The Fates of Challengers in US House Elections

The Fates of Challengers in U.S. House Elections: TheRole of Extended Party Networks in SupportingCandidates and Shaping Electoral Outcomes

Bruce A. Desmarais University of Massachusetts AmherstRaymond J. La Raja University of Massachusetts AmherstMichael S. Kowal University of Massachusetts Amherst

Extended party network (EPN) theory characterizes political parties in the United States as dynamic networks of interestgroups that collaboratively support favored candidates for office. Electoral predictions derived from EPN theory have yetto be tested on a large sample of races. We operationalize EPNs in the context of organized interest contributions to U.S.House campaigns. We deduce that support by a partisan community of interests signals the ideological credibility andappeal of a candidate. EPN integration overcomes voter ambiguity surrounding challengers ideological preferences, andresources provided by these coordinating interest groups promote a consistent message about the candidate. Using datafrom the 19942010 cycles, we apply network analysis to detect EPN support of challengers and find that EPN integrationsubstantially improves the electoral prospects of challengers. The effect of EPN integration is distinct from that of campaignresources. The findings provide support for EPN theory, as applied to congressional elections.

The Extended Party Network andCongressional Elections

Arich scholarly tradition exists in studying the or-ganizational forms of political parties and howsuch characteristics potentially shape key politi-cal outcomes (see, e.g.,Cotter 1989;Key1949;Ostrogorski1902; Schattschneider 1942).

That tradition is being renewed. Recent researchdrawing on network theory conceptualizes political par-ties as dynamic, dispersed systems of interconnected in-terest groups, centered on traditional formal party orga-nizations, which Koger, Masket, and Noel (2009) term

Bruce A. Desmarais is Assistant Professor, Department of Political Science, University of Massachusetts Amherst, Thompson Hall, 200HicksWay, Amherst,MA 01003 ([email protected]). Raymond J. La Raja is Associate Professor, Department of Political Science,University of Massachusetts Amherst, Thompson Hall, 200 Hicks Way, Amherst, MA 01003 ([email protected]). Michael S. Kowalis a Ph.D. Student, Department of Political Science, University of Massachusetts Amherst, Thompson Hall, 200 Hicks Way, Amherst, MA01003 ([email protected]).

We would like to thank the editor, the anonymous reviewers, Brian Schaffner, Maryann Barakso, Tatishe Nteta, Jesse Rhodes, DavidNickerson, Paul Herrnson, David Lazer, Scott McClurg, Rahsaan Maxwell and Dino Christenson for helpful feedback on this project.Previous drafts of this article were presented at the UMass American Politics Research Working Group, the 2012 Political NetworksConference at the University of Colorado Boulder, the 2012 Northeastern Political Science Association Annual Meeting in Boston, theConnections Conference on Network Science at the MIT Media Lab and the Boston-Cambridge Colloquium on Complexity and SocialNetworks at NortheasternUniversity. This researchwas supported in part by aUniversity ofMassachusetts Amherst Department of PoliticalScience Graduate Research Initiative Assistantship. All mistakes are our own.

Data for replication are available on the AJPS Dataverse Archive (http://dvn.iq.harvard.edu/dvn/dv/ajps).

extended party networks (EPNs). According to this the-oretical framework, the central functions of the politicalparty are to select and support candidateswhoaredeemedlikely to advance the party coalitions policy agenda oncein office (Bawn et al. 2012; Cohen et al. 2008; Herrnson2009; Koger, Masket, and Noel 2009; Masket 2009; Skin-ner, Masket, and Dulio 2012). This stands in contrast tothe dominant, politician-centered explanation for partyformation (Aldrich 1995), which attributes the originsof parties to the need for legislators to compromise andcooperate in passing legislation.

An important implication of the EPN theory isthat candidates, especially challengers, who are targetedby partisan coalitions of interests are inherently more

American Journal of Political Science, Vol. 00, No. 00, March 2014, Pp. 118

C 2014, Midwest Political Science Association DOI: 10.1111/ajps.12106

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2 BRUCE A. DESMARAIS, RAYMOND J. LA RAJA, ANDMICHAEL S. KOWAL

appealing to the party, on a policy basis, than those whodo not receive the support of the network. By extension,challengers who are backed by a significant contingent ofthe EPN will gain support from attentive voters who takecues fromvisible groups and activists in the party network(i.e., partisan elites; Dominguez 2011; Lupia 1994; Zaller1992) and should therefore have better prospects on Elec-tion Day than those who do not, regardless of campaignexpenditures by the candidates. The strength of this cue isbolstered by the consistency of preferences in the networkof interests and the coherence of the ideological messageamplified by the groups resources.

Research addressing party networks has focusedmostly on identifying patterns of connectivity amongparty organizations and interest groups (Grossman andDominguez 2009; Heaney et al. 2012; Herrnson 2009;Koger, Masket, and Noel 2009; Kolodny and Dwyre 1998;Skinner, Masket, and Dulio 2012). These studies revealpartisan affinities among interest groups with respect toinformation flows (Koger, Masket, and Noel 2009, 2010;Skinner, Masket, and Dulio 2012) and electoral or leg-islative activity (Grossman and Dominguez 2009). To thedegree that research has examined the impact of thesenetworks on political outcomes, it has been confined tolooking at the link between endorsements of prominentpoliticians and the likelihood that a candidate wins aparty nomination or general election (Cohen et al. 2008).These studies rely on a relatively small sample of can-didates in presidential nominations (Cohen et al. 2008)or local legislative elections (Masket 2009). Such studiesprovide preliminary evidence of an electoral influence ofEPNs. However, the electoral implications of EPN theoryhave yet to be tested on a large and representative sampleof electoral contests.

What is missing in the literature is a clear blueprintfor linking the activity of networked interest groups withoverlapping political agendas (i.e., political parties as con-ceived by Cohen et al. 2008 and Bawn et al. 2012) to theelectoral fates of the candidates they target. Our first goalin this article is to translate theories about extended partynetworks into empirical expectations regarding how par-tisan interest groups rally around selected candidates.Oursecond goal is to explore whether integration into thisnetwork of partisan interests affects election outcomes.To directly test interest group behavior and the electoralconsequences derived from EPN theory, we turn to cam-paign finance, which is a central mechanism by whichdisparate interest groups attempt to influence electoraloutcomes. We focus on outcomes in U.S. House elec-tions from 1994 to 2010. Political contributions by or-ganizations provide a robust measure of the intensity ofsupport for candidates, while allowing us to distinguish

behaviors among a broad population of groups seekingto influence federal elections. We draw on the frameworkof network analysis to operationalize the EPN using dataon group contributions to, and independent spendingfor, candidates for the U.S. House. We do this through acommunity detection (Newman2006) algorithm that isinformed by the structure of advocacy that underlies EPNtheory.

We find a consistent pattern of campaign financecommunity structure that strongly coheres with EPNtheory. That is, challengers are integrated into eitherhighly partisan, well-resourced communities of inter-est group political action committees (PACs) exhibitingdense, overlapping ties (i.e., the EPN) or a large bipartisancatch-all community that is defined by meager support.We find that integration of a challenger into the densepartisan communities of PAC contributors substantiallyincreases the probability of electoral success, an effect thatgoes well beyond campaign spending. Our findings pro-vide support for a theory of political parties as interestgroup coalitions of policy demanders. It also illustrates,in practice, why political parties may fail to converge ide-ologically on the median voter due to the types of candi-dates they help elect.

The Extended Party Network

Despite thewell-documented resurgence of congressionalpolitical parties (Aldrich 1995; Heberlig and Larson 2012;Herrnson 1988), contemporary research addressing con-gressional election outcomes offers little to say about theimpact of party organizations, or any coordinated polit-ical efforts beyond individual campaigns (see, e.g., Jones2010;Lazarus2008;WoonandPope2008).Of course,par-tisanship (e.g., of candidates, districts, the presidency, andCongress) plays an essential role in every account explain-ing outcomes in congressional elections (Canes-Wrone,Brady, and Cogan 2002; Jacobson 1989; Woon and Pope2008). This dichotomythe prevalence of partisanshipand the absence of party organizationfits well with thepolitician-centered understanding of parties. That is, par-ties arise in response to the needs of policymakers,mainlylegislators serving in the same body, to coordinate policyagendas in order to win individual gains through mutualsupport (Aldrich 1995). Thus, according to the domi-nant theoretical understanding, parties are organizationsborn out of service to legislators, with the central orga-nizational focus being the top-down enforcement by theparty leadership of adherence to a unified policy agenda(Cox and McCubbins 1993).

EXTENDED PARTY NETWORKS IN U.S. HOUSE ELECTIONS 3

The theoryof theparty as a coalitionof policydeman-ders, which we equate with EPN theory (Bawn et al. 2012;Cohenet al. 2008;GrossmanandDominguez2009;Koger,Masket, and Noel 2009), holds that parties arise from thebenefits that organized interests realize from aggregatingagendas and coordinating resources in pursuit of electoraland policy goals.1 Specialized interests that are relativelyrich in resources (i.e., money, large memberships, or ex-pertise) typically have very narrow policy objectives (e.g.,support entitlement programs for retirees, zonemore off-shore space for oil drilling, oppose gay marriage). Sub-stantial resources would be wasted if organized interestspursued separate candidates corresponding to each indi-vidual groups agenda. This logic was expressed by LeeSaunders, president of the American Federation of State,County and Municipal Employees, in a recent statementon the need for labor unions to align with other support-ive interests (e.g., the NAACP; Trottman 2013): Laborcant do it alone. Our density dictates that weve got tohave partners. To avoid this waste, policy demanders inpursuit of separate agendas that are compatible, or at leastnot contradictory, agree to pool resources in support ofcandidates who will represent overlapping agendas. Thiscombination of agendas becomes the party platform, andthe separate interest communities become the coordi-nated base of support and activism for the party.

An archetypal example of the loose connections thatform among issue-oriented groups in partisan interestnetworks is provided by the U.S. Senate election con-tribution activities of three PACs in the 2012 cycle: theGun Owners of America (Gun PAC), the Association ofOil Pipe Lines (Oil PAC), and the Republican NationalCoalition for Life (Abortion PAC). These three groupsare firmly aligned with central issues in the RepublicanPartys policy platform, but they span the economic andcultural issue dimensions.2 The Gun PAC contributed to11 Senate candidates in 2012. The candidate to whomthey contributed the most was Ted Cruz (R-TX). TedCruz was the second largest recipient of contributionsfrom the Oil PAC, which contributed the most to LisaMurkowski (R-AK). The Abortion PAC supported eightSenate candidates, five of whom were also supported bythe Gun PAC. In this example, the Gun PAC is directlyconnected to both the Oil PAC and the Abortion PAC,which are indirectly connected through the Gun PAC.The Abortion PAC indirectly helped the Oil PAC by free-ing up resources for the Gun PAC to invest in Ted Cruz,

1Cohen et al. (2008) andBawnet al. (2012) donot discuss networks,but the theory they elaborate clearly implicates the network conceptand inspired us to approach the study of parties in this manner.

2The data for this example were gleaned from opensecrets.org.

which in turn helped the Oil PAC spread resources to LisaMurkowski and other potential supporters of pipelineconstruction. This example highlights the loose networkof cross-supports that emerges from decentralized andoverlapping backing of candidates for Congress.

A common collective action motivation underliesparty formation in both the candidate-centered and EPNtheories of parties. That is, parties arise because thereare several political actors in pursuit of narrow policyobjectives, who will rarely achieve success if they go italone. The party forms as a collective action solution inwhich each actor achieves more than would be possiblein solitary pursuit, in exchange for supporting the goalsof other party members. Despite pursuing somewhat dis-connected policies, political actors choose to create a longcoalition that strives to stay together across time to in-crease the likelihood that eachwill achieve their particulargoals. The two theories diverge when it comes to (1) theactorswhorequire a collective action solutionand(2)whobenefits from party activity. In the candidate-centeredtheory, policy makers form a coalition (i.e., party) inorder to achieve policy objectives that appeal directly totheir constituents and therefore secure reelection (Aldrich1995). In the EPN theory, active special interests benefitfrom coordinating and standing behind candidates in theform of a party, and thus they secure benefits for theirgroup members by electing politicians loyal to the inte-grated party platform (Bawn et al. 2012).

Extended Party Networks andChallenger Success

Previous literature on challenger success has focusedlargely on whether a candidate has held elective office.Politically experienced challengers more frequently self-select into races in which they will serve as an appealingalternative to the incumbent (Carson et al. 2012; Lazarus2008). The literature clearly demonstrates the role of can-didate self-perception in choosing to run (e.g., Maiseland Stone 1997). We build upon this work with theoryregarding the impact of organized interest perception andmobilization in the context of the EPN.

The main challenge in achieving successful coordi-nation among groups in the EPN is the identificationof candidates for office who (1) have strong electoralprospects and (2) will deliver on the shared party agenda(Bawn et al. 2012). Bawn et al. (2012) highlight that theparties cannot simply commit resources to candidatesin the hopes of convincing them to adhere to a partyplatform, since there is a principal-agent problem in


which the partys constituent groups cannot effectivelymonitor legislators. This is why the party network needsto focus its energies on bolstering the prospects ofcandidates who would enter office with a priori credibleand agreeable policy stances (Cohen et al. 2008). Fromthis characterization of EPN activity, we deduce that EPNsupport sends a strong signal regarding a candidatesinterest in and capacity to deliver on the party agenda.Because different communities of interests (e.g., guncontrol advocates and environmentalist groups) convergeon shared, compromise candidates, the credibility signalsafforded by group support are concentrated on singlecandidates. This helps challengers overcome the hurdle(Bernhardt and Ingberman 1985) of convincing abroader, nationally focused partisan constituency thatthey will take appealing policy stances once in office.

The first boost to the candidacy will come from thereputations of these groups. The fact that a coalition con-verges on a candidate sends a strong signal to attentivevoters. These voters may have ties with such groups andcomprise a significant portion of the electorate, partic-ularly in primaries and low-turnout general elections.Research shows that issue groups often contact membersdirectly to endorse favored candidates, and many broad-cast theirpreferences to thewiderpublic. Such issue-basedadvocacy or endorsements serve as important heuristicsto attentive voters (Lau and Redlawsk 2001) and mayeven help low-information voters make decisions (Lupia1994), particularly when interest groups provide contex-tual information in campaigns to help voters understandthe ideological or partisan implications of group support(Arceneaux and Kolodny 2009).

The potency of the signal sent by EPN support for acandidate lies in the power of multiple consistent signalsto discriminate the underlying preferences of a candidate.Since the ideology of interest groups can be inferred basedon the candidates they support (Bonica2013),whenmanylike-minded groups in a policy domain back a candidate(e.g., Sierra Club, League of Conservation Voters, andNational Resources Defense Council support the samecandidate), this providesmultiplicative certainty in a can-didates policy preferences. Suppose there is some proba-bility p that a group interested in supporting a candidatewith position a component of the party agendasupports a candidate who actually does not prefer posi-tion , and a probability q > p that a group supports acandidate who actually does prefer position . Let therebeN groups that prefer , and k of them support the can-didate. As k increases, the probability that the supportedcandidate prefers position also increases.3 Specifically,

3Note that a similar result could be reached by considering theposterior variance of a continuous ideal point.

FIGURE 1 Posterior Probability That aCandidate Prefers Given theNumber of Groups PreferringThat Contribute to the Candidate

Note: To parameterize these curves, the prior probability thata candidate prefers is set at 0.5. The probability that a groupsupports a candidate given that the candidate prefers (i.e.,q) is set at an equal-spaced sequence of 20 values between0.55 and 0.99, with darker lines representing higher q. Givenq, p is set to 1 q.

applying Bayes rule, P(|k) = P(k|)()/(P(k|)()+ P(k|not)(1 ())), where P(k|) and P(k|not) arethe binomial distributions parameterized with q and p,respectively. The terms() and(not) represent priorbeliefs regarding the prevalence of candidates who pre-fer position . Thus, the number of like-minded groupssupporting a candidate plays a key role in discerning acandidates underlying preferences. Figure 1 illustratesthis relationship for a hypothetical situation inwhichN=10. For each additional like-minded group that contributesto a candidate, the probability of that candidates prefer-ences aligning with the policies preferred by those groups(i.e.,) increases by between 5 and 50%, depending uponthe number of groups already supporting the candidateand the inherent accuracy of an individual group in vet-ting the policy positions of a candidate. In this way, sup-port by a coherent community of interests sends a clearpolicy signal about a candidate. This signal goes beyondthe resource and reputational effects of any one group.

Complementary to this dynamic is the formation ofa broader and looser connection of not necessarily like-minded communities, which converges around a singlecandidate who satisfies the multiple interests of the parti-san coalition without offending any particular group. Byintegrating a coalition of interest communities, the EPNdraws together the support of otherwise disconnected


constituencies, raising the scale of candidate backing fromthe interest community to the political party.

The second class of benefits that arise fromEPN integration derives from intentional coordina-tion among interests. Interest groups provide electoralresourcesbeyond campaign contributions and groupendorsementsin coordination with other groups in thecoalition that boost candidate prospects (Herrnson 2011;Herrnson, Deering, andWilcox 2012). These include col-lective efforts tomobilize voters (Magleby 2010), air cam-paign ads attacking the opposing candidate (Franz 2010),and offer timely expertise on campaign matters (De Witt1980; Herrnson 2011).

We are not arguing that EPN support is all that driveselectoral outcomesmany voters will directly evaluatecandidates and make their own decisions. However, asBawn et al. (2012, 57) note, Some voters who care noth-ing about the interests of the various groups are nonethe-less attracted to their parties because of the values, suchas social order or equality, that they perceive in theirprogram. EPN support signals that a challenger crediblyrepresents the partys platform or significant componentsthereof and is likely to deliver on those policy preferencesif elected. Candidates backed by the EPN have strong cre-dentials on one or more elements of the party platformand will give offense to no one [in the party] (Cohenet al. 2008, 83). With this imprimatur, the chosen candi-date is the beneficiary of the reputation, resources, andexpertise of members of the long coalition. Given lowattentiveness of many voters and the first-past-the-postrules of American elections, the signaling and (noncon-tribution) resources of such coalitions will affect electoraloutcomes, beyond candidate spending.

Empirical Analysis

We focus on the electoral predictions regarding EPNintegration of challengers. Since incumbency offers ac-cess to party and other resources, we do not attemptto differentiate between EPN support and the incum-bency advantage (Abramowitz, Alexander, and Gun-ning 2006; Fenno 1978; Levitt and Wolfram 1997; May-hew 1974). Rather, we study challengers and considerwhether the conception and analysis of extended partynetworks can build upon our understanding of challengersuccess.

There are two fundamental tasks before us in ourempirical analysis: (1) measurement of whether a chal-lenger is integrated into a partisan network of organizedsupporters and (2) estimation of the effect of EPN sup-

port on electoral outcomes. In the analysis that follows,we show that the network analytic methodology referredto as community detection (Newman 2006) is very wellsuited to identifying groups of highly interconnected con-tributors and candidates. We then show that strong in-tegration into a party network community substantiallyincreases the likelihood of a challengers success.

Contribution Network Data

Data on political action committee (PAC) and formalparty committee contributions to all candidates for theU.S. House of Representatives and data on all House elec-tion outcomes from the 19942010 cycles form the basesof our empirical analysis.4 From here on, we include theformal party committees in our broad references to PACs.We also gather data on additional district and candidateattributes up to the 2008 elections.5

In order to identify candidates backed by EPNs, weseek to operationalize our conceptual understanding ofthe EPN. As noted above, we understand a party networkto be a collection of organized interests that concentratetheir support efforts around a core group of candidates.Before detailing our approach to identifying these coali-tions of interests, we must define the network that wewill analyze. We construct a bipartite network. A bi-partite network is one in which there are two typesof nodes (i.e., actors), and ties can only connect twonodes of different types (i.e., there are no intra-typeties; Newman 2010). The two types of nodes in the net-works we construct are PACs and candidates for the U.S.House. The ties are weighted by the amount contributedup to July 1 of the election year. We measure the net-work early in the election cycle to minimize the risk thatour inferences are driven by PACs giving to sure-thingchallengers.6

The network degree distributions in the bipartitenetworks we construct (Newman 2010) are given in

4Contribution data come from opensecrets.org. We focus on thistime period becausewe had significant data quality concerns, basedon our own checks, for the data predating 1994. Specifically, thecandidate type (e.g., incumbent, challenger) is riddledwith error inthe1992data.Weget around this issue in theWoonandPope (2008)replication presented below, as we use their challenger/incumbentclassification variable.

5We use data from Jacobson (2009), which covers the 19942008election cycles.

6Our inferences are robust to measuring the network at any pointin the latter half of the election year. In the first two quarters ofthe election year, there are not enough networked challengers toprovide a feasible sample size.


FIGURE 2 PAC and Candidate Cumulative Degree Distributions, 19942010

Note: Political action committee (PAC) and candidate cumulative degree distributions are shown in terms of totalamounts (in dollars) represented by ties and the total number of ties. The darker the line, the more recent the electionyear. The solid lines depict distributions for incumbents, and the dashed lines depict distributions for challengers. Notethat the x-axis is on the log scale in the amount plots, such that an increment of 1 represents an order of magnitude(i.e., a tenfold) increase.

Figure 2.7 The plots therein give the empirical cumula-tive distributions of the total amounts given by each PACover all candidates, the total amounts received by eachcandidate over all PACs, the total number of candidatesto whom each PAC contributes, and the total number ofPACs that contribute to each candidate. Twomain storiesemerge from these distributions. First, there is stark in-equality in the contributions received by incumbents andchallengers. Over the entire period, the median amountreceived by incumbents is approximately 100 times themedian amount received by challengers. This is a com-mon finding, and it has led researchers to conclude that

7Degree distributions are the distributions over vertices (i.e., PACsand candidates) of the total connections to which those verticesare incident (e.g., the distribution of the number of friends peoplehave, over people in a friendship network).

successful challengers owe their success to forces outsideof systematic organized party support (Herrnson 1995;Jacobson 2009). Second, there is substantial inequalityacross PACs and candidates. The best-funded incumbentsand challengers receive 10 times their median counter-parts, respectively. The most active PACs contribute 100to 1,000 times as much as their median counterparts. Re-garding the second column of Figure 2, it is rare for achallenger to attract a large number of contributors, asthe majority of challengers are supported by a handfulof PACs. Moreover, most PACs support just a few candi-dates, indicating that overlap among PACs should be aninformative signal.

We can explore the basic bivariate relationship be-tween interest network integration and challenger suc-cess through the graphics in Figure 3. The plots depict


FIGURE 3 Contribution Network Visualizations

Note: Networks depict PACnodes (invisible) connected to candidate nodes, which arecolored based on party and incumbency status. Coloration differentiates four groups:Democratic incumbents (royal blue), Democratic nonincumbents (light blue), Re-publican incumbents (red), Republican nonincumbents (orange). Nodes are placedbased on the algorithm of Fruchterman and Reingold (1991), with edges weightedby the amount contributed.

the structure of the network for three cycles (i.e., 2006,2008, and 2010). Ties are anchored in the positions as-signed to PACs in the node placement algorithm, but inthe interest of clarity, PACs are not drawn as nodes inthe network. Coloration indicates partisanship and in-cumbency status. The first column of graphics depicts allcandidates, and the second column displays only those

who win election.8 Figure 3 provides initial evidence thatinterest network integration is associated with challengersuccess. In all three cycles, nearly all of thenonincumbents

8In these plots, candidates appearing close to each other receivesimilar contribution amounts from a similar set of contributors.We use the R package iGraph to produce these figures.


who are victorious are closer to the core of the networkthan are nonincumbents who lose. Though compellingvisual evidence, this is not sufficient to identify an effectof EPN support. For that, we need to control for otherexplanations of challenger success and extract an explicitmeasure of EPN support from the contribution network.

Community Detection as Party NetworkExtension Detection

In the previous main section and Figure 1, we pre-sented a theoretical claim thatwhenmultiple like-mindedgroups converge upon a candidate, a clear signal is es-tablished regarding the ideological position representedby the candidate. To test this hypothesis, we must (1)empirically identify collectionsof like-mindedgroupsand(2) identify the candidates on whom they converge. Wedraw upon the network analytic method termed com-munity detection to operationalize extended party net-works of organized interests and the candidates theysupport.

Community detection is the process of identifyinggroups of nodes, within which ties occur with greater fre-quency and intensity than they do across communities(Newman 2006). In our case, we have two node types:PACs and candidates. The only ties in our data are thosefrom PACs to candidates. Thus, community detectionwithin the context of our data constitutes the identifi-cation of groups containing both PACs and candidates,within which the PACs give more to the candidates inthat group than they do to other candidates, and thecandidates in that group receive more from the PACs inthat group than they do from other PACs. An illustrativepicture of a bipartite network with three communities isgiven in Figure 4. There are still cross-community ties,but ties within communities occur with much greaterintensity.

We characterize each PAC as drawing donations forcandidates in a given community from its community-specific Poisson distribution.9 Thus, if there are k com-munities and m PACs, we infer k m Poisson distribu-tions. We permit the number of communities (k) to varybecause we expect the network of groups supporting can-didates to be characterized by several communities thatare densely connected within (i.e., focused on a coherent

9We adopt a soft clustering approach, which means nodes are as-signed to each community with some probability. Soft cluster-ing methods are straightforward to express within a probabilisticframework as mixture distributions (Imai and Tingley 2012). Assuch, we can use the Bayesian information criterion (BIC) to de-termine the number of communities in the network (Fraley andRaftery 1998).

FIGURE 4 Illustration of Communities in anArtificial Bipartite Network

Note: Brown circles represent one node type and navy squaresanother (e.g., PACs and candidates). All edges connect one circleand one square. The nodes in each community overlay a circle.

policy agenda) and loosely connected across (i.e., pursu-ing alternative aspects of a partisan agenda). The amountcontributed from PAC i to candidate j, denoted yi j , ismodeled as a mixture of Poisson distributions. A mixturedistribution is appropriate because, as central to the partycompromise underpinning EPN theory, we expect eachcandidate will present a platform that appeals to over-lapping interest communities. A total of k Poisson rateparameters () are estimated for each PAC, and k com-munity membership probabilities () are estimated foreach candidate. The rate parameters can be interpretedas the intensity of an interest communitys support for acandidate, and the community membership probabilitiesrepresent the cross-community ties offered by candidates.The probability of yi j is thus expressed as

P (yi j ) =k

t=1j t p(yi j,i t),

where p() is the Poisson probability mass function.In each election cycle, we estimate the model with k

from 5 to 50 and select the k that results in the lowestBIC.10 For the purpose of the discrete analyses we per-form below, we assign each candidate to the community

10It might seem appropriate to begin at k = 2, simply capturingthe two political parties. However, the algorithm fits quite poorlyand exhibits convergence problems with k < 5. This is because,


FIGURE 5 Community Visualizations

Note: Each diamond corresponds to a community. Height is proportional to median contributions to a candidate in thecommunity, and width is proportional to the number of candidates in the community. Axes depict the partisanship andincumbency characteristics of the candidates in the community.

of which he or she is most likely to be a member. Thisfacilitates the identification of candidates who are not in-tegrated into the EPN. For each election, this results ina partition of the set of the candidates into communitiesthat contain a mixture of candidates and PACs. Thesememberships and attributes of these communities are

at minimum, there are five distinct connection patterns we see inthe data: (1) Some candidates (i.e., duck soup challengers) receivemeager or disparate PAC support, (2) some (i.e., strong Republicanchallengers) receive primary support from conservative ideologi-cal groups, (3) some (i.e., strong Democratic challengers) receiveprimary support from liberal ideological groups, (4) some (i.e., Re-publican incumbents) receive primary support from right-leaningaccess-oriented donors, and (5) some (i.e., Democratic incum-bents) receive primary support from left-leaning access-orienteddonors.

used to determine whether candidates are integrated intoEPNs.

The communities we identify, in four of the electioncycles, are illustrated in Figure 5. The regularity that weseek to illuminate with this figure is the community thatappears as a nearly flat line in the lower half of each plot.Based on the shape and position of this cluster, it can beseen that (1) the median member is very poorly funded,(2) themajority are nonincumbents, (3) there is little par-tisan bias to this community, and (4) it is by far the largestcommunity. In truth, this cluster might be better labeleda noncommunity, capturing those candidates and con-tributors who cannot be classified into tight, coherentnetworks. This is evidenced through the lack of a parti-san bias in this community, the large membership, and


FIGURE 6 Community Partisan andIncumbency Balance, 19942010

Note: Each point corresponds to a community identified in one ofthe election cycles. The y-axis gives the percentage of candidatesin one party in the community, and the x-axis gives the percentageof candidates in the community who are incumbents.

the paltry level of support. Though it might be possibleto derive more nuanced measures of networked support,we see the classification of a candidate into this wide, flatcluster as a clear, simple, and stark indication of a lackof substantial support from any networked communityof interests. In our empirical analysis going forward, weclassify a challenger as having EPN support if he or sheis not assigned to the largest and most weakly fundedcommunity (i.e., not in the wide, flat cluster). In thesupporting information, we show that this wide and flatcluster appears in each cycle. The other clusters reflectcommunities arising from highly dense PAC-candidatelinkages. And most importantly, the communities with alarge nonincumbent membership are highly partisan.

We have proposed community detection as anapproach to identifying the integration of candidatesinto the EPNs. We have yet to show that the communitiesinto which challengers are integrated are, indeed,partisan. It is conceivable, for instance, that bipartisancommunities exist with pro-business challengers fromboth parties who receive support from business interests.However, Figure 6 clearly illustrates that, insofar asthese communities of PACs are supporting challengers,they are doing so on a partisan basis. We plot theaggregate partisanship of candidates in each communityagainst the percent of community members who areincumbents. Over all election cycles, nearly every com-munity that contains any nonincumbents is decidedlypartisan. Over the nine election cycles, only a few smallcommunities emerge that are less than 90% in one partyand contain any nonincumbents. This supports ourclaim that the community detection algorithm reliably

FIGURE 7 Month-to-Month Rate of Change inNetworked Challenger Classificationover the Election Year

Note: Plot gives the proportion of networked/non-networked clas-sification of challengers that changed from the previous to the cur-rent month. Plot depicts the 19942008 congressional elections.Darker lines represent more recent election cycles.

locates challengers in partisan networks of interestgroups.

Another concern we want to directly address empir-ically is the possibility that we are measuring instancesin which groups are piling on to challengers with a highlikelihood of success in a last-minute effort to gain access.Though we can never perfectly discern a groups motives,we can assess the aggregate dynamics in the campaignunder the assumption that the likelihood of challengersuccess will be revealed as the campaign progresses. Fig-ure 7 depicts the rate of change in the networked chal-lenger classification throughout the election year. This isthe percentage of challengers for whom their networkedchallenger status is not the same as it was in the previ-ous month, where challenger status is 0 or 1 dependingon EPN support classification. The final spike in change,which is the last-minute shuffle toward challengers win-ning the race, occurs at some point between August andNovember. This is why we measure network integrationon July 1.

Electoral Success Analysis

We utilize two separate analytical approaches, applied totwo related but distinct data sets, to separate out the ef-fect of EPN integration from other known predictors ofsuccess in U.S. House elections. Our first analysis uti-lizes the matching framework to examine whether we seethe effects of EPN integration when we compare races in


which the EPN- and non-EPN-integrated challengers areapproximately the same on variables known to be strongpredictors of success inU.S.House races. The secondanal-ysis constitutes a replication and extension of a regressionmodel of partisan vote margins presented by Woon andPope (2008). Both analyses support the hypothesis thatEPN integration contributes to the success of challengersin U.S. House elections.

Matching-Based Inference. Matching is an alternativeor complementarymethodology for taking account of theeffects of potential confounding variables. The purposeof matching is to derive balanced treatment and controlgroups (Sekhon 2011; i.e., the treatment).11 When treat-ment and control groups are balanced, they are compa-rable on all potential confounding variables. Using theintegration of a challenger into a party network commu-nity as the treatment, we identify treatment and controlgroups that differ only trivially on variables that have beenfound to predict congressional election outcomes.We usea mixture of exact and carefully tuned coarsened exactone-to-one matching (Iacus, King, and Porro 2012). Inexact matching, the control case selected for each treatedcase must have exactly the same values on potential con-founding variables as the treated case. Coarsened exactmatching (CEM) is a process by which matches for eachtreated case are selected to be within rounding error (i.e.,within a bin) of the treated case.

The unit of analysis is the race, and the data span the1994 to 2008 election cycles. We exactly match treatmentand control cases on election cycle, incumbent party, andpolitical experience.12 Matching on these variables con-trols for the macropolitical context and the traditionalmeasure of challenger quality, respectively. Coarsened ex-act matching is used to match races based on incum-bent vote share in the prior election, challenger spend-ing, and incumbent spending. These variables accountfor the conventional measures of the competitiveness ofthe district and resources, respectively. We start with abin size of one standard deviation in each variable anditeratively reduce the bin size used for the CEM by in-crementing the denominator of the standard deviation(i.e., se/1, se/2, . . ., se/k) until hypothesis tests appliedto the noncoarsened treatment and control group valuesindicate that there is no significant difference between

11Though the terminology of causal inference and the potentialoutcomes framework is commonly used in the development ofmatching methods, we should be clear that matching is by nomeans a case of design-based causal inference.

12Exactly matching on election cycle and incumbent party controlsfor a litany of partisan and economic contextual factors (e.g., partyof the president) that have been found to affect elections.

TABLE 1 Balance check for continuous variablesmatched with coarsened exactmatching (CEM).

Challenger Incumbent IncumbentSpending Spending Vote Share (t-1)

ATTMean (treated) 1796270.48 2660867.38 54.63Mean (control) 1836609.71 2401184.62 54.70KS test (p) 0.96 0.16 0.75t-test (p) 0.82 0.22 0.70

ATEMean (treated) 1794847.61 2724234.73 57.60Mean (control) 1743907.41 2623581.18 57.59KS test (p) 0.39 0.10 0.41t-test (p) 0.65 0.65 0.95

ATCMean (treated) 1700922.49 2426617.67 55.22Mean (control) 1456979.00 2273322.29 55.38KS test (p) 0.52 0.33 0.78t-test (p) 0.18 0.63 0.50

Notes: The test statistic rows report the two-sided p-values asso-ciated with the test of the hypothesis that there is no differencebetween the treated and controlled samples after filtering withCEM. One- to-one CEM for estimating ATT, ATE and ATC re-sults in matched samples of sizes 21, 26 and 22 (i.e., 42, 52 and 44observations), respectively

treatment and control groups. Table 1 shows that ourtreatment and control groups are well balanced, in thatthere are no statistically significant differences betweentreatment and control observations in the quantitativevariables.

Figure 8 gives our estimates of treatment effects. Weestimate the overall average treatment effect (ATE), theATE among control units (ATC), and the ATE among thetreated units (ATT; Morgan and Winship 2007).13 TheATT and ATC constitute estimates of howmuch the partynetwork integration increased the probability of victoryamong the treated units and how much it would haveincreased the probability of victory among the controlunits, respectively. The ATE constitutes an estimate of thedifference in the rate of victory between treated and con-trol units, discarding units that are not comparable alongthe control measures. The ATC is noticeably larger thanATE and ATT, which indicates the need for our matchingapproach in estimating ATEthe main effect of interest.If treatmentwere truly unrelated to the confounding vari-ables, then in expectation ATE = ATT = ATC (Konisky

13The R package Matching (Sekhon 2011) is used to estimate theeffects and their standard errors.


FIGURE 8 Effects of Interest CommunityIntegration on the Probability ofChallenger Victory in U.S. HouseElections, 19942008

Note: Our matching procedure for estimating ATT, ATE, and ATCresults in matched samples of sizes 21, 26, and 22 (i.e., 42, 52, and44 observations), respectively.

and Reenock 2012). Focusing on ATE, we find that partynetwork integration increases the chance of challengersuccess by approximately 12 percentage points, an effectthat is statistically significant at the 0.05 level (two-tailed).This effect is substantively significant considering that achallengers nave chanceof success is, innearly every elec-tion cycle, below 10% (Friedman and Holden 2009).14

Regression-Based Inference. Regression models consti-tute the predominant approach to studying the factorsthat influence congressional election results (see, e.g.,Abramowitz, Alexander, and Gunning 2006; Alexander2005; Canes-Wrone, Brady, and Cogan 2002; Carson etal. 2012; Jones 2010; Woon and Pope 2008). In this sec-tion, we extend a regression model fromWoon and Pope(2008), in which the dependent variable is the vote sharewon by the Democratic Party candidate in U.S. Houseelections, to include an indicator that measures whetherthere is an EPN-supported challenger in the race. Thereare two primary benefits of combining regression- andmatching-based inference. First, we can assess whetherour conclusion about the effect of EPN support is depen-dent upon the use of matching. Second, we are able toincorporate many more potential confounding variablesin a regression model than can be accommodated in thematching analysis.

14Our conclusions are robust to the use of logistic regression withthe data from this section. Logit results are presented in the sup-porting information.

TABLE 2 Independent variables fromWoon andPope (2008)

Variable Description

incMID Midpoint between incumbent Ideology andchallengers party mean

incDHA Heterogeneity of the party ideological signalincGAP Ideological gap between incumbent and

challengers partyPresidential

VoteProportion in the district voting for

Democratic presidentDemocratic

IncumbentIndicator of Democratic incumbent

Dem. QualityChallenger

Indicator of Democratic challenger who hasheld elective office

Rep. QualityChallenger

Indicator of Republican challenger who hasheld elective office

South South region indicatorMidterm Midterm electionDem. Pres Indicator of Democratic president

Woon and Pope (WP; 2008) examine the relation-ship between the aggregate ideological characteristics ofpolitical parties regarding the behavior of partisans inCongress and the results of individual House elections.Broadly speaking, they argue that party labels serve abranding function for candidates in congressional elec-tions, and that the behavior of partymembers inCongressdetermines ideological informationcommunicatedby theparty brands. In order to evaluate the empirical accuracyof their theory, WP study U.S. House election outcomesbetween 1952 and 2000. They estimate a series of hier-archical regression models in which the dependent vari-able is the vote share of the Democratic candidate in adistrict. They draw upon the literature to include sev-eral control variables and specify a number of additionalindependent variables that operationalize concepts de-rived from their theory of party branding. The variablesincluded in their models are described in Table 2. Theinterested reader should see the original article for in-depth discussion regarding hypotheses related to thesevariables.

Our data on campaign finance networks overlapwith WPs data in the election years between 1990 and2000. We utilize the subset of WPs data that falls withinthis range, which includes 1,939 races in total.15 Themodel includes election-level random effects, which are

15Uncontested seats are excluded from the sample.


TABLE 3 Replication ofWoon and Pope (2008); Table 2, Model 1 (i.e., All)

No PACDummies PACDummies

Variable Coefficient (Std. Err.) Coefficient (Std. Err.)

incMID 0.112 (0.018) 0.050 (0.018)incDHA 7.176 (0.921) 6.785 (1.641)incGAP (incDH A< 0) 0.563 (0.059) 0.569 (0.108)incGAP (incDH A> 0) 0.189 (0.031) 0.189 (0.058)Presidential Vote 0.518 (0.018) 0.462 (0.020)Democratic Incumbent 0.726 (0.103) 0.740 (0.186)Dem. Quality Challenger 0.024 (0.006) 0.023 (0.006)Rep. Quality Challenger 0.039 (0.005) 0.023 (0.005)South 0.002 (0.004) 0.003 (0.004)Midterm Dem. Pres 0.023 (0.004) 0.015 (0.007)Networked Rep. Challenger 0.081 (0.005) 0.063 (0.006)Networked Dem. Challenger (0.006) 0.080 0.066 (0.007)Intercept 0.779 (0.072) 0.817 (0.131)R2-within 0.872 0.931R2-between 0.672 0.990R2-overall 0.871 0.931N 1,939 1,939

Notes:Unit of observation is the congressional district. Sample covers the 1990-2000 elections. Dependent variable is the Democratic Partyvote share in the district. Linear regression coefficients from model with election random effects are reported. All effects are statisticallysignificant at the 0.05 level (two-tailed) except for the South region indicator. The AC Dummiesodel includes 530 PAC/election yearpartyindicator variables

estimated by feasible generalized least squares.16 To testthe effect of EPN support, we include two additionalindependent variablesNetworked Rep. Challenger andNetworked Dem. Challengerwhich are indicators ofwhether there is a Republican challenger in the race withEPN support and whether there is a Democratic chal-lenger in the race with EPN support, respectively. Weexpect that having an EPN-supported Republican chal-lenger in the race will reduce Democratic vote share andthat having an EPN-supported Democratic challenger inthe race will increase Democratic vote share. In one of themodels, we also include a set of election year and party-specific PAC dummies that indicate the support of highlyinfluential PACs. We do this to account for the benefitsof individual group support that go beyond campaigncontributions.17 The results are given in Table 3.

16WP present several similar models of Democratic vote share. Wepresent just one replication. We replicate the model from Table 2,since this one fits the data best. However, our conclusions regardingthe effect of networked challenger do not change if we use one ofthe other specifications presented by WP.

17We adopt a three-stage approach to incorporating influential PACdummies. A single PAC dummy indicates that group X gave tothe Republican/Democratic candidate in election year Y. They are

The results provide strong support for our hypothe-sis that EPN integration improves the electoral prospectsof challengers. The presence of a challenger in the racewith networked support statistically significantly reducesthe expected incumbent vote share by approximately 8percentage points, an effect that is nearly identical forRepublican and Democratic challengers. To put the mag-nitude of this effect in perspective, the conventional mea-sure of challenger qualitythe indicator of whether achallenger has held elective officeresults in a 24 per-centage point reduction in the expected incumbent voteshare. This analysis illustrates that our result is robustto the choice between matching and regression to draw

PAC/election year/party-specific indicators. There are 26,138 po-tential PAC effects we could include in our model. We do not haveenough observations to identify that many effects. We use a com-bination of forward and backward search to arrive at our final setof PAC indicators. In the first stage, we omit any indicators that arenot statistically significant in a simple linear regression in whichDemocratic vote share is the dependent variable. This leaves 9.794potential indicators. In the next stage, we use backward search withthe lassoa formof penalized regression that forces the coefficientsof variables that do not contribute substantially to the predictiveperformance of the model (Tibshirani 1996)to further trim theset of indicator variables. We use tenfold cross-validation to tunethe lasso (Friedman, Hastie, and Tibshirani 2010).


inferences. Despite the fact that the sample size is only20% of that appearing in the original study, except for theSouth regional indicator, the effects of all of the variablesare statistically significant and in the same direction as inthe original study.

Discussion

We demonstrate that the EPN is active and appears in-fluential in congressional elections. In support of previ-ous theoretical work about the role of interest groups aspolicy demanders that shape the party, our analysis pro-vides evidence of coordinated efforts by contingents of theEPN to support and elect challengers of their choice. Incontrast toprevious analyses that focusedon relationshipsamong PACs (Dreiling and Darves 2011; Grossman andDominguez 2009), our community detection algorithmwas able to isolate unique subsets of PACs that convergedon distinctive subsets of challengers to finance their elec-tions with contributions and independent expenditures.We also provide evidencethe first of its kind with alarge sample of electionsthat such backing elevates thechances of electoral success for selected challengers.

It is instructive to unpack the groups that comprisethe communities that we use to classify candidates as be-ing integrated into the EPN. If these communities arethought to represent EPNs, then their top supporters (1)should be clearly on the same side of the partisan di-vide and (2) represent a diverse mix of specific interests.Table 4 lists the top 10 contributors to the communi-ties that supported the most challengers (i.e., the greatestnumber of challengers were classified into their ranksby the community detection), broken down by the par-tisanship of candidates in the communities for electionyears 20042010.On theDemocratic side,we observe thatthe formal party organization, the Democratic Congres-sional Campaign Committee (DCCC), sits atop the listeach year. Right below the DCCC are interest groups thatare often associated strongly with the Democratic Partycoalition (Grossman 2012), including labor unions, triallawyers associations, environmental groups, andwomensorganizations. On the Republican side, we observe anti-tax organizations, the National Rifle Association, pro-lifegroups, and trade associations. In support of EPN the-ory, our study shows that these partisan and ideologicalgroups coordinate their efforts to support targeted can-didates in ways that potentially shape the outcome of therace.

Previous accounts document two primary waysthat partisan groups coordinate efforts: those driven by

formal party committees and those that arise throughloosely affiliated ideological groups. First, the formalparty committees like theDCCCandNationalRepublicanCongressional Committee orchestrate partisan strat-egy with PACs (OConnor 2012), some of which haveleaders sitting on advisory boards of the party commit-tee (Kolodny and Dwyre 1998). On the Republican side,party leaders with strong policy preferences have creatednew organizations dedicated to recruiting like-mindedchallengers, providing them with seed money, and en-couraging interest groups to support them (Peters 1999).A recent example is the Young Guns PAC started in2007 by Republican Party leaders Eric Cantor, Paul Ryan,and Kevin McCarthy to recruit and support conservativecandidates for Congress (Burns 2010). A second coordi-nating strategy involves meetings among interest groupsto devise common campaign strategies. Campaign fi-nance law prevents the party committees from coordi-nating with interest groups that use soft money. For thisreason, partisans form umbrella organizations that helpcoordinate in the absence of a formal party organization.In 2004,Democratic partisans used AmericaComingTo-gether to convenemeetings of labor, environmental, andwomens groups to coordinate voter mobilization efforts(Skinner 2005). In the 2012 elections, a diverse collectionof conservative organizations took its lead from a promi-nent group called American Crossroads with the broadelectoral goal of helping the Republican Party take fullcontrol of Congress (Confessore 2011). These coordina-tion efforts have been documented in previous elections(Magleby, Monson, and Patterson 2007).

These developments in congressional campaign ac-tivity provide evidence that the kind of coordination andsignaling among partisan elites in selecting presidentialcandidates described by Cohen et al. (2008) also takesplace in legislative elections. EPN theory portrays theparty as a conglomerate of nonconflicting and cooper-ating interests who select and support candidates theycan trust to pursue the policy objectives of the party. Thisimplies that the integration of a candidate into the EPNis a signal that they are credible and appealing on partisanpolicies. Such signaling should pay electoral dividendswith a broader constituency of partisans. Moreover, thepresence of a community of like-minded PAC contribu-tors may imply a broader electoral effort by the coalitionto campaign collectively for the candidate through directmobilization of voters and campaign advertising. Similarto presidential elections (Cohen et al. 2008), participantsin the EPNhave discovered ways to coordinate, but acrossthe many candidates and campaigns that constitute con-gressional elections.


TABLE 4 Top Ten donors to the community containing the highest number of challengers for eachparty

Republican Democrat Republican Democrat2004 2006

NRCC DCCC NRCC DCCCNatl. Assn of Realtors EMILYs List Club for Growth AFSCMENRA Machinists/Aerospace

Workers Un.RNC Moveon.org

Am. Medical Assn Intl Brotherhood of ElectricalWorkers

Natl. Assn of Realtors EMILYs List

Am.s for a Rep. Majority Am. Federation of Teachers Natl. Right to Life NEANatl. Assn of Realtors Assn of Trial Lawyers of

AmericaEvery Rep. is Crucial PAC SEIU

Help Americas Leaders United Food & CommercialWorkers Un.

Rely on Your Beliefs Dem. Executive Cmte ofFlorida

Together for Our Majority NEA Freedom Project AFL-CIOKeep Our Majority PAC AmeriPAC Keep Our Majority PAC Dem. Party of NCRely on Your Beliefs PAC to the Future NRA NEA

2008 2010NRCC DCCC NRCC DCCCRNC AFSCME NRA Am. Federation of TeachersNRA SEIU Am.s for Tax Reform SEIURep. Campaign Cmte of NM Defenders of Wildlife Rep. Party of Michigan Credit Un. Natl. AssnClub for Growth Natl. Assn of Realtors Natl. Rep. Trust PAC Women Vote!Credit Un. Natl. Assn EMILYs List Club for Growth Intl Brotherhood of Electrical

WorkersAm. Medical Assn AmeriPAC Revere America AFSCMEFreedom Project Intl Brotherhood of Electrical

WorkersFreedom Project Am. Assn for Justice

Natl. Auto Dealers Assn Am. Assn for Justice Arizona Rep. Party NEAEvery Rep. is Crucial PAC Our Common Values PAC Natl. Fedn of Independent

BusinessAmeriPAC

Conclusion

We addressed two basic questions. First, whoin termsof both supporters and candidatesconstitutes the po-litical party via the extended party network, and second,how are electoral outcomes shaped by the dynamics ofEPN support? We built upon emerging theories aboutthe organizational form of political parties and exploiteda new algorithmof community detection to reveal uniquepatterns of behavior among different groups that embodythe conceptual definition of an EPN. Our study confirmsprior accounts regarding the existence of partisan net-works, but it moves beyond prior work in two ways.First, we integrate both interest groups and candidatesinto communities using a bipartite network represen-tation. This method enables us to identify the core of

the party network and illustrate the tight links betweenparty resources and selected candidates in advancing elec-toral goals. Second,we demonstrate the stark difference inelectoral prospects for challengers within and outside theEPN. Challengers supported by densely interconnectedpartisan communities have a greater likelihood of win-ning compared to those with similar campaign resourcesand political backgrounds, but without EPN backing.

We posit two related mechanisms that account forthe improved success among EPN-supported challengers.First, a key challenge for nonincumbents is to surmountthe ambiguity that surrounds their candidacy. Giventhat candidate emergence in congressional electionstends to be highly entrepreneurial (Fowler and McClure1989; Herrnson 2011), candidates must win an uphillbattle to present themselves in an ideologically clear and


compelling manner. When a challenger is backed by acollection of like-minded interest groups, a clear signalregarding the policy positions represented by the chal-lenger is sent to the broader party network and attentivemembers of the electorate. Second, many of these par-tisan networks arise from and engage in intentionallycoordinated actions in support of candidates. We assumethat EPN-supported candidates benefit from these coor-dinated support efforts. The relative magnitude of thesetwo mechanisms needs to be explored in future research,but it is clear that EPN support plays a major role in ac-counting for challenger success in congressional elections.

The empirical findings from this analysis haveimportant implications for theories about contemporaryparties. By observing a unique set of groups thatcoordinate actions to help targeted challengers, we haverevealed exactly the kind of electoral activity that scholarsattribute to strong parties (see, e.g., Schattschneider1942). The patterns we find suggest that parties comprisecontending policy demanders in the electoral process(Bawn et al. 2012). Our findings also present a challengeto theories positing that officeholders in the legislatureshape the party to advance their electoral and policy goals(Aldrich 1995; Cox and McCubbins 1993). If interestgroups with strong policy agendas shape who winsoffice, then officeholders might have far less discretionabout the direction of party policy than previouslytheorized (Masket 2009). Our findings also speak towhy candidates often fail to move toward the medianvoter and instead take greater policy risks by adoptingmore extreme positions (Fiorina, Abrams, and Pope2005). An approach that draws on networked theoriesof parties helps explain why party elites are convergingon an ideological point that is more extreme than voterspositions (Fiorina, Abrams, and Pope 2005).

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Supporting Information

Additional Supporting Information may be found in theonline version of this article at the publishers website:

Figure S1: The analysis presented in this figure demon-strates that the high-membership, low-funding commu-nity appears in every election cycle in our data.Figure S2:The analysis presented in this figure shows thatthe inferences drawn in the matching analysis are robustto the use of logistic regression on the full, unmatcheddataset.Figure S3: The analysis presented in this figure showsthat the inferences drawn from matching are robust toiteratively excluding each of the variables on which wematched.TableS1.The analysis presented in this table examines thedegree of association between EPN support and a varietyof challenger background variables.

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