Party Control and Political Agendas: The Influence of
Party on Substantive Eras of Congress∗
David Sparks†
April 20, 2010
Abstract
The substantive focus of political action within the legislature, as seen in Congressional hearings, is driven both by exogenous events and by the political agendas
of the majority party. This research applies Network-Constrained Clustering, a generalization of cluster analysis, to identify contiguous eras in the substantive focus of
legislative political elites over time. These eras are related to changes in party control
of Congress, illustrating an effect above and beyond that which would be expected
based only on the concerns of the public at large. Policy agendas, modeled as discrete
choices wherein the agenda-setter selects a set of issue emphases, are also shown to be
significantly influenced by the majority party in each chamber.
What drives the congressional agenda? Does the electoral incentive make constituent
concerns the primary motivation for taking congressional action? Do current events, as
made salient by media attention, force a policymaker response? Does partisanship and
party leadership have its own effect, apart from these exogenous influences? This paper
tests the proposition that party matters in shaping congressional agendas, developing in the
process a novel means of classifying agenda eras over time and exploring the degree to which
congressional attention is predicated on party leadership.
∗
†
Prepared for the Annual Meeting of the Midwest Political Science Association, April 2010.
Graduate in Political Science, Duke University, Durham, NC; [email protected]; (919) 724-4443
1
There is substantial literature on the classification of agendas, and the relationship between public priorities and legislative action. Baumgartner and Jones (2004), using data from
the Policy Agendas Project (Baumgartner and Jones, 2002), explore the relationship between
the Gallup Polling Organization’s Most Important Problem responses and the subject matter of congressional hearings and statues, finding that “There is an impressive congruence
between the priorities of the public and the priorities of Congress across time” (Baumgartner
and Jones, 2004, 20).
Edwards and Wood (1999) compare the influences of the president, Congress, and the
media in several issue domains, and find that neither the president nor the (television) media
appear to influence the congressional agenda on any issue but education. Rather, Congress
seems to be strongly influenced by an internal inertia, which dictates that much of its agenda
is the product of past commitments.
Another study, by Abbe et al. (2003), finds evidence that voters’ perceive that party
matters in executing a congressional agenda. If a voter agrees with a given candidate on the
nature of the most important problem facing the nation, he or she is more likely to vote for
that candidate if the candidates’ party is thought to “own” the issue. This finding implies
that voters expect for partisanship to matter in setting the congressional agenda, above and
beyond issue salience levels. Further, Taylor (1998) finds that House and Senate majority
leaders have taken an increasingly large role over time in setting the congressional agenda;
a function of changes in the rules and increased polarization. This story is very much in line
with the party-centric theories of congress put forward by Rohde (1991), Aldrich and Rohde
2
(2000), Cox (2001), and Cox and McCubbins (2005).
Essentially, the existing literature focuses primarily on the extent of the relationship
between the substantive congressional agenda and the agenda interests of the president, the
media, and the electorate. Typically, this is done by relating relative levels of salience for a
given issue area in one arena to that in another.
The present study focuses on the influence of political parties, as an additional determinant of the congressional agenda. If party is an important driver of the agenda, we should
find evidence of such in at least two places:
First, patterns in the nature of the overall congressional agenda should match patterns
in party leadership. That is, we would expect each of the two major parties to have substantively distinct sets of issues around which to form an agenda – if agendas are categorized
over time, observed changes in agenda should match changes in the party in power for a
given chamber.
Second, controlling for exogenous and secular factors, such as media interest and the
passage of time, we should observe that the partisanship of the chamber majority has a
significant impact on the amount of attention allocated to specific issues. In other words, in
modeling how the House and Senate choose to allocate their resources (time and/or attention)
across issues on the agenda, we would expect the nature of the agenda-setting party to make
a difference.
3
Two Types of Evidence
To assess the agenda as a whole means to simultaneously account for attention paid to all
subject areas. A comprehensive understanding of the most important substantive concerns in
a given Congressional session requires more than just the classification of the most attendedto issues, but rather a multi-dimensional view of the distribution of attention and energy
across all potential issues of interest.
When the topic receiving the plurality of attention is the focus of only about 12% to 20%
of all hearings, as is the case in the House and Senate respectively, and when the plurality
topic changes very little from congress to congress, knowledge of the primary topic of interest
tells us very little about the overall agenda.
Rather, I am interested in treating agendas as, in essence, coordinates in a multidimensional space, wherein time and attention are distributed across several major topic areas –
in this case, the nineteen areas coded by the Policy Agendas Project.
For example, see Figure 1: In each case, the plurality of hearings were held on Government Operations, but the distribution of attention across all topics is markedly different.
Comparing the 99th and 109th Senates, in which Public Lands and Water Management was
the second-most attended-to topic, we can see that even knowledge of the two most important issue areas is insufficient to describe the nature of the overall agenda; the agenda of the
109th Senate was much more narrowly focused on a few primary topics.
Thus, it is necessary for comparison to either consider the agenda as a whole, or to
consider all topic areas simultaneously. In this paper, I adopt both approaches.
4
Hearing Topic Distribution, for Selected Senates
79
Spa
70
60
Soc
50
40
Pub
30
20
10 Mac
0
Tra
89
Agr
Ban
Spa
300
250
Soc
200
150
Pub
100
50 Mac
0
Civ
Def
Edu
Law
Ene
Lab
Hea
Civ
Def
Edu
Ene
Env
Int
Gov
For
Hou
99
Spa
140
120
Soc
100
80
Pub
60
40
20 Mac
0
Tra
Agr
Ban
140
120
Soc
100
80
Pub
60
40
20 Mac
0
Civ
Def
Edu
Ene
Lab
Int
Spa
Tra
Agr
Ban
Civ
Def
Edu
Law
Env
Ene
Lab
For
Hea
Hea
Gov
109
Law
Hou
Ban
Lab
For
Hou
Agr
Law
Env
Int
Tra
Env
Int
Gov
For
Hou
Hea
Gov
Figure 1: Distribution of hearing topics across several selected Senates.
5
Agenda Eras
The first approach is to identify discrete eras in the history of the congressional agenda.
That is, given information about the level of attention devoted to each of a set of issue areas
over time, at which points in the congressional timeline can we reasonably say that there
has been a shift between two contiguous observations?
I will do this by use of a (network-) constrained cluster analysis technique. Cluster
analysis would partition a set of observations, in this case the agendas of each Senate and
House from 1945 to 2006, into groups based on their similarity. To generate substantive
eras over time, however, I employ a constraint that requires all partitions to be internally
contiguous.
This method has been fairly well developed in the statistical (Ferligoj and Bategelj, 1983;
Murtagh, 1985) and data mining (Basu, Davidson and Wagstaff, 2008) literature. For specific
algorithmic approaches, see Murtagh (1985) and Gordon (1996). I base my implementation,
done in R, on that of Recchia (2010), who develops such code for SAS.
It is useful to find discrete era partitions over time because I am interested in whether
a change in party leadership is associated with change in the agenda. An alternative approach, not employed here, would be to partition senate-agendas and house-agendas into
two unconstrained clusters, and measure how well these partitions align with a two group
partition based on party majority control. I adopt the constrained approach here because I
am interested in the descriptive task of identifying eras and it is more useful analytically to
compare the incidence of partition boundaries over time, as such clusters can be better re-
6
lated to changes in leadership and are more numerous than a binary Republican/Democratic
majority status partition would produce.
Attention Allocation Choices
Another approach is to model the choices of party leadership about how to allocate time and
attention across the set of possible topics. Here, I look for the effect of partisan control on
the relative emphases placed on various issues, controlling for external influences. If party
matters in shaping the agenda, we would expect that knowledge about which party controls
a chamber will allow us to better predict the topics on which congressional hearings are to
be held.
In this paper, I do this by use of a multinomial probit model, which takes as a multivariate
dependent variable the percentage of hearings held on a topic, and estimates the effect of
external factors and party control on issue-area probabilities. Rather than explicitly include
the preferences of the president, the public, and/or the media, I use indicator variables for
each congress number, as a way to simultaneously account for all issue emphases external to
party control, over time.
7
Data and Analysis
The data used in this analysis is from the Policy Agendas Project1 , and consists of the
major topic categorization assigned to each congressional hearing held in the U.S. House
and Senate from the 79th to 109th congresses. Generally, the greatest number of hearings
in the House are held on Banking, Finance, and Domestic Commerce and International
Affairs and Foreign Aid, while the Senate devotes the plurality of hearings to Public Lands
and Water Management and Government Operations. Government Operations consists of
subtopics such as intergovernmental relations, bureaucratic oversight, postal service issues,
nominations and appointments, and campaign regulation, making it a very broad category.2
Figures 5 and 6 in the appendix attempt to convey the volume and heterogeneity of
hearing topics over time for the House and Senate. In general, the distribution of attention
across issues is more heterogeneous in later years, but peaks in both chambers in the mid- to
late-1980s. It is also worth noting that the number of hearings in the House are unsurprisingly
much higher than in the Senate, and House attention appears to be consistently more widely
distributed/less concentrated across issues.
Agenda Eras
I use a Euclidean distance measure between the percentages of hearings held by each House
and Senate to generate a matrix of similarities between each pair. Using Ward’s agglom1
Originally collected by Frank R. Baumgartner and Bryan D. Jones, with the support of National Science
Foundation grant number SBR 9320922, and were distributed through the Department of Government at the
University of Texas at Austin and/or the Department of Political Science at Penn State University. Neither
NSF nor the original collectors of the data bear any responsibility for the analysis reported here.
2
http://www.policyagendas.org/codebooks/topicindex.html
8
erative clustering method on these similarities, with a contiguity constraint, I identify 10
agenda eras in the Senate and 6 in the House, basing those numbers on the number of
majority transitions in each chamber over the time period in question.
Constrained Clusters of Congresses over Time
House
Senate
6
10
5
8
Cluster
4
Basis
6
Agenda
Majority
3
4
2
2
1
80
85
90
95
100
105
80
85
90
95
100
105
Congress
Figure 2: Comparison of agenda eras and eras of party control over time.
As Figure 2 and Table 1 (see Appendix) indicate, the fit between the agenda and majority
partitions is better for the Senate than for the House, and in both cases, the fit is better in
more recent years. To explicitly measure fit, I employ a metric similar to the variation of
information (Meila, 2007; Grimmer and King, 2009) to estimate similarities between clusterings. Essentially, for every pair of observations to be clustered (that is, 312 − 31 = 930
pairs of congresses), I note whether both observations are sorted into the same partition.
Sets of clusterings are similar to the extent that they group paired observations in similar
ways. That is, any given partition can be represented by the vector computed by equation
9
1, and similarities between partition sets are calculated according to equation 2, bounded
by 0 if no pairs are clustered similarly, and 1 if all pairs are clustered similarly by two given
vectorized representations of partitions, a and b.
ClusterV ector =
N
X
N
X


 1,
if Clus(i) = Clus(j)

if Clus(i) 6= Clus(j)
i=1 j=1;j6=i  0,
2 −N
NX
k=1
P artitionSimilarity(a, b) =
(1)


 1,


if CVa (k) = CVb (k)
0, if CVa (k) 6= CVb (k)
N2 − N
(2)
In this analysis, agenda-based clustering of Senates matches the leadership-era clusterings
for 83.0% of observation pairs, while the analogous figure for the House is 64.1%. To get an
idea about statistical significance, I assess the fit of 50, 000 randomly-generated contiguous
partitions with the party-based partitions. The observed similarity for the Senate clusterings
is better than that of 78.6% of these random partitionings, while the observed fit for the
House is better than only 53.7% of those based on simulated data. This is in line with the
impression gained from Figure 2, which suggests a relatively poorer fit for House data. In
general, neither of these results could be characterized as strongly statistically significant,
although the level of partition similarity observed in the Senate suggests the possibility of a
true relationship.
10
Partisan Choices
Approaching the question of party importance from a different angle, I model the distribution
of hearings across all nineteen major topics as the outcome of a choice process, predicted
by the party in power within a chamber, as well as a series of indicator variables intended
to represent any contemporaneous external influences on legislative preferences about the
substance of the agenda.
Table 2 in the Appendix displays the results of a multinomial probit model, illustrating
the coefficients for relative topic salience as a function of Democratic majority status in a
chamber. Figure 3 depicts these estimates graphically.
Estimated Effects of Democratic Majority on Agenda Topic Focus
House
Senate
●
Transportation
●
●
Space, Science, Technology, and Communications
●
●
Social Welfare
●
●
Public Lands and Water Management
●
●
Macroeconomics
●
●
Law, Crime, and Family Issues
●
●
Labor, Employment, and Immigration
●
●
International Affairs and Foreign Aid
Housing and Community Development
●
Health
●
●
●
●
Government Operations
●
●
●
Environment
●
●
Defense
●
●
●
●
−1.0
−0.5
FALSE
●
●
Banking, Finance, and Domestic Commerce
TRUE
●
●
●
Energy
Civil Rights, Minority Issues, and Civil Liberties
●
●
Foreign Trade
Education
95% C.I.
Includes
Zero
●
●
0.0
0.5
1.0
−1.0
−0.5
0.0
0.5
1.0
Figure 3: Multinomial probit estimates of the effect of Democratic majority status on the
relative salience of each topic, controlling for exogeneous influences.
In the House, the percentage of hearings on Social Welfare, Foreign Trade, and the
11
Environment are positively affected by Democratic majority status, while Public Lands and
Water Management, Government Operations, and Education are negatively influenced. In
the Senate, there are significantly fewer hearings on International Affairs and Foreign Aid
and Government Operations, and more held on Housing and Community Development and
the Environment, when Democrats are in the majority. It is important to note that as coded,
these topical categories are not valenced. That is, for example, hearings on the Environment
may be held to investigate the implications of expansions or contractions in the level of
environmental regulation – we can only say that the presence of a Democratic majority
appears to increase amount of attention paid to environmental issues, good or bad.
It can be tentatively concluded that party has some real effect on the nature of the
agenda. If these estimated effects were merely artifacts of the 95% confidence intervals, we
would expect to observe fewer of them than are present in the data. Additionally, the Akaike
Information Criterion suggests modest improvements in fit given the addition of the party
majority predictor variable in both chambers.
Predicting Majorities
As a final test of the thesis of party importance, I explore how well knowledge of the distribution of attention across topics can be used to predict the size of the Democratic majority
(i.e. number of Democrats less the number of Republicans serving in a chamber, a negative
value when Republicans are in the majority). OLS regression of the proportion of hearings
held on each topic can explain 53.5% of the variation in the magnitude of the Democratic
12
majority in the Senate, and 56.8% in the House, with no additional predictor variables. As
a summary of the accuracy of this classification, see Figure 4
Prediction of Democratic Majority Size,
by Congressional Hearing Agenda
House
Senate
●
150
●
●
Actual Democratic Majority
●
●
100
●
●
● ●
●
●
●
●
●
30
●
●
●
●
●
●
●
●
●●
●
●
●
●
●
●●
10
Number of
Hearings
●
0
●
0
●
−50
●
●
●
●
●
−10
●
−50
0
50
100
Correct
●
Incorrect
20
●
●
●
●
●
●
●
●
50
●
Majority
Prediction
●●
−10
0
10
500
●
1000
●
1500
●
2000
20
Predicted Democratic Majority
Figure 4: Predicted versus actual difference in the number of Democrats and Republicans
by chamber. Congresses for which this method incorrectly identifies the majority party are
marked with an X.
There is clearly a tight fit between predicted and actual Democratic advantage, and the
majority party is correctly identified in 83.9% of the cases in the House, in and 77.4% of
Senate cases. This serves as further evidence of the relationship between the substance of
the congressional agenda and the party dictating that agenda.
Discussion and Conclusions
This paper offers several tests of the hypothesis that the party of the majority in congress
has some measurable influence on the nature of the congressional agenda. Certainly, there
13
are a litany of other factors that influence the determination of the agenda, from presidential
pressure to media emphasis to constituent concerns, all of which may play a major role in
suggesting priorities to those who ultimately determine the agenda. However, there is at
least some support for the notion that party control makes a difference in deciding which
issues receive attention.
The use of any single aspect of the agenda, such as the amount of legislation pertaining
to education, individually, as a conceptualization of The Congressional Agenda, can be
problematic. Rather, I argue that a more robust understanding of the agenda considers the
entire program as a set of bundled emphases or priorities, reflecting a multidimensional set of
preferences over how to spend limited time and attention resources. Analysis is made more
straightforward by collapsing the dimensionality of these multifaceted observations, and in
this paper, I explored how to do so by means of constrained clustering.
This clustering technique was based on 31 congresses, each represented by a nineteendimensional vector of attention priorities, reducing them to groups of similarly-constituted
congresses along a timeline. For the purposes of testing the working hypothesis of party
influence, these groupings were compared to those derived from changes in party control,
with results that indicated the possibility, but not probability, of a systematic correlation.
Given that the low-dimensionality approach, which discarded substantial information,
failed to show signs of party influence, an approach that took advantage of the variation in the
data was merited. Attempts at both predicting legislative priorities from partisanship, and
partisanship from observed legislative priorities offered evidence to support the claim that
14
the two are related. Differences were found in the parties’ relative propensity to hold hearings
on several issues, and conversely, it is possible to predict fairly accurately the magnitude of
the Democrats’ representation advantage by reference to the priorities observed in a given
congress.
Thus, my findings are very much in line with those of Taylor (1998). There is some
evidence for the pronounced role of House and Senate leaders in determining the agenda,
and their influence seems to have been particularly pronounced in the post-reform era. To the
extent that the agenda-based partitions differ from those based on the majority party, those
differences generally disappear around the 93rd - 95th congresses, which began the postreform era. Reference to Figure 2 illustrates the pronounced shift in correlation between
these two sets of partitions.
This paper took a simplified approach wherein all external factors were accounted for
by use of indicator variables for each of the 31 congresses, but future work could attempt
to more clearly explicate the relative degree of influence held by various entities over the
agenda, in comparison to the internal preferences of party leadership. Further, it would be
worthwhile to explore the valence or policy direction taken in approaching various issues –
can it be reliably said that, controlling for other factors, Republican leaders prefer to move
a certain set of issues in a certain direction, while Democratic leaders adopt a different set
of valenced priorities?
Such data would help refine the understanding offered here about the issues that voters
can associate with parties’ “brand names.” Figure 3 suggests that a Democratic majority can
15
be associated with increased attention on Social Welfare, Housing and Community Development, Foreign Trade, and the Environment, while Republican leadership correlates with
increased focus on Public Lands and Water Management, International Affairs and Foreign
Aid, Government Operations, and Education. While several of these findings align with
commonly-held understandings of issue ownership by each party, others do not. However,
given that 16 of 19 coefficients from the multinomial logit estimation share the same sign
(even if they are not both significant) across chambers, it appears that party may have consistent and real implications for agenda construction in general. More finely-grained data
on the specific nature of party leaders’ intentions within each major topic could allow us
greater confidence in drawing conclusions about issue ownership and voters’ understandings
of policies likely to be pursued by their preferred party.
References
Abbe, Owen G., Jay Goodliffe, Paul S. Herrnson and Kelly D. Patterson. 2003. “Agenda
Setting in Congressional Elections: The Impact of Issues and Campaigns on Voting Behavior.” Political Research Quarterly 56(4):419–430.
Aldrich, John H. and David Rohde. 2000. The Consequences of Party Organization in the
House: The Role of the Majority and Minority Parties in Conditional Party Government.
CQ Press pp. 31–72.
Basu, Sugato, Ian Davidson and Kiri Lou Wagstaff. 2008. Constrained clustering: advances
in algorithms, theory, and applications. CRC Press.
Baumgartner, Frank D. and Bryan D. Jones. 2004. “Representation and Agenda Setting.”
The Policy Studies Journal 32:1–24.
Baumgartner, Frank R. and Bryan D. Jones, eds. 2002. Policy Dynamics. University of
Chicago Press.
Cox, Gary W. 2001. “Agenda Setting in the U. S. House: A Majority-Party Monopoly?”
Legislative Studies Quarterly 26(2):185–210.
16
Cox, Gary W. and Mathew D. McCubbins. 2005. Setting the Agenda : Responsible Party
Government in the U.S. House of Representatives. Cambridge University Press.
Edwards, George C. and Dan B. Wood. 1999. “Who Influences Whom? The President,
Congress, and the Media.” The American Political Science Review 93:327–344.
Ferligoj, Anuska and Vladimir Bategelj. 1983. “Some Types of Clustering with Relational
Constraints.” Psychometrika 48:541–552.
Gordon, A.D. 1996. “A Survey of Constrained Classification.” Computational Statistics &
Data Analysis 21:17–29.
Grimmer, Justin and Gary King. 2009. “Quantitative Discovery from Qualitative Information: A General-Purpose Document Clustering Methodology.” SSRN eLibrary .
Meila, Marina. 2007. “Comparing Clusterings: An Information Based Distance.” Journal of
Multivariate Analysis 98:873–895.
Murtagh, F. 1985. “A Survey of Algorithms for Contiguity-Constrained Clustering and
Related Problems.” The Computer Journal 28:82–88.
Recchia, Anthony. 2010. “Contiguity-Constrained Hierarchical Agglomerative Clustering
Using SAS.” Journal of Statistical Software 33:1–12.
Rohde, David. 1991. Parties and Leaders in the Post-Reform House. University of Chicago
Press.
Taylor, Andrew J. 1998. “Domestic Agenda Setting, 1947-1994.” Legislative Studies Quarterly 23:373–397.
17
% of Hearings
18
0
10
20
30
40
50
60
70
80
90
100
House Hearing Topics
Figure 5: Hearings held, by primary topic, over time in the House.
Congress Number
(Total Hearings)
)
61
(13 )
9
10 705
1
8(
10 663)
1
7(
)
10
99
(18
6
)
10
38
(17
5
10 595)
1
4(
)
10
33
(21
3
10
)
27
(23
2
10
)
60
24
(
1
10
)
03
23
(
0
10
)
08
(21
9
9
)
74
(21
8
9
)
64
21
(
97
)
01
22
(
96
)
84
(19
5
9
)
53
(17 )
4
9
63
(13 )
93 1073
)
(
92 1108
( 0)
1
0
9 (8 )
90 (8648)
89 (756)
88 (778)
87 (77 7)
86 154
(
85 50)
(14
84 326)
(1 )
83 1078
(
)
82 1336
(
81 57)
(18
80 503)
(
79
Topic
Transportation
Space, Science, Technology,
and Communications
Social Welfare
Public Lands
and Water Management
Macroeconomics
Law, Crime,
and Family Issues
Labor, Employment,
and Immigration
International Affairs
and Foreign Aid
Housing
and Community Development
Health
Government Operations
Foreign Trade
Environment
Energy
Education
Defense
Civil Rights, Minority Issues,
and Civil Liberties
Banking, Finance,
and Domestic Commerce
Agriculture
% of Hearings
19
0
10
20
30
40
50
60
70
80
90
100
Senate Hearing Topics
Figure 6: Hearings held, by primary topic, over time in the Senate.
Congress Number
(Total Hearings)
7)
(70 )
9
10 (939
)
8
10 161
1
(
7
10 203)
1
6(
10 18)
9
5( )
10 (929
)
4
10 1122
(
3
10 279)
1
2(
)
10
11
(14
1
)
10
38
(12
0
10 83)
(10
)
99
78
(13
8
9
)
24
(15
97
)
27
(16
96
)
83
(15
5
9
)
49
14
(
94
)
48
(14
3
9
)
81
(11
2
)
9
19
(13
1
9
6)
(92
90 07)
(9
89 05)
(9
88 016)
(1
87 06)
(9
86 27)
(9
85 026)
(1
84 72)
(11
83 04)
(7
82 856)
(
81 797))
( 2
0
8 (26
79
Topic
Transportation
Space, Science, Technology,
and Communications
Social Welfare
Public Lands
and Water Management
Macroeconomics
Law, Crime,
and Family Issues
Labor, Employment,
and Immigration
International Affairs
and Foreign Aid
Housing
and Community Development
Health
Government Operations
Foreign Trade
Environment
Energy
Education
Defense
Civil Rights, Minority Issues,
and Civil Liberties
Banking, Finance,
and Domestic Commerce
Agriculture
Congress
79th
80th
81st
82nd
83rd
84th
85th
86th
87th
88th
89th
90th
91st
92nd
93rd
94th
95th
96th
97th
98th
99th
100th
101st
102nd
103rd
104th
105th
106th
107th
108th
109th
Begin
1945
1947
1949
1951
1953
1955
1957
1959
1961
1963
1965
1967
1969
1971
1973
1975
1977
1979
1981
1983
1985
1987
1989
1991
1993
1995
1997
1999
2001
2003
2005
End Majority
1947
D
1949
R
1951
D
D
1953
1955
R
D
1957
1959
D
D
1961
1963
D
D
1965
1967
D
1969
D
D
1971
1973
D
1975
D
D
1977
D
1979
1981
D
R
1983
1985
R
1987
R
D
1989
1991
D
1993
D
D
1995
1997
R
1999
R
2001
R
2003
D
2005
R
2007
R
Senate
Cluster E.N. Topics Majority
10
6.0
D
10
8.1
R
10
6.6
D
10
4.7
D
1
7.3
R
1
7.6
D
5
7.9
D
5
7.5
D
5
6.2
D
7
6.9
D
7
6.7
D
7
7.9
D
2
10.9
D
2
12.1
D
8
11.5
D
8
12.4
D
8
11.8
D
8
11.9
D
9
10.2
D
9
13.0
D
9
14.2
D
9
14.1
D
6
12.8
D
6
13.3
D
6
10.5
D
4
12.6
R
4
11.1
R
4
13.4
R
3
11.7
R
3
11.4
R
3
9.9
R
House
Cluster E.N. Topics
5
5.3
5
7.1
5
6.3
5
5.6
5
6.7
5
7.0
6
6.9
6
9.4
6
10.8
6
10.7
6
10.8
6
11.0
1
14.2
1
13.6
1
13.1
1
14.8
3
16.2
3
15.2
3
16.3
3
17.4
3
16.8
2
15.8
2
16.0
2
16.0
2
15.6
4
12.5
4
13.7
4
14.8
4
14.5
4
13.5
4
12.9
Table 1: Agenda- and majority-based partitions of congresses over time, with a measure of
the diversity of issue attention, the Effective Number of Topics, an inverse Herfindahl index
of concentration.
20
Topic
Banking, Finance,
and Domestic Commerce
Civil Rights, Minority Issues,
and Civil Liberties
Defense
Education
Energy
Environment
Foreign Trade
Government Operations
Health
Housing and Community Development
International Affairs and Foreign Aid
Labor, Employment, and Immigration
Law, Crime,
and Family Issues
Macroeconomics
Public Lands and Water Management
Social Welfare
Space, Science, Technology,
and Communications
Transportation
Senate
Coef. S. E.
z
House
Coef. S. E.
z
-0.06
0.17
-0.36
-0.08
0.12
-0.67
-0.39
-0.26
0.00
0.18
0.70
0.25
-0.34
0.20
0.45
-0.86
0.01
0.25
0.17
0.24
0.19
0.19
0.20
0.16
0.17
0.23
0.21
0.20
-1.56
-1.49
0.01
0.97
3.70
1.23
-2.11
1.15
1.96
-4.09
0.04
-0.21
-0.05
-0.49
0.21
0.50
0.35
-0.46
0.14
0.14
-0.19
0.06
0.16
0.12
0.17
0.14
0.13
0.15
0.12
0.12
0.16
0.12
0.13
-1.35
-0.47
-2.89
1.51
3.91
2.35
-3.89
1.17
0.84
-1.60
0.43
-0.22
-0.02
-0.15
0.08
0.19
0.21
0.16
0.24
-1.14
-0.11
-0.88
0.31
-0.08
-0.18
-0.62
0.51
0.13 -0.63
0.16 -1.09
0.13 -4.78
0.22 2.38
-0.06
0.14
0.22
0.19
-0.27
0.75
0.10
-0.20
0.15 0.69
0.14 -1.48
Table 2: Multinomial probit estimates of the effect of Democratic majority status on the
relative salience of each topic, controlling for exogenous influences.
21