mapping political communities: a statistical analysis of...
TRANSCRIPT
Mapping Political Communities: A Statistical Analysis
of Lobbying Networks in Legislative Politics∗
In Song Kim† Dmitriy Kunisky‡
First Draft: August 25, 2017This Draft: June 13, 2018
Abstract
Political connections between special interest groups and politicians play a significant rolein policymaking. Yet, empirical studies of interest group politics and representation have beenfundamentally limited by the difficulty of observing these ties directly. We bridge the two withan original dataset of two political behaviors around congressional bills: (1) sponsorship bypoliticians, and (2) lobbying by interest groups. We then develop a methodological frameworkto examine the political networks these data describe. Unlike networks in electoral politics,whose structure has been found to reflect ideology, we find distinct political communities inthe lobbying network, organized according to industry interests and jurisdictional committeememberships. Furthermore, we observe important actors having mixed memberships in multiplecommunities, capturing their simultaneous commitments to diverse political interests. Ourfindings provide evidence for the existence of powerful networks in U.S. legislative politics,quite distinct from electoral networks and exhibiting numerous unique structural features.
Keywords: Network analysis, lobbying, ideal point estimation, scaling, stochastic block model,link community model, community detection.
Word Count: 12,029 (abstract: 149)
∗We thank Pablo Barbera, J. Lawrence Broz, Devin Caughey, Marina Duque, Nolan McCarty, Cristopher Moore,Michael Peress, Yunkyu Sohn, Erik Voeten, and Hye Young You for helpful comments. We also thank seminarparticipants at the American Political Science Association annual meeting, Asian Political Methodology annualmeeting, Princeton University, University of California, San Diego, and University of Maryland. Kim acknowledgesfinancial support from the National Science Foundation (SES-1264090 and SES-1725235).†Assistant Professor, Department of Political Science, Massachusetts Institute of Technology, Cambridge, MA,
02139. Email: [email protected], URL: http://web.mit.edu/insong/www/‡Ph.D. Student, Department of Mathematics, Courant Institute of Mathematical Sciences, New York University,
New York, NY, 10012. Email: [email protected].
1 Introduction
Special interest groups engage in lobbying to promote their political objectives (e.g., Wright, 1996;
Grossman and Helpman, 2001).1 A dominant view among political scientists holds that interest
groups take part in such costly political activity to retain access to policymakers, and in return
policymakers gain an effective means of informing their legislative decisions (Bauer, Dexter, and
Poll, 1972; Potters and Van Winden, 1992; Austen-Smith and Wright, 1992; Wright, 1996). Others
consider whether policymakers might be compensated more directly through the related mechanism
of campaign contribution (Austen-Smith, 1995; Ansolabehere, Snyder, and Tripathi, 2002). Yet
another theory is that lobbying serves instead as a “legislative subsidy,” through which interest
groups help allied politicians pursue common objectives (Hall and Deardorff, 2006). In any case,
despite the significance of connections between interest groups and politicians for both politics and
economics (Khwaja and Mian, 2005; Faccio, Masulis, and McConnell, 2006; Faccio, 2006; Kang and
You, 2017), empirical studies of legislative politics have been limited by the difficulty of directly
observing these political ties, let alone the actual policy context in which they are formed.
In this paper, we identify a type of political connection that can be observed tractably between
every Member of the U.S. Congress and every actively lobbying special interest group. Our first
contribution is to construct a large political network dataset tracing the universe of lobbying
activities that are directly related to 108,086 congressional bills introduced between the 106th and
the 114th Congress. We then consider for further analysis a subset of these data joining two specific
types of political behavior around each bill: (1) sponsorship by a politician, and (2) lobbying by
interest groups. Although lobbying on a single bill does not necessarily imply political ties to its
sponsor, recurring instances of lobbying that involve the same interest group and sponsor across
several bills do reliably encode close political relationships.2 Therefore, analyzing how often various
politicians and interest groups coincide on individual bills lets us infer the structure of political
networks underlying the legislative process.
The left panel of Figure 1 shows that about 12,000 bills are introduced in each Congress and the
majority of them are lobbied by at least one interest group, while very few are eventually voted on
the floor. Note that the remaining bills do not necessarily “die.” Rather, they tend to be merged
into larger bills that are eventually voted on the floor through a complex process of amendment
and compromise. Our dataset allows us to observe political connections before this process takes
place, and therefore at a finer level of granularity. Indeed, as the right panel shows, most bills are
1We use the term special interest groups to refer to any political actors who have particular policy objectives,including firms, trade associations, labor unions, business associations, and professional associations.
2There is ample empirical evidence that lobbyists help to draft or even write bills on behalf of legislators withwhom they have political connections (Nourse and Schacter, 2002; Hertel-Fernandez, 2014).
1
106 108 110 112 114Congress
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ills Bills Lobbied
Bills Introduced
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0 50 100 150 200+Number of Unique Interest Groups Lobbying per Bill
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f Bills
in 1
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Figure 1: Descriptive Statistics of Lobbying on Congressional Bills. The left panel com-pares the numbers of bills introduced, lobbied, and voted on between the 106th and the 114thCongress. On average, about 12,000 bills are introduced in each Congress, only about 600 areeventually voted on the floor, but the majority are lobbied by at least one interest group. Theright panel shows the distribution of the number of interest groups lobbying on each bill in the113th Congress. The distribution is highly skewed: the median is three, the maximum is 978 and25% of the lobbied bills are lobbied by only one interest group. Many more bills are identified aslobbied beginning in the 110th Congress, when lobbying reports became digitized per the HonestLeadership and Open Government Act of 2007.
lobbied by very few interest groups, implying that each individual instance of lobbying tends to
reflect narrow interests. A typical example: on “A bill to exempt the aging process of distilled
spirits from the production period for purposes of capitalization of interest costs” (113th S. 1457),
sponsored by Senator Mitch McConnell (R-KY), the Distilled Spirits Council of the U.S.
was the only interest group to lobby.3
Our second contribution is to develop statistical network models to examine the underlying
factors that might drive political connections. We begin by introducing a latent space model, an
extension of the widely used item response theory (IRT) model, to estimate a latent “ideal point”
or “preferred policy” for each political actor. This approach has been widely used to study various
legislative bodies, including state legislatures (Shor and McCarty, 2011), the U.S. Congress (Fowler,
2006), and the United Nations (Bailey, Strezhnev, and Voeten, 2017). Unlike existing studies that
rely on roll calls for the few bills that are voted on the floor (Poole and Rosenthal, 2011; Clinton,
Jackman, and Rivers, 2004), we analyze all introduced bills to infer underlying policy preferences,
as reflected at the point of sponsorship.
Our model locates legislators and interest groups in a common “marketplace,” in which prox-
imity implies a closer alignment of unobservable interests. We find that, in this marketplace,
there is clustering of interest groups and legislators according to their industry affiliations and
3See Kim (2017) for examples of similar patterns in trade bills.
2
memberships in committees with jurisdiction over those industries, respectively. This is different
from findings based on similar models in other political contexts, such as campaign contributions
and social media following (Bonica, 2013; Barbera, 2014; Bond and Messing, 2015), where the
revealed underlying preferences tend to align strongly with existing measures of ideology, such as
DW-NOMINATE scores (Poole and Rosenthal, 2011). Instead, our finding is consistent with the
thesis of Ansolabehere, Snyder, and Tripathi (2002), who argue that political actors who act mainly
through lobbying are more bipartisan and less ideological than those who act mainly through cam-
paign contributions. It also attests to the influence committees have over policy outcomes through
intervention at several stages of the legislative process, as described by Shepsle and Weingast
(1987). In short, interest groups target influentially-positioned legislators in interactions localized
to specific policy domains, forming communities in the lobbying network.
We then propose a new methodological framework tha incorporates the community structure we
identify in the spatial model. We begin by applying the stochastic block model, a sociological tool
(Snijders and Nowicki, 1997) more recently adopted in physics and machine learning (Fortunato,
2010). We verify that the communities identified by the stochastic block model largely agree
with those identified ex post in the latent space model, confirming that the lobbying network
exhibits significant community structure. However, we also observe an important and qualitatively
different behavior in the lobbying network, namely that many interest groups and politicians tend
to represent mixed interests across several political communities.
To study the effects of this, we develop a new statistical model that allows actors to belong to
multiple political communities (so-called “mixed membership”). The proposed model finds that
interest groups such as the Chamber of Commerce that represent the heterogeneous interests of
many members engage in aggregate lobbying, highly mixed among many communities, often target-
ing politicians who are members of broad “procedural” committees such as the House Committee
on Ways and Means and the Senate Committee on Appropriations. This is consistent with
Wright (1990) who, based on numerous personal interviews and a survey of lobbyists, found the
prevalence of lobbying by powerful interest groups to affect politicians’ voting especially strongly
within the House Committee on Ways and Means. Our model also captures the intermedi-
ate lobbying behavior of firms involved in a few industries at once, and the focused lobbying of
industry-specific organizations observed previously. In this regard, the proposed methodology im-
proves our understanding of the high-level organization of lobbying in the U.S. Congress, accurately
capturing the full spectrum of behaviors from very targeted to very broad lobbying.
Taken together, our results illustrate a nuanced community structure in the lobbying network,
remarkably different from most political networks studied previously. We find evidence undermining
3
the broad claim that “lobbying is specialized,” which plays a key role in the argument of Hall and
Deardorff (2006), as our analysis shows that mixed interests and the spectrum between specialist
and generalist interest groups are in fact crucial distinctions for describing lobbying activity.4 The
lobbying network, we find, must be understood as a collection of interlocking topical “arenas” in
which lobbying on particular issues occurs, and both the individual arenas and the mechanism of
mixed interests through which they interact are important in generating the diversity of behavior
observed in the lobbying network. More broadly, our results call for the mechanisms through
which committee power is exercised to express policy preferences in legislative politics to be more
carefully considered, as has been previously explored by Shepsle and Weingast (1987); Schickler
(2001); Sinclair (2016); and others. In particular, these mechanisms cannot be understood merely
by analogy with public-facing politics: our results show that the interactions of legislators with
interest groups are quite different from their position-taking interactions with the public at large
as observed through roll call votes and the like, the latter being driven primarily by electoral
motivations (Mayhew, 1974).
We also believe that our network analyses will provide useful technical guidance for further
research in this direction. To the best of our knowledge, ours is the first statistical study of
lobbying networks in legislative politics in their entirety, including both politicians and interest
groups. The proposed community detection approach introduces a novel empirical framework for
applied political scientists to systematically identify political communities and characterize their
memberships, which will help leverage the rich network data that have become increasingly available
in various subfields of political science (Keck and Sikkink, 1998; Hoff, Raftery, and Handcock, 2002;
Maoz et al., 2006; Hafner-Burton, Kahler, and Montgomery, 2009; Clark and Lauderdale, 2010;
Ward, Stovel, and Sacks, 2011).
The rest of the paper is organized as follows. Section 2 introduces a database of lobbying and
a network dataset obtained from it. In Section 3, we introduce a Bayesian Latent Space Network
Model (LSNM) and apply it to lobbying data from the 113th Congress. We then introduce the
community detection framework and develop the Bipartite Link Community Model (biLCM) in
Section 4. Lastly, Section 5 gives some concluding remarks. Open-source software implementing
the proposed methods will be made available as a package for the R and Python languages. The
network data, all estimated pairwise measurements of political connections between politicians and
interest groups, the estimated spatial preferred policy locations and their posterior distributions,
and the visualization tools used in preparing this paper will also be made publicly available.
4We do not address in this work the more intricate question of whether lobbyists, rather than interest groups, aretypically specialists or generalists, though we believe the lobbying network dataset will allow future work to conductsuch analyses.
4
2 The Lobbying Network Database
The Lobbying Disclosure Act of 1995 (amended by the Honest Leadership and Open Government
Act of 2007) requires mandatory quarterly electronic filing for any organization (“client”) that
“actively participates” in lobbying. Filings must disclose the general lobbying issue area, the con-
gressional and federal agencies contacted, and the specific issues on which lobbyists (“registrants”)
have engaged in political activities.5 Based on the information available from this disclosure of
political activities, researchers have studied various aspects of the politics of lobbying, including
the sources of and returns on decisions to lobby (Richter, Samphantharak, and Timmons, 2009;
De Figueiredo and Richter, 2014; Kang, 2015; You, 2017), the similarities and differences between
campaign contribution and lobbying (Ansolabehere, Snyder, and Tripathi, 2002), characteristics of
lobbyists (Baumgartner et al., 2009; Vidal, Draca, and Fons-Rosen, 2012; Bertrand, Bombardini,
and Trebbi, 2014), and the implications of lobbying for trade politics (Bombardini and Trebbi,
2012; Kim, 2017). However, the available lobbying data has the important limitation that interest
groups are not required to report the identities of their political contacts. This is unfortunate, given
that almost 90% of lobbying reports indicate that at least one Member of Congress or a member
of their staff was indeed contacted. In order to study the connections between interest groups and
politicians, there is therefore no choice but to infer those unobserved connections statistically from
the available data.
To that end, we construct an original database describing the lobbying network, which consists
of all of the links described above among lobbyists, interest groups, bills, and politicians. The
database is generated from lobbying reports filed between 1999 and 2017, which are available
from the Senate Office of Public Records (SOPR). A key component of the database is a suite of
automated systems to (1) detect congressional bills reported to have been lobbied, (2) identify the
session of Congress those bills occurred in, and (3) relate each bill to its sponsor. Below, we give
a brief outline of the process used to construct the lobbying database.
First, we identify all lobbying activities related to legislative bills, which is possible due to 2
U.S.C. §1604(b)(2)(A) legally requiring interest groups to disclose the “list of bill numbers” lob-
bied.6 For example, a report filed by Intel Corporation mentions “H.R. 289, National STEM
Education Tax Incentive or Teachers Act of 2011—Science and math education legislation.” In
practice, bills are often referenced within the text of a lobbying report, and sometimes only by
alternative names or subtitles, so the entire report text must be processed and all bill references
5See https://lobbyingdisclosure.house.gov/amended_lda_guide.html for the definition of lobbying.6Compliance with disclosure requirements is closely monitored and enforced. It is annually audited by the Govern-
ment Accountability Office (GAO). According to the 2014 audit report by GAO, 90% of organizations filed reports asrequired, and 93% could provide documentation related to expenses. The 2014 GAO report on lobbyists’ compliancewith disclosure requirements is available from http://www.gao.gov/products/GAO-15-310.
5
algorithmically identified. Second, we identify the session of Congress that each bill belongs to,
which often is not mentioned explicitly, as in the above example. We use text data mining tech-
niques to determine whether information such as the bill title or phrases like “Act of [YEAR]”
occur in the report text. Based on our algorithm, described in greater detail in Appendix A.1,
we correctly determine that, for instance, the above bill H.R. 289 is from the 112th Congress.
If the year that the lobbying report was filed had been used to determine the Congress instead,
then this bill would have been identified as H.R. 289 “Value Our Time Elections Act” from the
113th Congress (since the report was filed in 2013).7 Finally, we identify the sponsor of each bill,
completing the connection between lobbying clients and politicians. We repeat this process over
each of 1,111,859 lobbying reports, which in total link 20,092 special interest groups with 1,164
(current and former) Members of Congress.
Figure A.3 in the Appendix shows that lobbying and sponsorship tend to be heavily skewed
in their frequency, in that most politicians and interest groups have very few interactions, while a
very small number are highly active. The incidences of politicians and interest groups on bills are
similarly skewed, and are moreover very sparse, most pairs of actors not coinciding on any bills at
all, and a few coinciding on many. For instance, 98.28% of pairs of actors in the 113th Congress do
not coincide on any bills, while the pair with the greatest number of bills lobbied and sponsored
in common during the 113th Congress consists of Senator Bernard Sanders (D-VT) and the Iraq
and Afghanistan Veterans of America, having 25 common bills; we will later see that a
group of veterans’ associations in fact forms one of the significant clusters in our models, which
ultimately belongs to a larger but subtler cluster related to issues of civil society. More details on
our datasets are given in Appendix A.2.
Notations Before describing our models, we introduce some mathematical notation and short-
hand terminology that will be useful in the sequel. We index a set of interest groups (we will
sometimes use the term “lobbying clients” or simply “clients”, this being the terminology of the
Lobbying Disclosure Act, as well as the more colloquial “firms”) by [m] = {1, . . . ,m} and a set
of legislators by [n]. When a bill is reported as lobbied on by client i and sponsored by politician
j, we say that the client and politician coincide on that bill (coinciding pairs will also sometimes
be called “partners”), and each lobbying report client i writes that mentions the bill is a political
incidence. We denote the number of incidences (over some period of time, usually a single ses-
sion of Congress) between the pair i, j by Ai,j , and organize these numbers as the entries of the
incidence matrix A ∈ Rm×n. This matrix may be viewed as the adjacency matrix of a bipartite
7In fact, the OpenSecrets.org database from the Center for Responsive Politics, which is often used in academic re-search, erroneously finds that Intel lobbied on the latter. See http://www.opensecrets.org/lobby/billsum.php?id=hr289-113.
6
graph G with weighted edges, where the politicians and interest groups lie on opposite sides of
the partition. The degree (a term from network theory) of a client or politician is the sum of the
incidences between them and every actor of the other type. Therefore, the degree of a politician
is the total number of times bills they have sponsored have been lobbied on, and the degree of an
interest group is the total number of times they have lobbied on individual bills.
3 Latent Space Modeling of the Lobbying Network
In this section, we develop a Bayesian Latent Space Network Model (LSNM). Following the in-
troduction of this style of model by Hoff, Raftery, and Handcock (2002), there has been a great
proliferation of variants, of which we mention just a few that are especially useful for understanding
our methods. Our model is formally similar to the “Wordfish” model of Slapin and Proksch (2008)
for text analysis, but while we estimate the underlying preference structure of two distinct groups
of interacting political actors, Slapin and Proksch (2008) estimate ideal points of a single group of
political agents based on text data. Our model is also closely related to that of Barbera (2014),
which relates ideal points of politicians and social media users, but instead of modeling whether
a binary political interaction exists (e.g., Twitter follows), we consider how frequently political
contacts occur, our interaction measurements therefore having an associated magnitude.
The Model We model the connection between client i and legislator j as a function of the two
actors’ latent preferred policy positions in a d-dimensional Euclidean space, θi ∈ Rd and ψj ∈ Rd
respectively, and assume that the frequency of their interactions Ai,j has a Poisson distribution,
with its mean parametrized by the Euclidean distance ‖θi −ψj‖.To account for the differences in agents’ baseline propensities to sponsor or lobby (see Fig-
ure A.3), we also include client- and legislator-specific “popularity” terms, αi and βj respectively,
in the mean of the modeled Ai,j .8 We then take the probabilistic model
Ai,j ∼ Poisson(exp(αi + βj − ‖θi −ψj‖)) (1)
for the entries, which we model as independent conditional on the parameters, to obtain the
following joint distribution:
P (A | α,β,θ,ψ) =m∏i=1
n∏j=1
Poisson (Ai,j | exp (αi + βj − ‖θi −ψj‖)) . (2)
We take a further hierarchical extension of this model where the latent space positions of clients
and politicians are distributed with multivariate normal population distributions, N (0, diag(τ ))
8These variables in the literature are variously called “popularity”, “gregariousness”, or “idiosyncratic” factors(we adopt the first term). A similar construction to ours was tested on synthetic datasets by Krivitsky et al. (2009).
7
and N (µ,Σ) respectively, where the distribution of client positions is centered and assumed to have
a diagonal covariance matrix to account for translational and rotational invariances of the latent
space positions. Likewise, popularity factors are assumed to be drawn from normal population
distributions, N (ν, σ2(L)) andN (0, σ2(P )), where the latter is centered to account for the translational
invariance of the popularity factors. The posterior distribution under this model is then:
P (α,β,θ,ψ | A) ∝m∏i=1
n∏j=1
Poisson (Ai,j | exp (αi + βj − ‖θi −ψj‖))×
m∏i=1
N (θi | 0, diag(τ ))×n∏j=1
N (ψj | µ,Σ)×
m∏i=1
N (αi | ν, σ2(L))×n∏j=1
N (βj | 0, σ2(P )). (3)
Identifiability The hierarchical priors we use resolve identifiability issues due to the invariance
of θi and ψj under rotations and translations, and the invariance of α and β under translations.
The remaining identifiability issue is the invariance of θi and ψj under reflections, which makes
the posterior distribution multimodal. This issue becomes more significant as the latent space
dimension increases: in dimension d, one expects the posterior to have 2d modes, one for each
choice of which axes to reflect across. We will only consider d ∈ {1, 2}, and with d = 2 we find that
this issue can still be resolved by the same method as is usual with d = 1 in ideal point estimation,
namely by fixing the position of one agent whose position we believe to be far from the origin of
the latent space. In practice, even fixing the starting position of one important agent to lie in a
specified orthant appears to suffice to ensure that MCMC sampling will only explore one mode of
the posterior. As we will see, the agents on the periphery of the latent space tend to belong to
particular industries. Thus, we choose one significant client from such an industry (we have found
large energy holdings companies, which together form the “core” of the very large energy industry
cluster, to work well for this purpose) and fix its starting position in a particular orthant of Rd.
For a thorough discussion of this problem when d = 1, see Bafumi et al. (2005). For some technical
discussion of an unusual identifiability issue we observed when replacing ‖θi − ψj‖ in our model
with ‖θi −ψj‖2, see Appendix A.3.4.
Computation We perform inference for our model with the Hamiltonian Monte Carlo No-U-
Turn Sampler implementation in the Stan software package (Carpenter et al., 2017). In the results
presented here, we use estimated posterior means of our parameters taken as point estimates, and
perform further analysis of these point estimates so as to focus on politically meaningful findings.
We give further details, a more technical evaluation of our implementation and of concentration of
the posterior distribution, and the Stan code describing our model in Appendix A.3.
8
One-Dimensional LSNM Two-Dimensional LSNM
2 0 2 41D Latent Space Position: Politicians
1.00
0.75
0.50
0.25
0.00
0.25
0.50
0.75
1.00
DW-N
OMIN
ATE
Dim
ensio
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( = 0.13)
4 2 0 2 42D Latent Space Dimension 1: Politicians
( = 0.12)
5.0 2.5 0.0 2.5 5.02D Latent Space Dimension 2: Politicians
( = 0.19)
Figure 2: DW-NOMINATE Ideology vs. LSNM. We illustrate the lack of correlation betweenpoliticians’ latent space positions obtained from one- and two-dimensional LSNM estimates and theDW-NOMINATE ideology dimension. It shows that the ideological structures common in electoralpolitics are not prevalent in the lobbying network. Points are colored on a linear scale from blueto red according to the DW-NOMINATE dimension as well, to introduce a convention we will usethroughout the paper.
Empirical Findings We apply the proposed model to the lobbying network dataset for the 113th
Congress. We first consider a one-dimensional LSNM, the simplest model our framework admits.
To compare with the situation in electoral politics, we examine how estimated latent preferred
policy positions of politicians (ψj ∈ R) correlate with the DW-NOMINATE ideology measures.
As the left panel of Figure 2 shows, we do not find evidence that ideological differences drive
the interaction between interest groups’ lobbying and politicians’ legislative activities (Pearson’s
ρ = 0.13 between our latent dimension and the DW-NOMINATE ideology dimension). This lack
of correlation holds for restrictions to only Democrat and Republican Members of Congress, as
well. Our finding is consistent even when we increase the dimensionality of the latent positions.
Specifically, the right panel shows that each of the estimated latent positions are weakly correlated
with the ideology score.
Increasing the dimensionality of the LSNM, however, facilitates the interpretation of the esti-
mates.9 For the two-dimensional LSNM, Figure 3 presents the posterior means of the estimated
latent spatial positions, θi and ψj , for all clients i and politicians j.10 Each client and each politi-
cian is represented by a circle, the clients’ circles colored black, and the politicians’ circles colored
according to their DW-NOMINATE ideology dimension. The size of each circle is proportional to
the exponential of their popularity factor (αi and βj), so that the mean of the Poisson interaction
between two agents at fixed distance is proportional to the product of their circles’ sizes.
9For more technical analysis of choice of dimensionality for the LSNM based on heuristics from spectral methods,the reader may consult Appendix A.3.1.
10We analyze the posterior distributions further in Appendix A.3, with particular attention to the uncertainties ofthe point estimates given by the posterior means to illustrate the stability of these results.
9
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aT
wo
agen
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outl
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tions
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owth
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Rob
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A)
andW
elchAlly
n,In
c.,
are
om
itte
dfo
rth
esa
ke
of
vis
ual
clari
ty.
10
(H) Fina
ncial
Servi
ces
(S) Ban
king,
Housin
g, & Urba
n Affa
irs
(H) Bud
get
(S) App
ropria
tions
(J) Eco
nomic
(H) Tran
sporta
tion &
Infra
struct
ure
(S) Sp
ecial
Aging
(H) Hom
eland
Secur
ity
(H) Small
Busine
ss
(H) Way
s & Mea
ns0369
1215182124
Num
ber o
f Pol
iticia
ns
Finance & Insurance Clusters
(H) Ene
rgy & Com
merce
(H) Natu
ral Reso
urces
(H) Arm
ed Se
rvices
(S) Agri
cultur
e, Nutr
ition,
& Fores
try
(H) Tran
sporta
tion &
Infra
struct
ure
(H) Ove
rsigh
t & Gov
. Refo
rm
(S) En
viron
ment &
Public
Work
s
(S) En
ergy &
Natural
Resourc
es
(S) Fo
reign
Relatio
ns
(S) Sm
all Busi
ness
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prene
urship
Energy Cluster
(H) Way
s & Mea
ns
(H) App
ropria
tions
(H) Edu
cation
& the W
orkfor
ce
(H) Tran
sporta
tion &
Infra
struct
ure
(H) Ene
rgy & Com
merce
(H) Scie
nce, S
pace,
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ology
(H) Bud
get
(H) Fina
ncial
Servi
ces
(H) Judic
iary
(S) App
ropria
tions
Center Cluster
Figure 4: Committee Membership of Politicians Per Cluster. Histograms of the top 10committee memberships for politicians in the larger clusters highlighted in Figure 3. Bars aredivided by political party (red for Republican and blue for Democrat), and the horizontal bars atthe top show the overall party distribution of each cluster. Senate committee and House committeelabels are prefixed with (S) and (H), respectively.
Figure 3 shows that the key structure emerging from this two-dimensional model is industry-
related, domain-specific interests of political actors. In fact, significant clustering appears in the
LSNM representation as indicated by the outlines and lists of example members in the figure. As
shown by the example group members, the clustering among lobbying clients is strongly industry-
based. Importantly, these clusters emerge even though the clients involved do not lobby on the
same bills (recall that most bills are only lobbied on by a few clients). Instead, related clients are
“pulled together” through their connections to the same politicians, who sponsor separate bills on
which the clients separately lobby. As in the one-dimensional model, there is no clear partisan
divide either across or within industry clusters. Rather, we find in each large cluster of clients a
concentration of politicians from relevant committees involved with the policy issues that directly
affect those clients. For instance, Figure 4 shows that the legislators who belong to the “Finance”
and “Insurance” clusters are likely to serve in the House Financial Services Committee, and
those who belong to the “Energy” cluster are likely to serve in the House Committee on Energy
and Commerce.11
To formally investigate the importance of industry and committee affiliations, we conduct a
series of statistical tests. Specifically, we compare the performance of “Restricted” and “Full”
models whereby the former is nested in the latter. Table 1 presents the F-statistics and p-Values
summarizing the significance of obviating a certain set of observable covariates from the “Full”
model. We find that committee memberships are the only significantly informative covariates of
11We also provide the full lists of cluster memberships in Appendix A.7 for reference.
11
Independent Variables Restricted Full Restricted Full Restricted FullChamber X X X X X XGender X X X X X XLeadership Position X X X X X XParty X X X X X XCommittees X X X X XState X X X X XDW-NOMINATE X X X X XDependent Variable F -Statistic (p-Value) F -Statistic (p-Value) F -Statistic (p-Value)1D LSNM 2.86 (1.55e−8) 1.19 (0.18) 1.38 (0.25)2D LSNM, Dimension 1 2.68 (1.22e−7) 1.76 (1.25e−3) 0.97 (0.38)2D LSNM, Dimension 2 3.10 (8.85e−10) 1.19 (0.18) 1.22 (0.30)
Table 1: Regression Analysis of LSNM Covariates. Statistics of linear models of numerouscovariates against the one- and two-dimensional LSNM latent dimensions for politicians are pre-sented. We compare full linear models with all available covariates included against restricted mod-els, omitting either committee-related covariates, geographic information, or ideology measures. Inthe bottom three rows, for each of the three latent dimensions, we give the F statistics and p-values of the comparison F -test. The test illustrates that the committee membership covariatesare significantly explanatory of the LSNM latent space organization, and, with lesser confidence,geographic covariates are also explanatory of one dimension in the two-dimensional LSNM.
politicians’ positions in the one-dimensional model (see the row labeled as 1D LSNM). In the two-
dimensional model, we find some evidence for the importance of geographic location. However, we
consistently find that including committee memberships significantly improves the statistical fit of
the model, whereas ideology plays insignificant roles.12
Not all regions in our spatial representation, however, are characterized by domain-specific
political interests. We find that a region in the center of the latent space (marked by the dotted
circle in Figure 3) is populated by politicians with committee memberships that are not directly
related to the industries of the relevant clients (which vary widely) but rather concern federal
spending and taxation, most prominently the House Committee on Ways and Means and
House Committee on Appropriations, as shown in Figure 4. Several of the highest-degree
lobbying clients, such as Chamber of Commerce (the single highest-degree lobbying client in the
network), lie in this region. The cluster also includes many of the highest-degree politicians (who
are also often among the most senior politicians), such as Thomas Harkin (D-IA), Patty Murray
(D-WA), Carl Levin (D-MI), Harold Rogers (R-KY), and Chuck Grassley (R-IW). We defer further
interpretation of the characteristics they have in common to Section 4.2, where we will introduce
mixed-membership community models in part to answer this question.
The analysis presented in this section demonstrates the importance of political communities
in the lobbying network, organized according to industry interests and jurisdictional committee
memberships. This is in contrast to the dominance of ideological factors that underlie the networks
12Similarly, we find that industry affiliation, measured by interest group’s NAICS industry code, is the primaryfactor that is significantly related to the estimates.
12
of electoral politics mentioned before. Although the identification of clusters and their visual
demarcations can still be seen as arbitrary, the spatial location of estimated “preferred policy”
exhibit substantively meaningful patterns. In fact, the relative geometry of the clusters is consistent
with common intuitions about relationships among industries: we observe adjacencies between
clusters corresponding to “Finance” and “Insurance”, “Technology” and “Telecom”, “Energy” and
“Environmentalism.” Having bolstered our belief that political communities exist in the lobbying
network, we next seek to study them in a more explicit and statistically principled manner, adapting
our modeling techniques to better suit community structure.
4 Community Modeling of the Lobbying Network
In this section, we introduce new models to clarify the interpretation of the political community
structure that we identified in Section 3. We use variants of the stochastic block model (SBM) to
explicitly model the interaction between a politician and a client as a function of their community
memberships, so that politicians and interest groups with shared group memberships interact more
frequently. Unlike the LSNM, the SBM explicitly assumes a data generating process in which
communities exist. Furthermore, being a probabilistic model, it gives a principled way to estimate
the community parameters, unlike our ex post analysis of the LSNM. The findings from these
models confirm that political communities do exist, and that most of them are organized around
industries and associated committees. They also suggest some revisions of the clustering we found
from the spatial representation of the lobbying network given by the LSNM, capturing several new
clusters that were not previously apparent. Finally, as we will see, these models lead us to discover
novel organization in the lobbying network stemming from mixed interests.
4.1 Motivation: The Bipartite Stochastic Block Model
We base our initial analysis on the Bipartite Stochastic Block Model (biSBM) developed by Lar-
remore, Clauset, and Jacobs (2014). As we will see, though this model confirms the existence of
communities in the lobbying network, it will also raise modeling concerns that will require new
techniques to address. We first review the definition of this model in our particular setting below.
The Model Following the formulation of the LSNM in Section 3, we model Ai,j as having a
Poisson distribution, whose mean now depends exclusively on the cluster memberships of client i
and politician j. To formalize this for our case, we introduce a matrix parameter B ∈ Rk×` with
entries Br,s, where k is the number of client clusters and ` the number of politician clusters, viewed
as hyperparameters.13 To fix these, we draw upon our exploratory analysis with the LSNM, which
suggested roughly how many distinct clusters we expect to exist. Using the analysis of Figure 3,
13The original work of Larremore, Clauset, and Jacobs (2014) treats as multiple edges what we treat as non-negativeinteger edge weights, but formally the models are identical.
13
we choose to use eight politician and lobbying clients throughout our analysis. The membership
parameters are denoted by xi ∈ [k] and yj ∈ [`] for each i ∈ [m] and j ∈ [n]. As before, “popularity
factor” variables αi and βj are introduced per client and politician respectively. The entries Ai,j
are then modeled as
Ai,j ∼ Poisson(αiβjBxi,yj ) (4)
independently, so that the joint probability is:
P(A | B,x,y,α,β) =m∏i=1
n∏j=1
Poisson(Ai,j | αiβjBxi,yj ). (5)
In the block modeling literature the inclusion of the variables αi and βj is known as degree cor-
rection, because these variables adjust the model to account for broad degree distribution in a
network. The model described by equation (4) is therefore named the the Degree-Corrected Bipar-
tite Stochastic Block Model (dc-biSBM), while the model with these variables omitted is simply the
Bipartite Stochastic Block Model (biSBM).14
Computation We perform maximum likelihood parameter estimation for both the biSBM and
the dc-biSBM using the Expectation-Maximization (EM) algorithm implementation provided with
the work of Larremore, Clauset, and Jacobs (2014). Since this algorithm is not guaranteed to
converge to the parameters maximizing the likelihood, we take the parameters obtained from 50
randomly initialized runs and choose those that attain the highest likelihood value.
Empirical Findings Previously, we interpreted the proximity of latent positions obtained by the
LSNM as an indicator of common community membership. To validate this interpretation, we begin
by comparing the LSNM results from Section 3 with the community memberships found by the
dc-biSBM. Figure 5 overlays the clusters identified by dc-biSBM on the geometry obtained from the
LSNM. To facilitate a direct comparison, we show the latent positions of each individual political
actor as well as the grey boundaries previously used to group clients and politicians. We then pair
the dc-biSBM communities of clients and politicians (under maximum likelihood assignment) by
spatial proximity, assign different colors and markers to visually distinguish the eight community
pairs, and label them according to natural interpretations of their members.
The figure shows that the LSNM and dc-biSBM community structures align remarkably well
with each other: the boundaries that we drew indeed correspond to distinct political communities
or to mixtures of at most two or three political communities. Indeed, in one case, for the “Energy”
cluster, the two models find essentially identical clusters: 98% of the lobbying clients and 94% of the
14We will work primarily with the dc-biSBM in the following analysis; further discussion of its advantages over thebiSBM for analyzing the lobbying network is given in Appendix A.4.
14
4 3 2 1 0 1 2 3 4Latent Space Dimension 1
4
3
2
1
0
1
2
3
4
5
Late
nt S
pace
Dim
ensi
on 2
EnergyFinance &InsuranceTechnology &TelecomHealthcareLand Use &Gun RightsCivil SocietyMixed Cluster 1Mixed Cluster 2
Figure 5: Latent Space Position vs. Community Membership. We plot paired communitiesof clients and politicians discovered by the dc-biSBM against latent positions from the LSNM. Mostof these communities align well with the industry-level clusters we manually identified in Figure 3.The light gray box highlights a region where two dc-biSBM clusters intersect in the spatial model,which is plotted in greater detail in Figure 6.
politicians in the LSNM energy cluster also belong to a single dc-biSBM cluster (orange triangles).
The “Finance” and “Insurance” clusters, which the LSNM led us to separate, are instead merged
into a single cluster in the dc-biSBM (green squares), with 98% of lobbying clients and 97% of
politicians from both of these LSNM clusters belonging to a single dc-biSBM cluster. Several other
similar relationships may be observed in the figure; simply put, most of the variation captured by
the LSNM can be captured by discrete community assignments.
However, the LSNM and the dc-biSBM do not always depict the same underlying community
structure. Specifically, we find that an alignment of preferences sometimes cannot be summarized in
a single community membership. This can be seen especially clearly in the highlighted rectangular
region in Figure 5, where we observe that some spatially distant political actors belong to the
same dc-biSBM communities, while some belonging to different dc-biSBM communities are spatially
adjacent. To investigate this, we zoom in on the region and display several example clients from
the two most salient communities in Figure 6. First, we find that most of the agents in the
spatial “Environmentalism” and “Gun Rights” clusters, which lie far away from each other in
15
1.0 0.5 0.0 0.5 1.0 1.5 2.0Latent Space Dimension 1
0
1
2
3
4
Late
nt S
pace
Dim
ensio
n 2
Land Use & Gun RightsCivil SocietyMixed Cluster 1Mixed Cluster 2
Land Use & Gun Rights Clients
Ntl. Rifle AssociationNtl. Shooting Sports FoundationEverytown for Gun SafetyNtl. Fisheries InstituteDefenders of WildlifeFarm Animal Welfare CoalitionCropLife AmericaNtl. Milk Producers FederationGrocery Manufacturers Assn.
Civil Society Clients
Mental Health AmericaSenior Citizens LeagueNtl. Women’s Law CenterNAACPAnti-Defamation LeagueACLUAFL-CIOParalyzed Veterans of AmericaNtl. Education AssociationKent State University
Figure 6: New dc-biSBM Clusters. We plot two “issues” and two “mixed” clusters of clientsand politicians discovered by the dc-biSBM against the latent space positions from the LSNM,along with the clusters based on latent positions that we found manually and showed previouslyin Figure 3. We draw the reader’s attention to the fact that these clusters intersect spatially inthe LSNM, a circumstance whose interpretation is discussed in the main text. Some examples ofclients from each of the dc-biSBM issues clusters are given in the tables on the right.
LSNM, are merged into a single dc-biSBM community (grey circles). Instead of an industry-based
grouping, we interpret these as participants in a broad “Land Use” debate, including gun rights
and hunting organizations, agricultural organizations, and environmental conservation groups (The
connection with gun rights appears to arise due to controversy over hunting rights, evidenced by the
membership of organizations like the National Shooting Sports Foundation in this cluster).
Second, the “Military & Veterans’ Affairs” cluster at the top is expanded to include many other
agents below (brown plus signs), who we interpret as participants in a “Civil Society” debate.
Indeed, this cluster includes foundations for the rights of the disabled, senior citizens, women, and
ethnic and racial minorities, as well as veterans’ associations, labor unions, education groups, and
general civil rights advocacy organizations.
This classification notwithstanding, an issue like gun rights touches upon both questions of civil
rights and land use. Thus, an interest group concerned with gun rights might lobby similarly to
16
some interest groups that clearly belong to the “Civil Society” cluster, and similarly to others that
clearly belong to the “Land Use” cluster. Notice that the grey circles and brown plus signs intersect
one another in the middle of Figure 6. We argue that this is an instance of precisely such mixed
interests in the lobbying network: strict classification for some organizations is complicated by the
interplay of interests in multiple arenas of debate. For instance, the National Shooting Sports
Foundation previously mentioned lies near this region.15 Moreover, the two clusters we denoted
“Mixed Cluster 1” and “Mixed Cluster 2” in Figures 5 and 6, so named because there appears to
be no unifying topical description of their members, lie mostly in the central cluster of the LSNM,
the region marked by the dotted circle in Figure 3 which we previously found to contain large
lobbying clients with diverse interests and senior politicians with broad authority. It therefore
appears entirely reasonable that mixed interests are in fact the phenomenon characterizing this
region, an intuition that we will make precise in the following section.
To summarize, with the dc-biSBM we obtain an explicit analysis confirming that there in fact
exist communities in the lobbying network, and that these communities are again not aligned
with one-dimensional ideological polarization, but rather with a rich collection of industry- and
committee-level distinctions. We also find new community structure corresponding not to industries
but to cultural or political debates, and find that for these cases, the non-geometric approach
of community modeling uncovers interesting group memberships that were not clear from the
ideal point models previously used in much of the relevant literature. Finally, examining the
characteristic interests of some of the lobbying clients placed in these clusters, we find that it is
perhaps inappropriate to impose them a “hard” classification into a single community, because
such clients may be best described by a mixture of political interests. In the following section,
therefore, we propose a new community model that represents mixed interests of political actors
in an explicit probabilistic framework.
4.2 The Proposed Bipartite Link Community Model
We propose the Bipartite Link Community Model (biLCM), whose goal is to capture actors’ si-
multaneous membership in several political communities. In the proposed model, political “links”
(edges) between a politician and lobbying client can be manifested by heterogeneous interests. This
methodology allows us to capture the important possibility of politicians’ having multidimensional
preferences involving several issues (Lauderdale and Clark, 2014), as well as interest groups pur-
suing several simultaneous objectives and aggregate interest groups such as industry organizations
accomodating the varied interests of their members.
It is particularly apparent that this is sensible for politicians, who almost always sit on several
15The mean coordinates of this lobbying client’s latent position are (0.33, 2.42).
17
committees having distinct legislative jurisdictions. For lobbying clients, mixed interests of course
still arise, but in several varieties. We have already suggested one example in the previous section,
where a lobbying client like the Sierra Club may maintain interests in, say, an environmental
issue (e.g. lobbying on 113th H.R. 2424, Community Parks Revitalization Act together with the
Trust for Public Land), but may for various reasons also sometimes lobby on broader civil
rights legislation (e.g. lobbying on 113th H.R. 3206, Global Sexual and Reproductive Health Act of
2013 together with Planned Parenthood). Perhaps a simpler instance of the same phenomenon
occurs with larger holding companies, which lobby on behalf of their diverse subsidiaries in multiple
arenas. For instance, the lobbying client Philips Holding USA, Inc., which specializes in medical
equipment, home appliances, and lighting solutions, will split its membership between one link
community focused on healthcare, and another focused on retail and shipping. In fact, Philips
Holding USA, Inc. lies near the center of the LSNM latent space in Figure 3, the region whose
proper interpretation we have suggested in the previous section is linked to mixed interests, and
the Sierra Club lies in the nearby and likewise ambiguous region near the intersection of the two
clusters highlighted in Figure 6.16
Modeling Choices for Mixed Membership In the lobbying network, an even stronger version
of mixed interests arises than the one we have emphasized above: not only do political actors often
have several simultaneous interests, but even one politician and one lobbying client often coincide
on bills that should not be grouped together. This is especially apparent between agents with broad
ranges of interests, like large lobbying clients and senior politicians. For example, the Chamber of
Commerce and Senator Barbara Boxer (D-CA) coincide on both 113th S. 601 “Water Resources
Development Act of 2013” and 113th S. 462 “United States-Israel Strategic Partnership Act of
2013”. Likewise, the Specialty Equipment Market Association (SEMA) and Senator John
Cornyn (R-TX) coincide on both 113th S. 983 “Keep the IRS Off Your Health Care Act of 2013”
and 113th S. 2635 “21st Century Endangered Species Transparency Act”.
Therefore, a natural modeling assumption is that political actors have simultaneous member-
ship in several legislative communities, and they “play the role” of one of those memberships in
each individual interaction that they partake in. The mixed-membership stochastic block model
(mmSBM), introduced by Airoldi et al. (2008), is perhaps the most common alternative to the
type of model we will propose, and involves each actor having an associated probability distribu-
tion over communities, the interaction between a pair of actors depending on a pair of community
assignments that the actors choose for that interaction partner from their respective distributions.
For example, the authors apply the mmSBM to the Sampson monastery dataset (Sampson, 1969),
16The mean coordinates of these lobbying clients’ latent positions are (−0.25, 0.51) and (−0.68, 1.32), respectively.
18
(a) dc-biSBM (b) mmSBM (c) biLCM
Figure 7: Schematic Comparison of Community Models. We illustrate three generativenetwork models, the single-membership dc-biSBM model and the mixed-membership mmSBM andbiLCM models. For the sake of simplicity, the former two models are illustrated in the assortativecase, where each politician community interacts strongly with just one lobbying client communityand vice-versa. (a) In the single-membership model, political actors with a shared communitygroup membership tend to interact more frequently. (b) mmSBM allows for mixed membershipdistributions while all interactions involving a given partner are constrained to be a single type. (c)The proposed biLCM allows for mixed membership as well as mixed interaction (link) types. Thiscaptures the common lobbying interactions wherein interest groups and politicians with diversecommunity memberships interact for different political reasons.
a popular example in the social networks literature which summarizes survey data on social rela-
tionships among 18 novice monks joining a monastery, and identify the group of “waverer” monks,
who do not commit to one of the primary social factions in the monastery, but rather maintain
friendships with members of several factions.
Although this model has some desirable features that allow multiple memberships for political
actors, the mmSBM always retains the property that the mixed membership it models consists
of an agent varying their community membership across their interactions with different agents.
But, as we described before, the mixed membership we are interested in modeling is different—we
are concerned with political actors playing different roles even in their interactions with a single
partner over the course of a session of Congress. In fact, such variation in connections is ubiquitous
in political networks, as the motivating examples above demonstrate. We illustrate this important
conceptual difference graphically with Panels (b) and (c) of Figure 7.17 These show that a model
in the mmSBM family would bizarrely insist that a single common community membership must
account for each pair of incidences involving a given politician and interest group, while in reality,
and as modeled by the biLCM to be introduced, the bills that link a given politician and interest
17We consider an extended version of the original mmSBM to assortative and weighted relationships that are closerto the data we observe in the lobbying network. Mathematically, the difference we are emphasizing is that an mmSBMadjusted to have Poisson-distributed weights on interactions would model each edge weight as a mixture of Poissondistributions, while the link community model we propose will model each edge weight as a sum of independentPoisson distributions, one for each link community in which the agents interact.
19
group often have different underlying motivations and subject matter, and thus should be modeled
as such. (Of course, as Panel (a) of Figure 7 shows, both models are far more flexible than the
dc-biSBM, which makes the assumption that only a single fixed community membership is allowed
for each actor.)
The Model Our model combines three features which, to the best of our knowledge, have not
been previously combined in the mixed membership community modeling literature. First, it ex-
plicitly models link communities; second, it does so in the bipartite setting; and finally, it uses
a statistically principled generative model. Our model structure is most similar to that of Ball,
Karrer, and Newman (2011), but borrows the ideas for specialization to bipartite graphs from Lar-
remore, Clauset, and Jacobs (2014). The most similar work we are aware of is the link community
detection task in bipartite graphs treated as an optimization problem on a graph and solved by an
ad hoc genetic algorithm by Li, Zhang, and Zhang (2015); unlike that work, our model provides
an underlying probabilistic model and therefore a natural statistical interpretation.
We suppose that there are k link communities, which we will always take as indexed by a
variable z ∈ [k]. To emphasize the political interpretation, we will subsequently refer to these as
legislation communities as well. Each client and politician has a vector of parameters αi,z and
βj,z, respectively, which represent their involvement in legislation belonging to community z. The
number of bills lobbied by client i, sponsored by politician j, and belonging to legislation community
z is modeled as Poisson with a mean proportional to αi,zβj,z. To resolve the identification issue,
we assume that for each fixed z,∑m
i=1 αi,z =∑n
j=1 βj,z = 1, and introduce another parameter κz
to capture the overall weight of group z, so that the number of bills between client i and politician
j in legislation community z has mean κzαi,zβj,z. We assume that these Poisson variables are
independent, so that the model for the incidence matrix entries is
Ai,j ∼ Poisson
∑z∈[k]
κzαi,zβj,z
, (6)
and the joint distribution is
P(A | α,β,κ) =
m∏i=1
n∏j=1
Poisson
(Ai,j
∣∣∣∣ k∑z=1
κzαi,zβj,z
). (7)
Computation We derive an Expectation-Maximization (EM) algorithm for this model in Ap-
pendix A.5, which involes alternating expectation and maximization update steps until the log-
likelihood of the model converges. The update equations produced by our derivation are given
below, including ancillary optimization parameters qi,j(1), . . . , qi,j(k) (the first equation is the ex-
20
pectation step for the ancillary parameters, and the last three equations are maximization steps
for the model parameters).
qi,j(z) =κzαi,zβj,z∑kz=1 κzαi,zβj,z
, (8)
κz =
∑mi=1
∑nj=1Ai,jqi,j(z)∑m
i=1
∑nj=1 αi,zβj,z
, (9)
αi,z =
∑nj=1Ai,jqi,j(z)∑m
i=1
∑nj=1Ai,jqi,j(z)
, (10)
βj,z =
∑mi=1Ai,jqi,j(z)∑m
i=1
∑nj=1Ai,jqi,j(z)
. (11)
As for the dc-biSBM, this procedure is not guaranteed to converge to the parameters maximizing
the likelihood, so we take the parameters obtained from 50 randomly initialized runs and choose
those that attain the highest likelihood value.
Empirical Findings The majority of our analysis of the biLCM will focus on how “spread out”
agents’ distributions over (link) communities are, or how “mixed” the legislative activity of an
agent is between different legislation communities. We first describe a simple way to quantify this
notion. From the model definition, the mean total number of times client i lobbies in community
z equals κzαi,z, and likewise the mean total number of times politician j sponsors in community
z equals κzβj,z. A natural choice of measurement is then to normalize these quantities to form
probability distributions pi,z =κzαi,z∑k
z=1 κzαi,zand qi,z =
κzβj,z∑kz=1 κzβj,z
, and then consider the (Shannon)
entropies Hi = H(pi,1, . . . , pi,k) and Hj = H(qj,1, . . . , qj,k), where H(c1, . . . , ck) = −∑ cz log2 cz.18
In computing entropies, we take logarithms base 2 for the sake of interpretability: an entropy of
H may then be interpreted as, roughly speaking, an agent typically participating in 2H link com-
munities. Simply put, the higher an agent’s entropy, the more “spread out” their link community
distribution is.
With these tools, the first phenomenon the biLCM model can shed light on is the properties
of the region near the center of the LSNM latent space, where we did not find legible community
structure previously. The left panel of Figure 8 repeats the latent space plot from Figure 3, but
now dividing the latent space into small hexagonal parcels and coloring these by the mean link
community membership entropy of agents lying within them. We clearly observe actors of more
mixed membership (darker hexagons) clustered near the center of the latent space. As a more
intuitive visualization, we provide a schematic visual representation of the same phenomenon in
18We reuse the letter H for these quantities since it will always be clear from context whether we are discussing theentropy of specifically clients or specifically politicians. The astute reader may also object at this point that in facta more principled quantity to consider would be the mean of the entropy of empirical link distributions drawn fromour model with a given set of parameters. We concur, but find in practice that this mean agrees almost perfectly(Pearson’s ρ = 0.99) with the quantity we compute.
21
4 2 0 2 4Latent Space Dimension 1
4
3
2
1
0
1
2
3
4
5
Late
nt S
pace
Dim
ensio
n 2
Link Community Distribution Zonal Entropies
4 2 0 2 4Latent Space Dimension 1
Link Community Distribution Examples
Figure 8: Latent Space Position vs. Link Community Distribution. The left panel dividesthe latent space inferred for the full 113th Congress dataset into hexagonal regions (keeping onlythose containing some clients or politicians). We color each region by the average entropy oflink community memberships of the agents in that region, such that a darker color implies agents’participation in multiple communities. The right panel plots example link community distributionsat their corresponding latent positions. We observe that the distributions become more skewedtowards single communities as we move to the edges of the latent space, whereas actors with themost “spread out” link community distribution tend to lie in the center.
the right panel, where we choose just a few example lobbying clients and represent each with a
pie chart showing their estimated link community membership distribution. We consistently find
many of the same industry-specific communities at the edges of the latent space as we have seen
before, such as the energy, technology, and finance industries, whose members typically participate
in only one or two link communities (marked by pie charts with only one or two dominant colors).
And, we again find that political actors in the center region tend to have mixed membership among
many different political communities (marked by pie charts split evenly among many colors).
We have already seen in Figure 4 that the politicians in the central LSNM cluster tend to belong
to “procedural” committees, having broad and less domain-specific responsibilities and involving
general categories of government oversight or budgetary allocation. The lobbying clients in this
group also admit a simple and politically meaningful description: many of them are large associ-
ations collectively representing many small businesses, employee groups, or broad causes, such as
the Chamber of Commerce, the Specialty Equipment Market Association, Heritage
Action for America, the National Treasury Employees Union, and numerous others.
Based on these characterizations and the observation that such political actors also exhibit very
mixed community membership, we propose that these actors participate in lobbying through ag-
gregate action in the lobbying marketplace, either in the form of direct lobbying of politicians with
22
broad responsibilities (as indicated by their membership in procedural committees), or in the form
of lobbying through large associations by many individuals or smaller firms as often observed in
trade politics. We believe that these findings indicate a unique aggregate mode of lobbying, wherein
politicians with control over broad ranges of policy are lobbied by many otherwise unrelated inter-
est groups, and conversely lobbying clients representing broad ranges of smaller constituents lobby
many otherwise unrelated politicians. We compare some examples of the lobbying clients operating
in this manner to others whose lobbying is more focused in Table 2.
How do we know whether a given political actor’s link community distribution is unusually
concentrated or mixed? To provide an answer, we perform a permutation test on the lobbying
network to obtain a null distribution of “mixedness” (measured by entropy) of the biLCM results
for networks with similar connectivity to the lobbying network (see Appendix A.6 for technical
details). Since the biLCM can capture both concentrated and mixed community membership, this
is also a useful test of the statistical significance of the communities we have been discussing: if the
lobbying network has strong community structure compared to a “generic” similar network, then
the biLCM should find much more concentrated community memberships in the lobbying network
than in such a generic network. Indeed, we find that this is the case, observing that the link
community distributions in the null model are typically much more mixed than those in the model
of the lobbying network.
We also observe that, in addition to elucidating aggregate lobbying, mixed membership gives
a clean way to explain many of the questions raised by our single membership clusterings previ-
ously. A simple example is Philips Holding USA, Inc., which as we noted before manufactures
both healthcare products and electronics. Accordingly, it has a probability of participation in the
“Healthcare” cluster of 0.31, and in the “Retail / Shipping” cluster of 0.26 (and these are the
two clusters it is most likely to participate in). Similarly, CropLife America has a probability
of participation in the “Retail / Shipping” cluster of 0.74, and in the “Energy” cluster of 0.23.
The biLCM also finds a very cleanly delineated “Civil Society” cluster, which clarifies some of the
unusual findings around the corresponding cluster of the dc-biSBM. For instance, as we inferred
previously, several gun rights groups (the National Rifle Association, the National As-
sociation for Gun Rights, and Gun Owners of America, Inc.) indeed have the highest
probability of participating in the “Land Use” cluster, but also a small additional probability of
participating in the “Civil Society” cluster, confirming our interpretation that the motivation of
these groups to lobby is divided between an issue-driven interest in gun rights and an ideological
commitment to general conservatism on cultural questions. The same occurs for the Sierra Club,
which typically takes the opposite side of the gun rights debate from these organizations.
23
Lobbying Client Distribution Example Bills
Berkshire Hathaway EnergyEnergy Freedom and Economic Prosperity ActGeothermal Production Expansion ActHydropower Regulatory Efficiency Act
Microsoft CorporationStartup Act 3.0Cybersecurity Enhancement ActSurveillance Transparency Act
Sierra ClubTennessee Wilderness ActKeystone XL Pipeline Approval ActGlobal Sexual and Reproductive Health Act
Philips Holding USA
Integrated Electronic Health Records forMilitary and Veterans Act
Energy Savings and IndustrialCompetitiveness Act
Domestic Prosperity and Global Freedom Act
Chamber of CommerceSTEM Visa ActUnited States-Israel Strategic Partnership ActSensible Accounting to Value Energy Act
Heritage Action for AmericaMarriage and Religious Freedom ActSave American Workers ActFree Flow of Information Act
Table 2: Link Community Distribution Examples. We give examples of lobbying clients withfocused, intermediate, and very spread out link community distributions, along with examples ofbills illustrating their interests. We observe some consistencies among the communities emphasizedfor different clients; for instance, Berkshire Hathaway Energy and the Sierra Club share aninvolvement in an energy-focused community, while Philips Holding USA and the Chamberof Commerce share an involvement in a retail- and shipping-focused community.
On the whole, we find that the biLCM successfully recovers a readily interpretable mixed mem-
bership profile, where a lobbying client and a politician must balance the pursuit of several distinct
political commitments. That is, the proposed methods not only capture the community structure
that we often do not observe, but also provide a useful guidance for gauging the mixed-interests
and complex preference aggregation that drive lobbying in legislative politics.
5 Concluding Remarks
Lobbying is known to be an important channel through which special interest groups affect the
U.S. legislative process. Even so, explicit observable connections between interest groups and
the politicians who represent those interests have proved elusive, since the individual politicians
contacted in the course of lobbying need not be disclosed. We assemble a new lobbying network
dataset by measuring legislative interactions between interest groups and politicians across all bills
introduced since the 106th Congress, after carefully identifying the lobbying instances associated
with each bill.
24
We show that this network can be usefully modeled by both latent space models and community
models. Unlike previous applications of latent space models in political science under the rubric
of ideal point estimation, our models suggest that lobbying interactions mostly depend not on
an underlying ideological spectrum, but rather on an underlying grouping into domain-specific
communities. Stochastic block models confirm and clarify this finding by explicitly modeling the
community structure we empirically observe. Furthermore, our bipartite link community model
captures a richer network structure in which some politicians have a tight connection with a single
industry specifically related to their committee membership, while others who serve in “procedural”
committees with broader subject matter are linked to multiple political communities and aggregate
interest groups that represent highly heterogeneous political interests. The consistency between
latent space models and non-geometric community models in describing these patterns provides
strong evidence for political divisions that are not aligned with ideological polarization as the
dominant structural feature of the lobbying network in U.S. legislative politics.
Our findings open up new possibilities for studying the mechanics of lobbying, showing that
it is helpful to view lobbying activity as occurring in a network of political actors, and therefore
suggesting that other network analysis techniques may reveal further meaningful patterns underly-
ing lobbying. Of course, the lobbying network also contains much more information than we have
analyzed here, including bills and their contents, lobbyists and their lobbying firm affiliations, dis-
closures of money spent on lobbying, and the timing of lobbying and legislative events. Extending
the network models in this work to describe new types of agents and interactions should further
enhance the picture of lobbying and its political effects that we have obtained here.
25
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A Supplementary Appendix
A.1 Identifying Bills and Missing Congress Numbers
Identifying congressional bills in lobbying reports is difficult because bill numbers are repeated
across Congresses, and often do not appear directly annotated with Congress numbers in lobbing
reports. Using the report filing year to guess the Congress often leads to erroneous matches,
because reports filed at the beginning of a new Congress tend to include disclosures of lobbying
activities from the previous year (and therefore, if a new Congress has begun recently, from the
previous Congress as well). For example, consider the following lobbying report filed by Google,
Inc. in 2013. It reads:
Monitor legislation regarding online privacy including Safe Data Act (H.R. 2577, S. 1207) and Do not trackproposals (H.R. 654). Monitor any Congressional or Administration efforts to impose privacy laws on searchengines. Monitor Spectrum acts (S. 911, H.R. 2482).
Figure A.1: First Quarter Report by Google, Inc. in 2013
A naive guess would be that the House bill H.R.2577 refers to a bill from the 113th Congress,
because the report was filed in 2013. However, it is clear from the report that this is a 112th
Congress bill, the “SAFE Data Act”. We use the following strategies to mitigate this problem and
correctly identify Congress session numbers under various circumstances.
1. Bill Number Search: We first identify bill numbers (e.g., H.R.2577 above) using regular
expression search in the report text. In the above example in Figure A.1, our algorithm
would identify bill numbers H.R. 2577, S.1207, H.R.654, S.911, and H.R.2482. Note
that all of these bills are from the 112th Congress rather than the 113th.
2. Congress Identification: Given a bill number found in a specific issue text (a section of
the lobbying report), we attempt to identify the most likely Congress to which that bill would
belong using other text around the bill number. Starting from the Congress containing the
year of the lobbying report, we consider a range of candidate Congresses extending backwards
from that of the lobbying report (by default, we consider three Congresses back; therefore
in the above example, we would consider the 113th, 112th, and 111th Congresses). We then
obtain the bills of the same number from each of these Congresses (omitting the Congresses
that do not have a bill of the given number), and compute a bag-of-words representation
(after a tokenization and stopword filtering pipeline) of each of those bills, giving some vectors
v1, . . . ,vn, and the same representation of the text around the mention of the bill number,
w. The Congress that we choose then corresponds to the maximizer of the cosine similarity,
29
its index given by
i∗ = argmax1≤i≤n
v>i w
‖vi‖‖w‖. (12)
If no bill having the same number exists in the entire range of Congresses we consider, we
simply guess that the bill comes from the Congress of the year the lobbying report was filed.
3. Congress Propagation: If we successfully find a match for a Congress, it may be propa-
gated to the other bills mentioned in the lobbying report, since, being scheduled on a quarterly
basis, lobbying report will effectively always only pertain to legislation in a single Congress.
If different bills in a lobbying report disagree on the best-match Congress, a majority vote
may be taken, but this rarely occurs in practice.
4. Bill Title Search: Bills are sometimes only referred to by titles or alternate names. To
account for this, we clean and tokenize the specific issue sections of the lobbying report, and
perform a text matching operation against a table of bill titles. For instance, this operation
would identify “Safe Data Act” in our previous example, even if the bill number H.R.2577
were not mentioned.
5. Bill Range Expansion: It is also common for bills with nearby numbers to be related, and
for lobbying reports to refer to ranges of bills when they are all being lobbied at once.
“H.R.3009, Trade Act of 2002. Certain miscellaneous tariff bills to suspend the rates ofduty on certain toy-related articles (H.R.4182-4186; S.2099-2103). WTO market accessnegotiations for non-agricultural products Port and border security measures”
Figure A.2: Midyear Report by Mattel, Inc. in 2002
Therefore, if we find two bill numbers that are close (by default if they share the same prefix
and their numbers differ by at most 10), then we consider all other bills with numbers in be-
tween as also being lobbied. For instance, the pattern H.R.4182-4186 in the excerpt shown
in Figure A.2 would be expanded into bills H.R.4182, H.R.4183, H.R.4184, H.R.4185,
and H.R.4186 all being considered lobbied.
A.2 Assembling Datasets
A.2.1 Filtering
Besides reducing the size of our data, the key task in filtering is to increase the minimum degree
of the agents we include. This is especially important for latent space models, which will be the
class of models we consider in the greatest detail; it is intuitive that there is no principled way to
position an actor in the lobbying marketplace if the actor does not lobby or sponsor very much,
30
or at all. Thus, while it may appear appealing at first to filter the dataset independently on the
client and politician side by separate summary statistics like total numbers of bills sponsored or
lobbying reports submitted during a Congress, such filtering is not necessarily aligned with the
goal of finding a large submatrix of A (or induced subgraph of G) with only agents of sufficiently
high degree. In particular, we want to avoid sponsor filtering causing some clients who survived
client filtering to have their degree reduced again, or vice-versa.
We find that the simplest way to avoid such issues is to build the full matrix A without filtering
first, and then filter based only on incidences in a way that explicitly ensures that only high-degree
agents remain. To that end, we use a filtering procedure defined by two thresholds, denoted TL and
TP throughout, which alternates removing all clients with degree lower than TL and removing all
politicians degree lower than TP , until no more clients or politicians are removed in a full iteration.
This is a simple greedy algorithm for finding an induced subgraph of G with only high-degree
nodes, which we find to suffice for our purposes; almost always just a few iterations are required
for the algorithm to terminate, but rarely just one iteration.
A.2.2 Specifications
Each of our datasets pertains to a single Congress, and will be identified by the number of the
Congress, along with one of the following specifications.
• Unthresholded: All politicians in both chambers of Congress any of whose sponsored bills
have been lobbied on are included, and all clients who have lobbied on any bills are included.
• Full: The unthresholded dataset, filtered with the algorithm from XXX with thresholds
TL = 100 and TP = 10. (The name reflects that this dataset contains both chambers of
Congress unlike the per-chamber datasets below; the unthresholded dataset will only be
considered briefly at the beginning of our analysis and should not be confused with this
dataset, which will play a more important role.)
• Senate or House: The full dataset, restricted to include only politicians in the Senate or
House of Representatives, respectively, and then filtered again with TL = 20 and TP = 10
(this last filtering only has a very small effect on the dataset size, and serves only to exclude
a few very low-weight rows and columns from the restricted incidence matrix).
• Top Term: The same construction as the full dataset, but including only those bills whose
top term is the specified term (thereby restricting bills to a certain issue area). We tune the
thresholds used for these datasets to give dataset sizes roughly similar to those of the other
datasets. For the “Energy” top term, we take TL = 5, TP = 5.
31
Congress Specification Clients Politicians Sparsity
113 Unthresholded 6,747 542 98.28%Full 707 525 91.99%Senate 701 111 95.15%House 707 414 93.79%Top Term “Energy” 518 101 95.03%
111 Senate 945 118 86.27%House 949 410 94.26%
Table A.1: Datasets. The specification, shape, and sparsity (fraction of zero entries) of all of thedatasets used in our analysis.
0 50 100 150 200+Bills Lobbied
0.00
0.02
0.04
0.06
0.08
0.10
0.12
Prop
ortio
n of
Lob
byin
g Cl
ient
s
Bills Lobbied per Client
0 20 40 60 80 100Bills Sponsored
Prop
ortio
n of
Pol
iticia
ns
Bill Sponsored per Politician
Lobbying Client Bills LobbiedIraq/Afghanistan Veterans of America 725Chamber of Commerce 684National Cable and Telecom Assn. 312Berkshire Hathaway Energy 307Xcel Energy 292SEMA 290American Civil Liberties Union 286Duke Energy 283Integrys Energy Group 273Edison Electric Institute 253
Politician Bills SponsoredRobert Menendez 107Alan Grayson 96Mark Begich 92David Vitter 85Harry Reid 80Amy Klobuchar 78Sherrod Brown 69Bernard Sanders 69Dianne Feinstein 69Charles Schumer 67
Figure A.3: Distribution of Political Actions: We present the skewed distributions of politiciansponsorship and lobbying counts for the 113th Congress dataset. The activity counts and namesof the most active politicians and clients are presented in the tables.
Lastly, we refer in shorthand to the full 113th Congress dataset as the “main dataset”, since for
models other than latent space models we will present findings only for this dataset, our interest
in those models being primarily methodological comparison. More generally, most of our analyses
will focus on the 113th Congress, with the 111th Congress referenced for comparison. Table A.1
shows basic statistics for all datasets we will use.
32
A.3 Latent Space Model Details
A.3.1 Dimensionality Selection
We present some heuristics on choosing a dimensionality for the LSNM based on a simpler modeling
technique, the spectral biclustering of Kluger et al. (2003). In this method, one computes the
singular value decomposition (SVD) of the bipartite adjacency matrix A, and searches for piecewise
constant structure among the leading singular vectors. The idea we will borrow from this method
is simply that the leading singular vectors contain most of the relevant information for the analysis
of A, proportionally to the magnitude of their singular values, essentially the same concept as in
PCA analyses. We then make a plot of the singular values, analogous to a scree plot, and search
for an “elbow” indicating the suitable number of singular vectors to use. That number gives an
estimate of the dimensionality of the low-rank structure in A, and thus also a heuristic estimate
of the dimensionality to take in the LSNM. In A.4, we show that a dimensionality of two is a
reasonable choice if this heuristic is admitted.
0 100 200 300 400 500Index
0
1
2
3
4
5
6
Squa
red
Sing
ular
Val
ue
1e5
Figure A.4: Bipartite Adjacency Matrix Singular Value Decay. We plot the singular valuesof the matrix A for the 113th Congress dataset, highlighting that the singular values decay rapidlyand the first few appear to capture much of the total weight of this matrix. The first two singularvalues are highlighted, corresponding to our analysis primarily of a two-dimensional latent spacemodel in the main text.
A.3.2 Computation
The Stan code in Listing A.1 was used to fit our latent space models, run through the PyStan
interface. In practice, by far the most important optimization for this model is the vectorization
of the declaration of the Poisson distribution for edge weights; factorizing the latent space posi-
33
tion prior covariance matrix into Cholesky factors and using them to transform standard normal
variables is a common optimization for multivariate normal priors, but our covariance matrix is so
small that we find this not to affect (or even to degrade, in some cases) sampling performance.
For each latent space model we run four MCMC chains, each drawing 10,000 samples, the first
4,000 of which are discarded as warmup (“burn-in”) samples, leaving us with 6,000 usable samples
per chain, for a total of 24,000 samples, which are used to compute estimates of posterior means
of all of our models parameters as used in all visualizations in the main text.
A.3.3 MCMC Diagnostics
Position Variances We employ a useful visualization that captures the variance structure of
all of our latent space position estimates at once. For any agent, if we take N samples of its
d-dimensional position, we may view these samples as the columns of a matrix X ∈ Rd×N . We
let µ ∈ Rd be the vector of means of the rows of X, and let X0 be the centered matrix obtained
by subtracting µi from each entry of the ith row of X. Then, W = 1N−1X0X
>0 ∈ Rd×d is the
empirical covariance matrix of the position samples, which we diagonalize to obtain orthonormal
eigenvectors v1, . . . ,vd and associated non-negative eigenvalues λ1, . . . , λd. This is essentially a
principal component analysis (PCA), except unlike the usual high-dimensional setting for PCA,
we actually have many points in a low-dimensional space, N � d. Nonetheless, the computed
quantities have the same interpretations: λi is the amount of variance of the dataset of sampled
positions in the direction of principal component vi, and the shape of the set of sampled positions
is approximated by the ellipsoid with axes in the directions of the vi and having lengths√λi (one
“directional standard deviation” in each principal direction).
For small d, in particular for d = 2 as we use for most of our models, these ellipses can be drawn
for all embedded points at once, and their scale is easy to understand visually relative to the scale
of the entire latent space. Small ellipses correspond to concentrated posterior distributions for
position parameters, and concentrated posteriors justify the use of the mean position point in our
other visualizations (also, though we did not use such algorithms, concentrated posteriors would
justify safe use of inferential approximations such as EM algorithms and variational methods).
This visualization is given for the model inferred from the main dataset in Figure A.6.
34
64
20
24
Late
nt S
pace
Dim
ensi
on 1
1050510
Latent Space Dimension 2
Polit
icia
ns (D
emoc
rat)
Polit
icia
ns (R
epub
lican
)Lo
bbyi
ng C
lient
s
Cona
gra
Food
sNt
l. Ch
icke
n Co
unci
lNt
l. Tu
rkey
Fed
erat
ion
Amer
ican
Mea
t Ins
titut
eUt
d. F
resh
Pro
duce
Ass
n.Cr
acke
r Bar
rel O
ldCo
untr
y St
ore
Intl.
Dai
ry F
oods
Ass
n.
Stev
e W
omac
k (R
-AR)
Roge
r Wic
ker (
R-M
S)F.
Sen
senb
renn
er
(R-W
I)Bo
b Go
odla
tte
(R-V
A)
Food
Ford
Mot
or C
ompa
nyAl
lianc
e of
Aut
omob
ileM
anuf
actu
rers
Hyun
dai /
Kia
N/A
Auto
mot
ive
Ntl.
Corn
Gro
wer
s As
sn.
Amer
ican
Coa
litio
n fo
rEt
hano
lPo
et L
LCPa
trio
t Ren
ewab
le F
uel
Ntl.
Biod
iese
l Boa
rdAd
vanc
ed B
iofu
els
Assn
.
Dav
id L
oebs
ack
(D-I
A)
Rene
wab
le F
uels
Shel
l Oil
Com
pany
BP A
mer
ica
Exxo
n M
obil
Corp
.Ch
evro
n US
AM
arat
hon
Petr
oleu
m
J. Br
idge
nstin
e (R
-OK)
Don
You
ng (R
-AK)
Oil
Ucor
e Ra
re M
etal
sEl
ectr
on E
nerg
yGr
eat W
este
rn T
ech.
Thom
as a
nd S
kinn
erLy
nas
Corp
.
N/A
Rar
e Ea
rth
Met
als
Duk
e En
ergy
NV E
nerg
ySo
uthe
rn C
ompa
nyEd
ison
Ele
ctric
Inst
itute
Natio
nal G
ridPu
blic
Ser
vice
Ent
erpr
ise
Grou
p (P
SEG)
Bill
Shus
ter (
R-PA
)M
ike
Mul
vane
y (R
-SC)
Ben
Luja
n (D
-NM
)Ba
rbar
a Bo
xer (
D-C
A)Se
an M
alon
ey (D
-NY)
Uti
litie
s &
Hol
ding
s
Allia
nce
Pipe
line
Amer
ican
Nat
ural
Gas
Allia
nce
Spec
tra
Ener
gyUn
ited
Phos
phor
usCe
nter
Poin
t Ene
rgy
Will
iam
s Co
mpa
nies
Hear
th, P
atio
, and
Barb
ecue
Ass
n.
Mik
e Po
mpe
o (R
-KS)
Tom
Mar
ino
(R-P
A)Fr
ed U
pton
(R-M
I)Jo
hn W
alsh
(D-M
T)
Nat
ural
Gas
Amer
ican
Riv
ers
Cova
nta
Ener
gyNo
rthw
est R
iver
Par
tner
sNa
tiona
l Wat
er R
esou
rces
Assn
.Nt
l. Hy
drop
ower
Ass
n.
John
Bar
rass
o (R
-WY)
Scot
t Tip
ton
(R-C
O)
Orr
in H
atch
(R-U
T)Jo
n Te
ster
(D-M
T)Jo
hn L
arso
n (D
-CT)
Hyd
ropo
wer
Arch
Coa
lPe
abod
y En
ergy
Unite
d M
ine
Wor
kers
of
Amer
ica
New
mon
t Min
ing
Co.
Bitu
min
ous
Coal
Ope
rato
rs A
ssn.
J. Ro
ckef
elle
r (D
-WV)
Heid
i Hei
tkam
p (D
-ND
)Ni
ck R
ahal
l (D
-WV)
Coa
l & M
inin
gUS
Gre
en B
uild
ing
Coun
cil
Hone
ywel
l Int
l.Nt
l. As
sn. o
f Hom
eBu
ilder
sW
indo
w a
nd D
oor
Man
ufac
turin
g As
sn.
Air C
ondi
tioni
ng,
Heat
ing,
and
Re
frig
erat
ion
Inst
.
Dav
id M
cKin
ley
(R-W
V)Ro
bert
Lat
ta (R
-OH)
M. B
lack
burn
(R-T
N)Je
anne
Sha
heen
(D-N
H)M
ark
War
ner (
D-V
A)
Con
stru
ctio
n &
Rea
l Est
ate
Fig
ure
A.5
:E
stim
ate
dLSNM
Posi
tion
s:E
nerg
yB
ills
.W
ep
rese
nt
the
sam
evis
ual
izat
ion
asin
Fig
ure
3b
ut
for
theLSNM
infe
rred
from
ad
atase
tb
uil
tw
ith
on
lyen
ergy-r
elat
edb
ills
.T
he
sam
ecl
ust
erin
gin
toin
du
stry
-bas
edgr
oup
sar
ises
:in
add
itio
nto
anum
ber
ofm
ean
ingf
ul
ener
gy
sub
-in
du
stri
es(C
oal
,H
yd
rop
ower
,O
il,
Ren
ewab
les,
Uti
liti
es),
we
see
clu
ster
sco
rres
pon
din
gto
oth
erin
du
stri
esh
avin
gan
inte
rest
inen
ergy
legi
slati
on
(Au
tom
oti
ve,
Food
,C
onst
ruct
ion
).W
eal
soob
serv
ea
larg
e-sc
ale
div
isio
nb
etw
een
clu
ster
sp
erta
inin
gto
fuel
and
tran
spor
tati
onon
the
one
han
d,
and
clu
ster
sp
erta
inin
gto
elec
tric
ity
gen
erat
ion
and
dis
trib
uti
onon
the
oth
er(o
nth
eto
pan
db
otto
mof
our
plo
t,re
spec
tivel
y).
35
data {int<lower=2> N_row; // number of rowsint<lower=2> N_col; // number of columnsint<lower=1> D; // dimensionality of latent spaceint<lower=0> edges[N_row, N_col]; // connection strength data
}
transformed data {int flat_ix;int flat_edges[N_row * N_col];
flat_ix = 1;for (i in 1:N_row) {
for (j in 1:N_col) {flat_edges[flat_ix] = edges[i][j];flat_ix = flat_ix + 1;
}}
}
parameters {vector[D] mu_col_embedding;vector<lower=0.05>[D] cov_row_embedding_diag;cov_matrix[D] cov_col_embedding;row_vector[D] row_embedding[N_row];row_vector[D] col_embedding[N_col];
real mu_row_factor;real<lower=0.05> sigma_row_factor;real<lower=0.05> sigma_col_factor;vector[N_row] row_factor;vector[N_col] col_factor;
}
model {int flat_jx;vector[N_row * N_col] flat_log_means;
row_factor ∼ normal(mu_row_factor, sigma_row_factor);col_factor ∼ normal(0.0, sigma_col_factor);
row_embedding ∼ multi_normal(rep_vector(0.0, D), diag_matrix(cov_row_embedding_diag));
col_embedding ∼ multi_normal(mu_col_embedding, cov_col_embedding);
flat_jx = 1;for (i in 1:N_row) {for (j in 1:N_col) {flat_log_means[flat_jx] =row_factor[i] + col_factor[j] - distance(row_embedding[i], col_embedding[j]);
flat_jx = flat_jx + 1;}
}flat_edges ∼ poisson_log(flat_log_means);
}
Listing A.1: Stan code defining a sampler for the posterior distribution of the LSNM. Note that ford = 1 the model is greatly simplified, and it is much more efficient to remove the extra dimensionfrom the types related to the latent space position distribution.
36
−4 −2 0 2 4Latent Space Dimension 1
−4
−3
−2
−1
0
1
2
3
4
5
Lat
ent
Sp
ace
Dim
ensi
on
2
Typical Latent Space Positions: Politicians
−4 −2 0 2 4Latent Space Dimension 1
−4
−3
−2
−1
0
1
2
3
4
5
Lat
ent
Sp
ace
Dim
ensi
on
2
Typical Latent Space Positions: Lobbying Clients
Figure A.6: Latent Space Position Uncertainties. One of the cardinal advantages of theBayesian formulation of our latent space model is that it allows us to investigate the posteriordistribution beyond a point estimate of the parameters. We illustrate here an estimate of the“typical set” of each agent’s latent space position, an ellipse containing most of the samples drawnof their position by our MCMC algorithm. The computations giving the parameters of these ellipsesare detailed in Section A.3.3. The uncertainties are visibly small enough that we may surmise theclusters drawn in Figure 3 to be “stable”, in the sense that similar clusters would appear in atypical draw from the posterior distribution.
Popularity Factor Variances For popularity factors αi and βj , it is more convenient to consider
the variance of the estimates of the exponentials exp(αi) and exp(βj): first, these are non-negative
and so their statistics are easier to understand, and second they are the quantities we visualize
and are ultimately more interested in, since the interaction mean scales linearly in each. Thus, we
compute the standard error (standard deviation divided by mean) of each of these quantities. As
we did for the position variances, since there are many popularity factors and we analyze them in
this work only at a coarse-grained level, we prefer to visualize all of the standard errors at once,
giving in Figure A.7 their histograms for the main dataset. We observe that the distributions of the
standard errors are strongly concentrated on the low end; manual inspection reveals that the agents
with the highest standard errors are those with the lowest degrees, and so are less significant to
our substantive analysis anyway (these agents also tend to have smaller popularity factors, which
can further inflate the standard error). In both the latent positions and popularity factors, we see
that the politician parameters have more uncertainty than the lobbying client parameters, which is
consistent with our model finding that the latent space structure is imposed primarily by lobbying
client industry groupings.
37
0.1 0.2 0.3 0.4Standard Error of exp(αi)
0
25
50
75
100
125
150
Nu
mb
ero
fL
ob
byin
gC
lien
ts
0.0 0.2 0.4 0.6 0.8Standard Error of exp(βj)
Nu
mb
ero
fP
olit
icia
ns
Figure A.7: Latent Space Model Popularity Factor Uncertainties. Distributions of thestandard errors (standard deviation as a fraction of the mean) of the sampled popularity factorsfor all lobbying clients and all politicians.
Trace Plots In Figure A.8, we show two representative sets of trace plots for latent space position
and popularity factor parameters for one lobbying client and one politician. Generally, and as is
visible in these particular examples, the marginal distributions for lobbying client parameters mix
more rapidly than those of politician parameters, and the marginals for popularity factors mix
more rapidly than those of latent positions.
A.3.4 Identifiability in Multidimensional Log-Quadratic Latent Space Models
Latent space models that measure distance between latent positions with the Euclidean distance
‖θi−ψj‖ typically have means depending (after a suitable logarithmic or sigmoidal transformation
for Poisson or Bernoulli distributions for the incidence matrix entries) linearly on either ‖θi −ψj‖or ‖θi − ψj‖2 (we call these log-linear and log-quadratic latent space models, respectively). We
chose the former dependence for our model after finding that it gave a latent space that was better-
behaved numerically and more interpretable. In this section, we give a brief heuristic mathematical
argument and some numerical evidence on our datasets for why this might be the case.
We suggest that the issue may arise from a new invariance in the log-quadratic LSNM, which
only appears in models with ambient dimension d > 1 (explaining why, as far as we know, it has
not been observed previously). The problem of indentifiability (or “aliasing”) as it arises in our
model is that the log-mean of the Poisson distributions we assign to Ai,j , given by
logµi,j = αi + βj + ‖θi −ψj‖, (13)
38
0.5
0.0
0.5
Politician: Dimension 1
1.0
0.5
Client: Dimension 1
1.0
0.5
0.0Politician: Dimension 2
0.5
1.0
Client: Dimension 2
0 200 400 600Iteration (Every 10)
1.0
0.5
Politician: Popularity Factor
0 200 400 600Iteration (Every 10)
0.4
0.6
0.8
Client: Popularity Factor
Figure A.8: Latent Space Model Trace Plots. We show trace plots for the model parameters(two latent space dimensions and one popularity factor) drawn from two (out of four) MCMCchains, for one client, Intel Corporation, and one politician, Rand Paul (R-KY).
admits certain transformations of (α,β,θ,ψ) that do not change the mean, and therefore do not
change the modeled distribution. This can create multimodal or ridged posterior distributions,
making sampling more difficult, so we would like to add additional assumptions (in Bayesian data
analysis this is usually done by adding “more informative” priors) to remove these symmetries.
Two classes of symmetries are easy to see: any constant can be added to all αi and subtracted
from all βj , and any translation, rotation, or reflection can be applied to both θi and ψj . These
identifiability issues are well-understood in the literature, and are addressed specifically for our
model in the main text.
But when we instead consider means νi,j satisfying
log νi,j = αi + βj + ‖θi −ψj‖2, (14)
we believe that a more subtle identifiability problem arises, which depends on all four of the
39
parameters together. We expand the distance term, obtaining
log νi,j = αi + βj +d∑`=1
(θi` − ψj`)2 =
(αi +
d∑`=1
θ2i`
)+
(βj +
d∑`=1
ψ2j`
)− 2
d∑`=1
θi`ψj` , (15)
and consider a family of transformations parametrized by d non-zero constants K1, . . . ,Kd, and
taking the form
θi` 7→ K`θi` for ` ∈ [d] (16)
ψj` 7→1
K`ψj` for ` ∈ [d] (17)
αi 7→ αi +
d∑`=1
(1−K2` )θ2i` (18)
βj 7→ βj +
d∑`=1
(1− 1
K2`
)ψ2j`
(19)
The idea is that the first two mappings ensure that the last term of (15) is unchanged, while the
last two adjust α and β so that the contributions due to the first two mappings are cancelled.
When d = 1, since there is just a single parameter K1, the αi either all increase or all decrease,
and the βj do the opposite, so if, as mentioned above, we have already constrained these parameters
to not admit translations, then this transformation will only be admissible if K1 = 1, which is the
identity transformation, so there is no identifiability problem. However, when d > 1, we can have
a non-trivial transformation with the αi and βj remaining centered (which we think of as∑m
i=1 αi
and∑n
j=1 βj remaining constant) so long as
0 =
m∑i=1
d∑`=1
(1−K2` )θ2i` =
d∑`=1
(1−K2` )
(m∑i=1
θ2i`
)(20)
0 =
n∑j=1
d∑`=1
(1− 1
K2`
)ψ2j`
=d∑`=1
(1− 1
K2`
) n∑j=1
ψ2j`
(21)
which generically admits solutions with K` not all identically equal to 1 when d > 1. We see
from our definitions (16) and (17) that under this family of transformations, we would expect the
latent positions to move roughly along hyperbolae. In Figure A.11, we verify this prediction with
a numerical experiment on our main dataset.
A.4 Model Comparison
We give a few points of comparison between our latent space and community models, taking the
point of view not of attempting to choose a single “best fit” model, but rather of confirming that
40
the models all consistently reflect the same underlying properties of the lobbying network. There
are two components of the models to compare: first, both the dc-biSBM and the LSNM include
latent variables playing the role of “popularity factors”, which we named in both models αi and
βj for lobbying clients and politicians respectively (though in the LSNM their role differed by
an exponential transform). In Figure A.12, we compare these values and confirm that they are
positively correlated for both politicians and lobbying clients. Second, in the LSNM we assign
latent positions to each agent, while in the community models we divide up the agents into discrete
clusters. This prompts the question of whether the latent positions of the LSNM “respect” the
clusterings suggested by the community models, i.e., of whether the communities identified the
block models are localized in the latent space. We address this question in Figure A.14, and
Figure A.15 for the biSBM and dc-biSBM models, respectively, observing that the latter both gives
less weight to high-degree nodes, and is more aligned with the LSNM geometric representation.
Figure A.10 shows the incidence matrix A, with its rows and columns ordered according to
the groupings given by the biSBM (top panel) and the dc-biSBM (bottom panel). Both models
show that there exist clear “checkerboard” community patterns in the lobbying network data, and,
as mentioned before, without degree correction the biSBM suffers from the flaw of grouping high-
degree nodes together. (Indeed, one of the client clusters found without degree correction consists
of exactly the five lobbying clients with highest degree.) The dc-biSBM, in contrast, gives much
more balanced cluster sizes and less concentration of the highest-degree agents. Degree correction
also yields cluster memberships which, especially on the lobbying client side, correspond more
closely to the clusters we found previously in the LSNM.
Lastly, Figure 5 overlays the clusters identified by dc-biSBM on the geometry obtained from
the latent space model. To facilitate a direct comparison, we show the latent positions of each
individual political actor as well as the subjective grey boundaries that we used to group clients
and politicians. We then form eight paired political communities, each consisting of one lobbying
client community and one politician community. For example, we group all clients in Client Group
1 and all legislators in Politician Group 4 and call them members of “Community 1.” Finally,
we assign different colors and markers to visually distinguish the eight political communities. The
figure shows that the community structures align remarkably well with each other: the boundaries
that we drew indeed correspond to distinct political communities or to mixtures of two or three
political communities (this is especially prevalent near the center of the latent space, which we
observed earlier did not exhibit strong industry-level clustering).
41
2 0 21D Latent Space Position: Lobbying Clients
0
10
20
30
40
50
Num
ber o
f Lob
byin
g Cl
ient
s
InsuranceFinanceEnergyConservationTechnologyTelecom
Figure A.9: We illustrate that some of the clusters we identify in the two-dimensional model appearin the one-dimensional model as well, with some of the same adjacencies between clusters, thoughthere are more “outliers” positioned apart from their clusters.
Polit
ician
s
Bipartite Stochastic Block Model
0
40
80120160
Lobbying Clients
Polit
ician
s
Degree-Corrected Bipartite Stochastic Block Model
0
40
80120160
Figure A.10: Community Model Comparison. Results of the biSBM, and the dc-biSBM on themain dataset, shown by permuting the incidence matrix to group together the clusters that eachalgorithm finds. Both models identify community structure, and, as expected, the dc-biSBM infersmuch more balanced cluster sizes.
42
−4 −2 0 2 4Latent Space Dimension 1
−4
−2
0
2
4
Lat
ent
Sp
ace
Dim
ensi
on
2
Typical Positions: Politicians (Linear)
−4 −2 0 2 4Latent Space Dimension 1
−4
−2
0
2
4
Lat
ent
Sp
ace
Dim
ensi
on
2
Typical Positions: Lobbying Clients (Linear)
−2 0 2Latent Space Dimension 1
−2
−1
0
1
2
Lat
ent
Sp
ace
Dim
ensi
on
2
Typical Positions: Politicians (Quadratic)
−2 0 2Latent Space Dimension 1
−2
−1
0
1
2
Lat
ent
Sp
ace
Dim
ensi
on
2
Typical Positions: Lobbying Clients (Quadratic)
Figure A.11: Identifiability Problems in Log-Quadratic Latent Space Model. We see thattypical latent space positions “stretch” along hyperbolae in latent space models with a Poissonmean depending log-quadratically on the distance between positions, which is much mitigated inthe same model depending log-linearly on the distance.
0.00 0.05 0.10αi Value in dc-biSBM
0
10
20
30
exp
(αi)
Val
ue
inL
SN
M
(ρ = 0.73)
0.0 0.2 0.4βj Value in dc-biSBM
0
20
40
60
exp
(βj)
Val
ue
inL
SN
M
(ρ = 0.63)
Figure A.12: Popularity Factor Comparison. We plot the popularity factors obtained by theLSNM and dc-biSBM, confirming that there is a fairly strong positive correlation between theirvalues. It is most natural to take the exponential of the factors in the LSNM in this comparison,as can be seen from the formal expressions for the models’ distributions in the main text.
43
A.5 Bipartite Link Community Model EM Algorithm
In this appendix, we present a more detailed derivation of the EM update equations for the link
community model. After discarding constant terms, the log-likelihood of this model is given by
log P(A | α,β,κ) =
m∑i=1
n∑j=1
Ai,j log
(k∑z=1
κzαi,zβj,z
)−
m∑i=1
n∑j=1
k∑z=1
κzαi,zβj,z (22)
We then apply the standard technique of introducing parameters qi,j(z) that form a probability
distribution over z for fixed i and j and applying Jensen’s inequality to obtain the objective function
of our optimization task, a lower bound on the log-likelihood:
L(A,α,β,κ, q)df=
m∑i=1
n∑j=1
k∑z=1
(Ai,jqi,j(z) log
(κzαi,zβj,zqi,j(z)
)− κzαi,zβj,z
)(23)
≤ log P(A | α,β,κ).
We then seek to maximize L via coordinate ascent. Maximizing with respect to the qi,j(z) with all
other parameters fixed simply sets these parameters to the values that make Jensen’s inequality
sharp, which are
qi,j(z) =κzαi,zβj,z∑kz=1 κzαi,zβj,z
. (24)
It is also straightforward to differentiate with respect to κz, obtaining the update
κz =
∑mi=1
∑nj=1Ai,jqi,j(z)∑m
i=1
∑nj=1 αi,zβj,z
. (25)
For the α and β parameters, we must constrain our optimization to respect the normalization that
for all z,∑m
i=1 αi,z =∑n
j=1 βj,z = 1. If add to L Lagrange multiplier terms λz(1 −∑m
i=1 αi,z) +
µz(1−∑n
j=1 βj,z), then we obtain the updates
αi,z =
∑nj=1Ai,jqi,j(z)
λz +∑n
j=1 κzβj,z, (26)
βj,z =
∑mi=1Ai,jqi,j(z)
µz +∑m
i=1 κzαi,z, (27)
44
but now we see that to obtain the desired normalization we must in fact take λz and µz such that
they cancel out these denominators and leave us with the simpler
αi,z =
∑nj=1Ai,jqi,j(z)∑m
i=1
∑nj=1Ai,jqi,j(z)
, (28)
βj,z =
∑mi=1Ai,jqi,j(z)∑m
i=1
∑nj=1Ai,jqi,j(z)
, (29)
which in particular do not involve any coupling between αi,z and βj,z, meaning that a single itera-
tion of updates suffices for the M step of our EM algorithm, simplifying the calculation substantially
compared to the nested coordinate ascents that are usually involved in mixed-membership stochas-
tic block models.
A.6 Link Community Null Model Analysis
The configuration model, a standard tool in the theory of random graphs, gives a natural way to
build a null model for our analyses. In this model, the degree distribution of the graph is retained,
but all connections are “rewired” randomly. More precisely, in our bipartite setting, if there are m
lobbying clients indexed 1, . . . ,m and n politicians indexed 1, . . . , n, suppose that lobbying client
i is connected to a total of dLi politicians, and politician j is ocnnected to a total of dPj lobbying
clients. Note that the total number of edges E is given by the sums of either of these quantities:
E =m∑i=1
dLi =n∑j=1
dPj . (30)
Now, we perform the following random process to generate a draw from the configuration model:
1. From each lobbying client i, produce dLi “stubs” or partial edges, which we may label
eLi,1, . . . , eLi,dLi
. Likewise, from each politician j, produce stubs ePj,1, . . . , ePj,dPj
.
2. Generate a uniformly random matching of the two sets {eLi,k} and {ePj,`} (note that by the
preceding remark, the two have equal size.
3. Generate a bipartite graph by putting an edge between lobbying client i and politician j
whenever eLi,k and ePj,` were matched for some k, ` in the previous step (so that the total
number of edges is the total number of pairs k, ` for which these two stubs were matched).
It is simple to verify that the configuration model satisfies the following desirable properties. First,
every draw of the configuration model has the same degree distribution as the original graph; that
is, the collections of numbers {dLi } and {dPj } remain the same. And second, the configuration
model draws a uniform sample from the bipartite graphs having those degree distributions. In this
45
sense, the configuration model is the canonical null model that retains the degree distribution of a
graph, but “forgets” all other details.
To understand how exceptional our findings with the biLCM are, we analyze the link community
distribution entropy under the configuration model for the full 113th Congress dataset here. In
particular, in Figure A.13, we compare the entropy distributions obtained from the biLCM run
on this dataset with averaged distributions obtained from many draws of the configuration model
applied to the same dataset (as we would hope, those distributions concentrate fairly well around
a single representative null entropy distribution). We see that the entropies of the link community
distributions for the actual dataset are much lower, meaning the link community distributions
are much more concentrated, than those for the null model. This gives further evidence that
the communities we find are statistically significant, and not, for instance, merely artifacts of the
graph’s degree distribution (not a farfetched possibility, as less sophisticated community detection
algorithms often suffer from detecting spurious communities consisting only of high-degree nodes).
0 1 2 3Lobbying Client Entropy
0
20
40
60
80
100
Freq
uenc
y
Null Model113th Congress
0 1 2 3Politician Entropy
0
10
20
30
40
50
Figure A.13: Link Community Entropy Distribution vs. Null Model. We plot the distri-bution of entropies of link community distributions obtained by the biLCM run on the full 113thCongress dataset, compared to an average (per histogram bin) of the same distribution for sev-eral draws of the null configuration model generated from the same dataset. We observe thatfor both lobbying clients and politicians, the actual link community distributions are much moreconcentrated than those obtained in the null model, supporting the statistical significance of thecommunities we describe in the main text.
46
5.0
2.5
0.0
2.5
Client Cluster 1 Client Cluster 2 Politician Cluster 1 Politician Cluster 2
5.0
2.5
0.0
2.5
Client Cluster 3 Client Cluster 4 Politician Cluster 3 Politician Cluster 4
5.0
2.5
0.0
2.5
Client Cluster 5 Client Cluster 6 Politician Cluster 5 Politician Cluster 6
2.5 0.0 2.5
5.0
2.5
0.0
2.5
Client Cluster 7
2.5 0.0 2.5
Client Cluster 8
2.5 0.0 2.5
Politician Cluster 7
2.5 0.0 2.5
Politician Cluster 8
Figure A.14: LSNM vs. biSBM. We plot the latent positions of politicians and lobbying clientsfrom the LSNM, split by their memberships in clusters from the biSBM, with both run on themain dataset. For the sake of visual clarity, we no longer vary the sizes of the plotted latent spacepositions based on popularity factors. Localization of groups in the latent space suggests that thelatent space model is somewhat consistent with the biSBM, but much of the fine-grained lobbyingclient clustering we observe in Figure 3 is not reflected in the biSBM results. (The largest clustersbiSBM finds are those with low lobbying and sponsorship activity, and correspond to the mostspread out clusters in these plots.)
47
5.0
2.5
0.0
2.5
Client Cluster 1 Client Cluster 2 Politician Cluster 1 Politician Cluster 2
5.0
2.5
0.0
2.5
Client Cluster 3 Client Cluster 4 Politician Cluster 3 Politician Cluster 4
5.0
2.5
0.0
2.5
Client Cluster 5 Client Cluster 6 Politician Cluster 5 Politician Cluster 6
2.5 0.0 2.5
5.0
2.5
0.0
2.5
Client Cluster 7
2.5 0.0 2.5
Client Cluster 8
2.5 0.0 2.5
Politician Cluster 7
2.5 0.0 2.5
Politician Cluster 8
Figure A.15: LSNM vs. dc-biSBM. We plot the latent positions of politicians and lobbying clientsfrom the LSNM, split by their memberships in clusters from the dc-biSBM, with both run on themain dataset. Almost all clusters found by dc-biSBM are closely localized in the latent space,showing that the dc-biSBM captures much of the same information as the latent space model.
48
A.7 113th Congress Latent Space Model Clusters
The tables below list all client and politician members of each of the clusters shown in Figure 3.
A.7.1 Cluster: Finance
Lobbying Client NameAmerican Bankers Assn. Securities Assn.American Land Title Assn.American Council of Life InsurersBank of America Corp.Capital One Financial Corp.Center for Responsible LendingCitigroup Management Corp.Clearing House Payments Co. LLCCredit Union Ntl. Assn.Community Associations InstituteCommunity Bankers Assn. of IllinoisCompass BankDeutsche Bank Securities, Inc.Fifth Third BancorpFinancial Services RoundtableGenworth Financial, Inc.Independent Community Bankers of AmericaInvestment Company InstituteIntl. Swaps and Derivatives Assn.Massachusetts Mutual Life Insurance Co.Mortgage Bankers Assn.MetLife Group, Inc.Morgan StanleyNtl. Assn. of Federal Credit UnionsNew York Bankers Assn.Petroleum Marketers Assn. of AmericaPrincipal Financial GroupRadian Group, Inc.Securities Industry and Financial Markets Assn.State Farm Insurance CompaniesNorthwestern Mutual Life Insurance Co.TIAA-CREFTransamerica CompaniesUnum GroupWells Fargo & Co.
Politician Name State PartyBarr, Garland KY RepublicanCampbell, John CA RepublicanCorker, Bob TN RepublicanDuffy, Sean WI RepublicanFincher, Stephen TN RepublicanGarrett, Scott NJ RepublicanHahn, Janice CA DemocratHultgren, Randy IL RepublicanIsakson, John GA RepublicanJohnson, Tim SD DemocratKing, Peter NY RepublicanLuetkemeyer, Blaine MO RepublicanMcHenry, Patrick NC RepublicanMiller, Gary CA RepublicanNeal, Richard MA DemocratPerlmutter, Ed CO DemocratPittenger, Robert NC RepublicanShelby, Richard AL RepublicanSherman, Brad CA DemocratWagner, Ann MO RepublicanWaters, Maxine CA Democrat
A.7.2 Cluster: Travel
Lobbying Client NameGlobal Business Travel Assn.Mariott International, Inc.Morphotrust USASabre Global, Inc.U.S. Travel Assn.United States Olympic Committee
Politician Name State PartyCoats, Daniel IN RepublicanEmerson, Jo Ann MO RepublicanGowdy, Trey SC RepublicanHeck, Joseph NV RepublicanMiller, Candice MI RepublicanQuigley, Mike IL Democrat
49
A.7.3 Cluster: Insurance & Real Estate
Lobbying Client NameACE INA HoldingsAflac, Inc.Allstate Insurance CompanyAmerican Family Mutual Insurance CompanyAmerican Insurance Assn.Assn. for Advanced Life UnderwritingAmerican Council of Life InsurersBuilding Owners and Managers Assn. Intl.Chubb CorporationCincinnati Financial CorporationFarmers Group, Inc.Hartford Financial Services GroupIndependent Insurance Agents & Brokers
of AmericaJ.P. Morgan Chase & CompanyKPMG LLPLiberty Mutual GroupNational Assn. of Insurance and Financial
AdvisorsNational Assn. of Mutual Insurance CompaniesNational Assn. of Professional Insurance AgentsNational Assn. of Real Estate Investment TrustsNationwide Mutual Insurance CompanyProperty Casualty Insurers Assn. of AmericaPrudential Financial, Inc.Real Estate RoundtableState Farm Mutual Automobile Insurance
CompanyThe Travelers Companies, Inc. and SubsidiariesThe Village of Bald Head IslandUnited Services Automobile Assn.Zurich
Politician Name State PartyBrown, Sherrod OH DemocratCapuano, Michael MA DemocratChambliss, Saxby GA RepublicanCollins, Susan ME RepublicanCrawford, Eric AR RepublicanCummings, Elijah MD DemocratDiaz-Balart, Mario FL RepublicanFarenthold, Blake TX RepublicanGrimm, Michael NY RepublicanHuizenga, Bill MI RepublicanHurt, Robert VA RepublicanMenndez, Robert NJ DemocratNeugebauer, Randy TX RepublicanPalazzo, Steven MS RepublicanRichmond, Cedric LA DemocratRoss, Dennis FL RepublicanSires, Albio NJ DemocratStivers, Steve OH RepublicanThompson, Bennie MS DemocratWilson, Frederica FL Democrat
A.7.4 Cluster: Universities
Lobbying Client NameAircraft Owners & Pilots Assn.Assn. of Science-Technology Centers, Inc.Boston UniversityCalifornia Institute of TechnologyMassachusetts Institute of TechnologyMcGraw Hill EducationNtl. Business Aviation Assn.Northeastern UniversityNorthwestern UniversityOhio State UniversityUniversity of CincinattiUniversity of IllinoisUniversity of RochesterUniversity of Southern CaliforniaUniversity of VirginiaVanderbilt University
Politician Name State PartyBachus, Spencer AL RepublicanCollins, Chris NY RepublicanDoyle, Michael PA DemocratFoxx, Virginia NC RepublicanJohnson, Henry GA DemocratManchin, Joe WV DemocratMesser, Luke IN RepublicanReed, Tom NY RepublicanReichert, David WA RepublicanRokita, Todd IN Republican
50
A.7.5 Cluster: Technology
Lobbying Client NameActavisAmazon.comAmazon Corporate LLCAmerican Assn. of Law LibrariesAmerican Express CompanyAmerican Intellectual Property Law Assn.American Mushroom InstituteAssn. of National Advertisers, Inc.Business RoundtableBusiness Software AllianceCompTIA Member Services, LLCConsumer Electronics Assn.Dell, Inc.Digital 4thDirect Marketing Assocation, Inc.Eastman Kodak CompanyEbay, Inc.EMC CorporationExperian, Inc.Facebook, Inc.Google, Inc.Hewlett-Packard CompanyIntel CorporationInteractive Advertising BureauIntl. Council of Shopping Cneters Intuit, Inc.LinkedIn CorporationMagazine Publishers of AmericaMastercardMcAfee, Inc.Microsoft CorporationNational Venture Capital Assn.Oracle CorporationPfizer, Inc.Qualcomm, Inc.Semiconductor Industry Assn.Software & Information Industry Assn.Texas Instruments, Inc.The Internet AssocationVisa, Inc.Yahoo!, Inc.
Politician Name State PartyBarton, Joe TX RepublicanBrooks, Susan IN RepublicanCarper, Thomas DE DemocratCarter, John TX RepublicanCastor, Kathy FL DemocratChaffetz, Jason UT RepublicanCoble, Howard NC RepublicanCornyn, John TX RepublicanDeFazio, Peter OR DemocratDeutch, Theodore FL DemocratEnzi, Michael WY RepublicanHatch, Orrin UT RepublicanHeck, Denny WA DemocratHolding, George NC RepublicanIssa, Darrell CA RepublicanJeffries, Hakeem NY DemocratKaptur, Marcy OH DemocratLeahy, Patrick VT DemocratLofgren, Zoe CA DemocratMcCaskill, Claire MO DemocratSalmon, Matt AZ RepublicanSchumer, Charles NY DemocratShea-Porter, Carol NH DemocratToomey, Patrick PA RepublicanWomack, Steve AR RepublicanYoder, Kevin KS Republican
A.7.6 Cluster: Gun Rights
Lobbying Client NameBoyd Gaming CorporationBridgepoint EducationEverytown for Gun Safety Action FundGun Owners of America, Inc.Ntl. Assn. for Gun RightsNtl. Rifle Assn. of America
Politician Name State PartyDavis, Danny IL DemocratJordan, Jim OH RepublicanKelly, Robin IL DemocratLowenthal, Alan CA DemocratMcCarthy, Carolyn NY DemocratStockman, Steve TX Republican
51
A.7.7 Cluster: Telecom
Lobbying Client NameAmerican Cable Assn. , Inc.Americans for Tax ReformAT&T CorporationAT&T Services, Inc. and its AffiliatesCBS CorporationCincinnati BellComcast CorporationCompetitive Carriers Assn.Cox Enterprises, Inc.CTIA: The Wireless Assn.Endo Pharmaceuticals, Inc.IHeartMedia, Inc.Loews CorporationMultistate Tax CommissionNational Assn. of BroadcastersNational Cable & Telecommunications Assn.National Telecommunications Cooperative Assn.Time Warner Cable, Inc.Twenty-First Century Fox, Inc.TwinLogic StrategiesUnited States Telecom Assn.Verizon
Politician Name State PartyAmash, Justin MI RepublicanAyotte, Kelly NH RepublicanChabot, Steve OH RepublicanCollins, Doug GA RepublicanConaway, K. TX RepublicanEshoo, Anna CA DemocratGoodlatte, Bob VA RepublicanHeller, Dean NV RepublicanKeating, William MA DemocratLatta, Robert OH RepublicanMatsui, Doris CA DemocratMcCain, John AZ RepublicanMcCaul, Michael TX RepublicanMeng, Grace NY DemocratNunes, Devin CA RepublicanRockefeller, John WV DemocratRogers, Mike MI RepublicanRush, Bobby IL DemocratWalden, Greg OR RepublicanWasserman Schultz, Debbie FL DemocratWatt, Melvin NC DemocratWyden, Ron OR Democrat
A.7.8 Cluster: Military, Veterans’ Affairs
Lobbying Client NameAmerican LegionDisabled American VeteransFleet Reserve Assn.Iraq and Afghanistan Veterans of America, Inc.Military Officers’ Assn. of AmericaParalyzed Veterans of AmericaRetired Enlisted AssocationUnited Spinal Assn.
Politician Name State PartyBishop, Sanford GA DemocratBoozman, John AR RepublicanBrownley, Julia CA DemocratBurr, Richard NC RepublicanButterfield, George NC DemocratCoffman, Mike CO RepublicanGallego, Pete TX DemocratGutirrez, Luis IL DemocratKirkpatrick, Ann AZ DemocratLangevin, James RI DemocratLarsen, Rick WA DemocratMcCarthy, Kevin CA RepublicanMiller, Jeff FL RepublicanNegrete McLeod, Gloria CA DemocratO’Rourke, Beto TX DemocratPerry, Scott PA RepublicanPingree, Chellie ME DemocratRuiz, Raul CA DemocratRunyan, Jon NJ RepublicanSinema, Kyrsten AZ DemocratTakano, Mark CA DemocratTitus, Dina NV DemocratTsongas, Niki MA DemocratWalorski, Jackie IN RepublicanWenstrup, Brad OH Republican
52
A.7.9 Cluster: Environmentalism
Lobbying Client NameAmerican Beverage Assn.American Iron and Steel InstituteAmerican Motorcyclist Assn.American RiversArkema, Inc.Assn. of California Water AgenciesBlue Green AllianceBunge North America, Inc.Council on Federal Procurement of Architectural
Engineering ServiceEarthjustice Legal Defense FundEnvironmental Working GroupGrocery Manufacturers Assn.International Code CouncilJackson County MississippiJDRF Intl.Las Vegas Valley Water DistrictLeague of Conservation VotersManagement Assn. for Private Photogrammetric
SurveyorsMetropolitan Water District of Southern
CaliforniaNtl. Corn Growers Assn.Ntl. Right to Work CommitteeNtl. Society of Professional SurveyorsNtl. Wildlife FederationNature ConservancyNorthern California Power AgencyOcean ConservancyOpen Space InstitutePublic Power CouncilSafari Club Intl.Sierra ClubSociety of the Plastics Industry, Inc.Solano CountySouthern Environmental Law CenterSouthern Nevada Water AuthorityThe Wilderness SocietyTransportation Communications Intl. UnionTrust for Public LandsUnited Phosphorus, Inc.Waggoner Engineering, Inc.Waterways Council, Inc.
Politician Name State PartyBishop, Rob UT RepublicanBishop, Timothy NY DemocratBooker, Cory NJ DemocratBordallo, Madeleine GU DemocratBoxer, Barbara CA DemocratCalvert, Ken CA RepublicanCasey, Robert PA DemocratCosta, Jim CA DemocratCrapo, Michael ID RepublicanDaines, Steve MT RepublicanDavis, Rodney IL RepublicanDeGette, Diana CO DemocratFarr, Sam CA DemocratFleming, John LA RepublicanGaramendi, John CA DemocratGibbs, Bob OH RepublicanGosar, Paul AZ RepublicanHagan, Kay NC DemocratHastings, Alcee FL DemocratHeinrich, Martin NM DemocratHorsford, Steven NV DemocratHuffman, Jared CA DemocratJackson Lee, Sheila TX DemocratKind, Ron WI DemocratKinzinger, Adam IL RepublicanLautenberg, Frank NJ DemocratLujn, Ben NM DemocratMaloney, Sean NY DemocratMcClintock, Tom CA RepublicanMurkowski, Lisa AK RepublicanPaul, Rand KY RepublicanRuppersberger, C. MD DemocratSchweikert, David AZ RepublicanScott, Austin GA RepublicanThompson, Mike CA DemocratTipton, Scott CO RepublicanUdall, Tom NM DemocratValadao, David CA RepublicanVela, Filemon TX DemocratVitter, David LA RepublicanWilliams, Roger TX RepublicanWittman, Robert VA RepublicanYoung, Don AK Republican
53
A.7.10 Cluster: Energy
Lobbying Client NameAmerican Gas Assn.American Municipal Power, Inc.American Natural Gas Alliance, Inc.American Petroleum InstituteAmerican Public Power Assn.Arch CoalBerkshire Hathaway EnergyBiotechnology Industry Organization (BIO)Black Hills Corp.BP America, Inc.Centerpoint Energy, Inc.Chesapeake Energy Corp.Chevron USA, Inc.Clean Energy Fuels Corp.CMS Energy Corp.Consolidated Edison Co. of New YorkCovanta Energy Corp.DominionDTE EnergyDuke Energy Corp.Edison Electric InstituteEnergy Future HoldingsEntergy Services, Inc.Exelon Business Services LLCExxon Mobil Corp.FirstEnergy Corp.Ford Motor CompanyGreat Plains EnergyGrowth Energy, Inc.Hannon ArmstrongHollyFrontier CorporationHyundai Motor CompanyIndependent Petroleum Assn. of AmericaIntegrys Energy Group, Inc.Koch Companies Public Sector, LLCMarathon Oil CorporationMarathon Petroleum CorporationMissouri River Energy ServicesNtl. Corn Growers Assn.Ntl. Fisheries InstituteNtl. Grid USANtl. Ground Water Assn.Ntl. Mining Assn.Ntl. Propane Gas Assn.Nextera Energy, Inc.Nisource, Inc.NV EnergyOhio Municipal Electric Assn.Olin CorporationPacific Gas and Electric Co.
Politician Name State PartyAlexander, Rodney LA RepublicanBarrasso, John WY RepublicanBennet, Michael CO DemocratBridenstine, Jim OK RepublicanCapito, Shelley WV RepublicanClarke, Yvette NY DemocratClay, Wm. MO DemocratCoons, Chris DE DemocratCramer, Kevin ND RepublicanDuncan, Jeff SC RepublicanEngel, Eliot NY DemocratEnyart, William IL DemocratFlake, Jeff AZ RepublicanFlores, Bill TX RepublicanFranks, Trent AZ RepublicanGardner, Cory CO RepublicanGohmert, Louie TX RepublicanGreen, Gene TX DemocratHarper, Gregg MS RepublicanHeitkamp, Heidi ND DemocratHoeven, John ND RepublicanInhofe, James OK RepublicanJohanns, Mike NE RepublicanLaMalfa, Doug CA RepublicanLamborn, Doug CO RepublicanLankford, James OK RepublicanLarson, John CT DemocratLoebsack, David IA DemocratMarchant, Kenny TX RepublicanMarkey, Edward MA DemocratMatheson, Jim UT DemocratMcAllister, Vance LA RepublicanMcConnell, Mitch KY RepublicanMcKinley, David WV RepublicanMeehan, Patrick PA RepublicanMica, John FL RepublicanMullin, Markwayne OK RepublicanOlson, Pete TX RepublicanPayne, Donald NJ DemocratPeters, Gary MI DemocratPeters, Scott CA DemocratPompeo, Mike KS RepublicanRahall, Nick WV DemocratRothfus, Keith PA RepublicanScalise, Steve LA RepublicanSensenbrenner, F. WI RepublicanSerrano, Jos NY DemocratShaheen, Jeanne NH DemocratShimkus, John IL RepublicanSoutherland, Steve FL Republican
54
Peabody EnergyPeabody Investments Corp.Pepco Holdings, Inc.Pinnacle West Capital Corp.Poet, LLCPPL CorporationPublic Service Enterprise Group (PSEG)Puget Sound EnergyRenewable Fuels Assn.Salt River ProjectSempra EnergySheet Metal & Air Conditioning Contractors
Ntl. Assn.Shell Oil CompanySolar Energy Industries Assn.Southern CompanyThe Fertilizer InstituteUIL Holdings CorporationVectren CorporationXcel Energy, Inc.
Stewart, Chris UT RepublicanThornberry, Mac TX RepublicanTurner, Michael OH RepublicanUdall, Mark CO DemocratWaxman, Henry CA DemocratWelch, Peter VT DemocratWhitfield, Ed KY RepublicanWicker, Roger MS RepublicanYarmuth, John KY Democrat
55
A.7.11 Cluster: Health
Lobbying Client NameAARPAlzheimer’s Assn.American Academy of Dermatology Assn.American Academy of OphthalmologyAmerican Academy of OtolaryngologyAmerican Assn. for JusticeAmerican Assn. of Neurological SurgeonsAmerican Assn. of Nurse AnesthetistsAmerican Bar Assn.American Chiropractic Assn.American College of Emergency PhysiciansAmerican College of PhysiciansAmerican College of RheumatologyAmerican College of SurgeonsAmerican College of Surgeons Professional
Assn.American Fed. of State, County, and
Municipal EmployeesAmerican Health Care Assn.American Hospital Assn.American Medical Assn.American Optometric Assn.American Physical Therapy Assn.American Society of AnesthesiologistsAmerican Society of Interventional Pain
PhysiciansAmerica’s Essential HospitalsAssn. of American Medical CollegesChildren’s Healthcare of AtlantaCity of BaltimoreCoalition of Boston Teaching HospitalsCongress of Neurological SurgeonsCornell UniversityCubist PharmaceuticalsFederation of American HospitalsGreater New York Hospital Assn.Healthcare Leadership CouncilHospital & Healthsystem Assn. of
PennsylvaniaIllinois Hopsital Assn.Indiana University Health, Inc.International Brotherhood of TeamstersIowa Hospital Assn.Jewish Federation of Metropolitan ChicagoMassachusetts Medical SocietyMichigan State UniversityNtl. Active and Retired Federal Employees
Assn.Ntl. Air Traffic Controllers Assn.National Assn. of Psychiatric Health Systems
Politician Name State PartyBera, Ami CA DemocratBlack, Diane TN RepublicanBlunt, Roy MO RepublicanBraley, Bruce IA DemocratBucshon, Larry IN RepublicanBurgess, Michael TX RepublicanClark, Katherine MA DemocratCoburn, Thomas OK RepublicanCotton, Tom AR RepublicanCourtney, Joe CT DemocratCrowley, Joseph NY DemocratDeSantis, Ron FL RepublicanDent, Charles PA RepublicanEdwards, Donna MD DemocratGerlach, Jim PA RepublicanGingrey, Phil GA RepublicanGranger, Kay TX RepublicanGraves, Sam MO RepublicanHarris, Andy MD RepublicanHirono, Mazie HI DemocratJenkins, Lynn KS RepublicanJohnson, Sam TX RepublicanKildee, Daniel MI DemocratKingston, Jack GA RepublicanPascrell, Bill NJ DemocratPierluisi, Pedro PR DemocratPitts, Joseph PA RepublicanPrice, Tom GA RepublicanRenacci, James OH RepublicanRoskam, Peter IL RepublicanSchock, Aaron IL RepublicanSchwartz, Allyson PA DemocratSessions, Pete TX RepublicanSmith, Adrian NE RepublicanTierney, John MA DemocratWalberg, Tim MI Republican
56
National Assn. of Pediatric NursePractitioners
National Fed. of Federal EmployeesNational Treasury Employees UnionNebraska Hospital Assn.NetworkNorth Shore - Long Island Jewish Health
SystemNumbersUSA ActionOchsner Clinic FoundationPartners Healthcare, Inc.Professional Aviation Safety SpecialistsRutgers: The State University of New JerseySusan B. Anthony ListTenet Healthcare CorporationThe Ickes & Enright Group, Inc.The Trustees of Purdue UniversityTufts UniversityTulane UniversityUnited Parcel ServiceUniversity of CaliforniaUniversity of Northern IowaUniversity of Wisconsin-MadisonWorld Wildlife Fund, Inc.Yum! Brands, Inc.
57