will require better data on phenomena such as
abstention and information levels that may drive differences. Knowing that the poor are
under-represented is an important step, but it is less useful to policymakers and advocates
than knowing what can and should be done.
The policy implications of unequal representation are ambiguous at best, but an
important caveat should be in place. The results here pertain to district-level
representation of large constituencies. They do not speak to any national disparities, such
as the possible influence of lobbying culture in Washington, or the effect of concentrated
special interests on legislator position taking. The question of whether these factors cause
a broader institutional bias towards policies favoring the rich is an important topic for
future research.
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