The Effect of Party Networks on Primary Elections October 2, 2020

The Effect of Party Networks on Primary Elections*

Shawn Patterson, Jr.†

October 2, 2020

Abstract

Who controls nominations? Group-centered theories of parties predict that networked interests will compete in primaries to nominate candidates faithful to their agenda. Pre- vious attempts to test the electoral implications of this view have confronted the difficulties of observing group support directly. In this paper, I present a new measure – Existing Network Density – that detects group support indirectly through patterns in campaign contributions. Specifically, I measure the density of a candidate’s donor network in the previous election to infer which candidate’s have the support of durable, coordinated groups. I then estimate the influence of group support on a candidate’s likelihood of winning an open-seat House primary between 1982 and 2014. I find that candidates with group support perform substantially better after controlling for individual campaign resources. These findings suggest that while parties may not formally choose candidates, the coalition of interests comprising these parties can influence who wins the nomination.

*I would like to thank Kathleen Bawn and John Zaller for advice and guidance throughout this project. I would also like to thank Jennifer Victor and Vanderbilt’s CSDI reading group for particularly helpful feedback on a previous draft. †Assistant Professor, Southern Oregon University. [email protected] 1 Introduction

Pennsylvania Congresswoman Allyson Schwartz began laying the groundwork to challenge incumbent Re- publican Governor Tom Corbett soon after winning re-election in 2012. By mid-2013, the field of candidates seeking the Democratic nomination to her now-open 13th district had narrowed to four contenders: Valarie

Arkoosh, a health care advocate and president of the progressive National Physicians Alliance; State Repre- sentative Brendan Boyle, the “union candidate” from the largely blue-collar area of Northeast Philadelphia;

State Senator Daylin Leach, a “liberal lion” known for his “boisterous” advocacy of liberal social issues; and former Congresswoman Marjorie Margolies, whose campaign centered on her House tenure and relationship with the Clintons (see Cahn 2014). Given Margolies’ early polling and strong fundraising, she was considered the early favorite for the nomination.

Despite her front-runner status and wide name recognition, Margolies was unable to carry that momentum over the finish line. Neither did Arkoosh nor Leach, the candidates with the greatest fundraising andPAC contributions, win the nomination. Behind in the polls, out-raised and out-spent by his opponents, Boyle won the Democratic primary with 41% of the vote to runner-up Margolies’ 27%. The heavy Democratic lean of the district would comfortably carry Boyle into Congress come November.

What allowed Boyle to overcome Margolies’ numerous advantages to become the Democratic nominee?

The overwhelming support of organized labor. This is not to suggest that the other candidates lacked group support. Arkoosh received endorsements from many medical organizations, including the American Med- ical Association, and received the majority of her campaign contributions from people working in medical fields. Leach was endorsed by many liberal leaning interest groups, such as the Progressive Change Cam- paign Committee. Groups like MoveOn.org also helped Leach raise tens of thousands of dollars from their network of activists. Margolies, while politically inactive in recent years, was an early success of EMILY’s List and maintained connections to its members and donors, not to mention her relationship with the Clintons.

But none of Boyle’s competitors were able to match the scope and organization of labor’s support. Unions representing the electrical workers, carpenters, pipefitters, among others, issued endorsements, made donations, sent mailers, and directed independent expenditures in a coordinated attempt to secure Boyle the nomination. Behind the scenes these unions worked to discourage other Philadelphia-based candidates from running in the primary and used labor’s financial leverage over the local party to secure an endorsement from the chair of the Philadelphia Democratic Party, Congressman Bob Brady (see Gibson 2013). This gave Boyle

2 a geographic monopoly in Philadelphia, home to half of the districts registered Democrats. In the lead up to primary day, the unions also organized hundreds of volunteers to knock on doors and make phone calls to mobilize Boyle’s supporters. While verifying these efforts in retrospect is difficult, on primary day exit poll surveyors reported seeing union members distributing campaign literature at all of the selected polling locations (DeMora et al. 2015). In another analysis, I find that these efforts increased turnout by approximately 3% in Philadelphia.1 In sum, a network of Philadelphia trade unions marshaled a number of campaign resources and field management efforts to help nominate their preferred candidate.

In this paper, I argue that group support is a crucial asset for candidates seeking their party’s nomination.

Beyond the campaign resources these groups can provide, their efforts can shape the field of candidates in favor of their preferred candidate and signal credibility to primary voters. These benefits are often beyond the reach of “free agent” candidates who lack organized support. In this light, Boyle’s victory was not simply a product of having more endorsements or a larger field operation, but the cumulative result of a more coordinated network of supporters leveraging all of their political influence on his behalf.

Systematically observing the presence of group support, however, is challenging. Visible signals of group support, like endorsements, are difficult to observe after the primary, and the behind the scenes pressures that elites use to shape the field of candidates are often purposefully kept from the public eye. I propose a new measure – Existing Network Density (END) scores – to detect group support indirectly through patterns of coordination among campaign donors. Specifically, I use the density of a candidate’s donor network in the prior election cycle to determine which candidates have the support of durable, coordinated coalitions of supporters. I use this measure as a proxy for the quality, rather than quantity, of a candidate’s financial support and as a signal for which candidates will have access to the range of resources and field management efforts that come with organized group support.

I find that candidates with greater END scores are more likely to win open-seat primaries to the House of

Representatives between 1982 and 2014. Importantly, these effects are consistent over time and across party after controlling for traditional measures of candidate success, such as fundraising and prior elected experience. While the observational nature of this data opens the analysis to endogeneity concerns – particularly that these networks are bandwagoning behind the eventual nominee, not causing their electoral success –

I present qualitative and quantitative evidence from these contests suggesting that groups are indeed driving the relationship. In sum, I argue that organized networks of support hold significant influence over the

1For the full analysis, see the Appendix on my website www.shawnpattersonjr.com/myresearch/.

3 selection of a party’s nominees for Congress.

2 Party Coalitions in the Extended Network

As Schattschneider (1942) noted, “he who can make nominations is the owner of the party” – to understand the dynamics of nomination contests, we should look to the sources of influence within the political parties. Early studies of nominations focused on party organization’s formal ability to select the nominee

(Key 1949; Merriam 1923). More recent work has expanded the concept of political parties to include the network of interest groups, activists, consultants, donors, and local party organizations informally associated with the national party organizations. In this view, the “extended party network” (Koger et al. 2010) recruits candidates, helps them win office, and works with them to enact their overlapping political agendas (Bawn et al. 2012). They achieve these ends by marshaling scarce campaign resources on behalf of their preferred candidate (Hassell 2018) and signaling to voters which candidates will be faithful agents for the party (Desmarais et al. 2015).

Research on the extended party network (EPN) has made many contributions toward our understanding of the internal structure and composition of the major parties (see Koger et al. 2009; 2010; Skinner et al.

2012; Herrnson 2009). But given how central nominations play in the group-based theories of parties (see

Bawn et al. 2012; McCarty and Schickler 2018), the ability of these intra-party coalitions to influence electoral outcomes has received comparatively little attention. The question has been largely addressed tangentially

– addressing the ability of individual campaign resources to impact primary outcomes. Group support can provide candidates with campaign staff and advisers, canvassers and get-out-the-vote resources, independent expenditure campaigns, field-clearing efforts, fund-raising assistance, and numerous other benefits often beyond the reach of candidates without group support. In numerous studies, these resources have been found to individually aid candidates in their pursuit of nomination (see Cain 2013; Carnes 2018; Gerber and Green

2000; Hassell 2018). Moreover, in an environment lacking party cues and detailed media coverage, group endorsements are themselves an electoral benefit (Dominguez 2011; Kousser et al. 2015).

Given the wide variety of potential organizations active in a particular race, observing these sources of support systematically is often difficult. Consider the obstacles in measuring endorsements. Dominguez

(2011) uses a survey of candidates in open-seat congressional primaries to gather endorsement data that confirms by contacting campaigns directly. She found “that about half the time the candidate had received at least

4 some endorsements that were not listed on the Web page” (fn. 5). To compile accurate records of endorsements, Dominguez contacted the campaigns directly to verify the lists. It would be unfeasible for scholars to gather data on endorsements for a large sample of races in this labor-intensive manner. Approaching endorsements from the perspective of endorsers would be just as difficult. The universe of potential endorsers is unknown beforehand and observing their behavior in retrospect overlooks some efforts. As Hassell notes in his analysis of EMILY’s List endorsements, “groups tend to scrub their institutional memories of any candidates which they supported that lose the election” (2018, p. 75, fn. 32). All of which makes endorsements

– a resource candidates have the incentive to make public – hard to observe.

These limitations pale in comparison to the task of detecting the more private aspects of network support. With national parties hesitant to appear heavy-handed in local races, records of staff and consulting assistance are often buried in financial disclosures and not widely publicized (see Cain 2013; Robbins 2017).

The informal, behind-the-scene efforts to recruit and dissuade candidates are often purposefully kept outof public view (Bawn et al. 2014). And given how central these pressures are in the decision to run for office

(see Carnes 2018; Fox and Lawless 2010; Ocampo 2017), the difficulties in measuring it limits our ability to detect party network influence in primaries.

For example, recall the opening example from Pennsylvania. The field shaping efforts by the unions secured Boyle a geographic monopoly in Philadelphia. But even after months of research and over a dozen interviews, many of the details about where these pressures came from remain unclear. For example, another state legislator from Philadelphia with close ties to labor, Mark Cohen, filed to run in 13th district, but did not mount a serious campaign, quickly dropped out, and endorsed Boyle. These actions were seen by insiders as labor keeping the field clear for Boyle. However, no one with direct knowledge of this decision agreed to speak with me, highlighting the opaque nature of field management efforts.

The diversity of potential actors and resources in the party network provides another obstacle for direct observation. A group like EMILY’s List can provide campaign staffers for a candidate, but has no formal powers in the nomination process. The national parties, unwilling to appear heavy handed and step on the toes of local organizations, rarely endorse in primaries, but will direct contributions to their preferred candidate (Hassell 2016; 2018). Local activists may have the manpower to knock on doors and make phone calls, but rarely have the financial resources to pursue the five- and six-figure independent expenditure campaigns increasingly common in the post-Citizen United era. Lacking a theory of which group resources should be more important than others, researchers interested in the effect of group support generally would need data

5 on all possible sources of support.

The difficulties associated with observing group support directly have left studies to consider onlypres- idential nominations (e.g., Cohen et al. 2008), small samples (e.g., Masket 2009; Schwartz 1990), or general elections (e.g., Nyhan and Montgomery 2015). As Dominguez (2011) noted in her study of group endorsements in primaries, scholars require “other proxies” as current measures of group support are too “cumber- some to gather for large numbers of candidates.” For example, Desmarais et al. (2015) measure the effect of extended party network support on the success of challengers in congressional elections. To do so, they construct networks of candidates based on PAC contributions to their campaigns in the general election.

Candidates are connected within the network if they share a common PAC contributor. They find that challengers incorporated into the party network performed better in the general election than those candidates on the network’s periphery. However, given that most House contests are safe for one party and incumbents rarely lose reelection, EPN theory places central importance on nominating a candidate faithful to the network’s agenda. By focusing on general election outcomes, these authors overlook the party’s best opportunity to influence the composition of Congress.

Work directly considering primary outcomes, while informative, has either overlooked network organization or focused on the organization of only a small portion of the EPN. For example, Ocampo (2017) analyzes the impact of group support on the primary prospects of candidates in districts with sizable Latino populations. Using data on Latino and non-Latino candidates running in all open seats from 2004 to 2014 in congressional districts with a Latino population of at least 15 percent, she finds that a candidate’s share of

PAC and party contributions has a positive and significant impact on the likelihood of winning an open-seat primary. She concludes that the effect “group-level support has on electoral success is quite meaningful as it substantially increases the chances of Latino candidates winning their respective primary.” While this approach more directly tests EPN theories by considering primary outcomes, a measure of party support based off of individual PAC contributions cannot differentiate between many individual supporters and a coordinated effort. This leaves the empirical analysis more open to concerns of endogeneity – that PAC support is not driving electoral outcomes, but following the inevitable winners.

Hassell’s analysis of Senate primary elections (2016; 2018) deals with the issues of coordination and endogeneity directly. Using the number of donors that a primary candidate shares with their national party congressional fundraising committee2 as his measure of party support, he finds that party support predicts

2For example, the Democratic Congressional Campaign Committee.

6 which candidates will drop out of the contest before the primary and which candidates will go on to become the nominee. Because these donors give to both the candidates and the national party, Hassell argues that they are part of a coordinated party network. Through the use of a Granger causality test, he also demonstrates that early party support drives future fundraising success, but early fundraising successes do not drive later party support. This suggests that party supporters are not simply bandwagoning onto the campaigns of successful candidates, but are driving electoral outcomes. The empirical findings are supplemented with interviews from party operatives, further demonstrating that party support drives and does not follow electoral success.

While Hassell’s work is notable for its breadth – his measure allows him to consider all primary elections from 2004 through 2014 – by focusing on coordination with the national party, his analysis ignores the influence of the diverse array of actors in the party network. As Rauch and La Raja point out in their

Brookings report on primaries, interest groups and activists are “organizing in regions where party organizations lack resources or incentives to invest” (2017). While the national party focuses on the handful of competitive contests that will determine partisan control of Congress, groups within the party network are free to compete in the more numerous seats safe for their party. This could explain why Hassell’s analysis of

House primaries (2018) had its strongest effects in competitive districts. It is not that parties are only active in competitive House primaries, as Hassell’s results would imply, but the different components of the EPN are active in different electoral environments.

This fits with much of what we observed in the field. Beginning in the summer of 2013, my co-authors and I began interviewing candidates, interest groups, party leaders, campaign operatives, local activists, and voters in an open-ended attempt to understand party nominations to Congress.3 In our overview of the open- seat primaries of 2014, we found very little evidence of party-wide coordination or cooperation, and more often observed free-for-all contests among the many party factions within a district (Bawn et al. 2015). With the vast majority of seats safely in one party’s column, the national parties rarely engaged either explicitly or behind the scenes. Only in competitive seats does the national party have the incentive to participate in the primary. This does not mean that the larger party network does not influence primaries in safe-seats, only that the groups and interests active in these races differ from those in competitive seats. Considering that the majority of congressional seats are overwhelmingly safe for one party, these diverse actors should not be

3These interviews were conducted for a parallel project, Parties on the Ground, a forthcoming book authored with Kathleen Bawn, Knox Brown, Angela Ocampo, John Ray, and John Zaller.

7 overlooked as sources of organization and influence in nomination contests.

3 Measuring Network Support: Existing Network Density

To clearly test whether groups within the extended party network are able to influence primary outcomes, we need a measure that (1) can be gathered for a representative sample of primaries; (2) can measure the presence of group coordination on behalf of a candidate; (3) can detect the activity of actors beyond the formal party, and (4) is not simply responding to candidate viability. In this section, I propose an alternative measure – Existing Network Density – to measure the presence of network support indirectly through donation patterns.

Rather than measuring which candidates are provided with which campaign resources, I propose a network- based method to systematically measure the presence of group support. By detecting durable patterns of coordination among campaign contributions, I can infer the presence of organized support for a particular candidate. Specifically, I measure the density of a candidate’s network of campaign contributors in the election cycle prior to the primary. I refer to this value as Existing Network Density or a candidate’s END score.

I rely on the concept of network density in the construction of my measure (see Scott 2017). Density considers the overall connectedness of a network – the more connections between individuals, the denser the network. Rather than speaking to the nature of individual actors in the network, density refers to structure and organization of the entire network.

To measure the density of a network, I calculate the fraction of total possible connections that actually occur within the network. It can also be thought of as the probability that any two randomly selected actors in a network are connected. This statistic ranges from 0 to 1, from a network in which no actors are connected to one in which all actors are tied. Figure 1 displays variations in density across three networks of the same size. Each point represents an campaign donor, and pairs of donors are connected by a line if they contributed to the same candidate. Each contains ten donors, but vary in density. The density is provided in the lower corner of each panel. I use this to measure the connectedness of a set of donors, in particular, the donors who give to a candidate during the primary.

To construct each candidate’s donor network, I compile a list of donors who contribute to their campaign during the primary (t1). For those donors, I then find every donation they made in a primary during the

8 Figure 1: Illustration of Network Density

previous election cycle (t0). I omit general election contributions as all elements of the EPN are assumed to cooperate in the general election.4 Because state campaign finance data is not consistently available across time, I also only include federal contributions. I then generate a network where these donors are connected if they contributed to the same candidate in the prior primary cycle (t0). Finally, I calculate that network’s density. This value is a candidate’s Existing Network Density (END) score.

I construct these networks based on donor behavior from the previous cycle to suggest that these are durable networks of support and to set a higher threshold for detecting organization. In this light, it is a conservative estimate of group support. Organization that does not extend to multiple election cycles will not be detected. For example, Tea Party organizations – active players new to the 2010 election cycle – would not be detected for candidates until their 2012 activities. When considering only open-seats as this analysis does, this requirement also finds donors whose coordination is not driven by an individual candidate as their coordinated contributing existed prior to their candidacy for that office. This strengthens the assumption that this organization is distinct from an individual campaign.

Figure 2 provides a example demonstrating how this measure is calculated. This example is based on the candidates running in the opening example from Pennsylvania. Though the candidates are real and the general nature of the support networks are plausible, the donor networks presented are hypothetical. Real donor networks are vastly more complex and difficult to visualize (see Figure 3). The first row of the figure

4Cooperation in the general election is central to the group-centered theory presented by Bawn et al. (2012). Koger et al. (2010)’s observation of factional cooperation in general election information sharing confirms many of the expectations.

9 Figure 2: Illustrating Existing Network Density through Hypothetical Donor Networks

Dr. Dre Dr. Oz Boilermakers Carpenters PCCC MoveOn.Org Donna Shalala Steny Hoyer ● ● ● ● ● ● ● ●

Arkoosh Boyle Leach Margolies

AMA Dr. Phil IBEW Bricklayers PETA Sierra Club Bill Clinton Robert Rubin ● ● ● ● ● ● ● ● ● ● ● ●

● ● ● ● ● ● ● ● BrainPAC RheumPAC Kevin Boyle Pipefitters DFA Trial Lawyers Harold Ickes ExxonMobil PAC

● Bricklayers ● IBEW Trial Lawyers ● ● 10 Dr. Dre ● Bill Clinton

PETA Pipefitters Sierra Club BrainPAC Dr. Phil Dr. Oz Boilermakers ● ● ● Steny Hoyer ● ● ● ● MoveOn.org Robert Rubin AMA ● ● ● ● Donna Shalala Kevin Boyle RheumPAC ● ExxonMobil PAC ● ● ● DFA ● Carpenters ● PCCC ● Harold Ickes ●

0.2 1 0.47 0.23 Note: This figure provides hypothetical donor networks for the real candidates in Pennsylvania’s 13th district in 2014. The first row shows stylized donations during the campaign, and the second row shows hypothetical behavior for those donors in the previous cycle. Donors are connected in 2012 if they both contributed to the same primary campaign in that cycle. The Existing Network Density for these example networks is reported beneath the prior networks. provides four stylized networks, where the candidates Arkoosh, Boyle, Leach, and Margolies each have six donors. Each hypothetical candidate receives contributions from individuals and groups associated with their base of support: the medical community for Arkoosh, organized labor for Boyle, liberal advocacy groups for

Leach, and the Clinton political network for Margolies.

The networks in the second row present imagined behavior for these donors during the 2012 primaries.

The density of these networks (and thus the candidate’s imagined END score) are printed below these networks. Arkoosh’s hypothetical existing network is the least dense of the candidates – the American Academy of Neurology (BrainPAC) and the American College of Rheumatology (RheumPAC) both contributed to the same candidate as the American Medical Association’s PAC, but no other actors made similar contributions.

Moreover, one of her contributors, the fictional Dr. Oz, did not make a contribution during the 2012 primaries, and is therefore omitted from the network.5 With only two ties out of a possible ten ties, Arkoosh has a relatively low END score of 0.2. In comparison, Boyle’s network demonstrates a high degree of intercon- nectivity – all of the imagined unions gave to the same candidates in 2012 – resulting in an existing network density of 1.

Do END scores detect group support? For initial evidence, let’s look at the actual networks from the Penn- sylvania example. Between the fall of 2013 and the summer of 2015, I conducted eighteen interviews with candidates, campaign staff, interest group leaders, and party officials with first-hand knowledge of thePA-13

Democratic primary. These interviews shed light on not only the key players in the primary, but on types of support made available to each candidate and the degree of organization present among those supporters. In general, these interviews confirmed each candidate’s base of support from the above stylized networks.

Boyle’s victory was largely attributed to labor’s superior organization and involvement in the primary. The trade unions frequently coordinate in Democratic primaries – the International Brotherhood of Electrical

Workers (IBEW) Local 98 is Pennsylvania’s largest political spender (Mathis 2014) – often engaging in large independent expenditure campaigns through the Building a Better PA super PAC (Otterbein 2015). Labor is also the largest contributor to state and local Democratic Party organizations in Pennsylvania, giving them leverage over the party organizations that often relied on them “to keep the lights on.” Those interviewed also referenced the union’s ability to “put bodies on the streets.” The IBEW’s extensive field operation put as many as 100 door knockers a day into the field leading up to the 2014 primary. In sum, the group supporting Boyle

5Omitting donors who do not participate, rather than counting them as unconnected, prevents the measure from penalizing ambitious candidates who are also successful fundraisers in their own right.

11 was a frequent, well-organized, and influential participant in Democratic primaries.

At the opposite end of the spectrum was Arkoosh’s support from the medical community. While more financially endowed than labor, the medical community is less politically organized and a more sporadic participant in Democratic politics. According to one campaign insider, receiving the AMA’s endorsement was important to Arkoosh’s campaign because as a liberal Democrat she “was not their usual candidate.” As an industry, medicine leans more toward the Republican party, with 60% of the American Medical Association’s

(AMPAC) endorsements going to Republican candidates in 2016 (AMPAC 2016). Individuals working in the medical field tend to be evenly divided by party (Bonica, Rosenthal, and Rothman 2014). While medical PACs ran independent expenditures on her behalf and helped contribute to Arkoosh’s fundraising dominance, they did not provide any of the boots-on-the-ground support that the unions provided Boyle. Nor were they able to influence the recruitment and dissuasion of Arkoosh’s competitors to help shape the field in her favor.

The groups supporting Leach and Margolies were seen as falling somewhere in between Arkoosh’s and

Boyle’s on the organizational continuum. The network of liberal advocacy groups supporting Leach’s campaigns were frequent participants in Democratic politics who often coordinated in primary campaigns. How- ever, like the medical community, they lacked the field operation that helped Boyle so greatly in Philadelphia and held little sway over other candidates’ decisions to run. Margolies, in comparison, had numerous endorsements from big names in Democratic politics: Bill & Hillary Clinton, former Governor Ed Rendell,

House Minority Whip Steny Hoyer, and the majority of the party establishment in the Montgomery County half of the district. And while Bob Brady officially endorsed Boyle, he never “lifted a finger” for Boyle otherwise (Van Zuylen-Wood 2014). In fact, he sent his most trusted advisors to go advise Margolies field operation, which while paltry in compared to the unions, had more of a presence than either Arkoosh or Leach.

Are these variations in organization apparent in these candidate’s campaign contribution networks? Fig- ure 3 plots the actual networks for the four candidates’ sets of donors in 2012. Visualizing large, dense networks is difficult. The result is often described as a “hairball” where the dynamics of the network’s structure are hidden by closely overlapping points and an abundance of connections (see Nocaj, Ortmann, and Brandes

2014). To make these networks more intelligible, I increase the threshold needed to connect donors only in these visualizations. Donors are tied if they contributed to two of the same candidates in the previous cycle.

This creates dramatically sparser networks.6 However, in the regression analyses to follow, END scores are

6The rank ordering of END scores for the candidates in PA-13 remain whether the thresholds are set at 1, 2, or 3 donors. Foran example, see the Appendix on my website www.shawnpattersonjr.com/myresearch/.

12 calculated with the threshold of one donation.

The first row presents the complete network of donation patterns in the previous election. For each candidate, this appears as a small cluster surrounded by a crescent of individual points. The clusters are those contributors who are connected, where the crescent consists of those donors who made contributions in

2012, but did not contribute to at least 2 of the same candidates. To better visualize the structure of those donors who are connected, the second row zooms in on just the clusters. The densities of the whole networks and just the clusters of connected donors are provided below the network. For example, at the threshold of two previous donations, Arkoosh, Boyle, Leach, and Margolies have END scores of .04, .21, .07, and .14, respectively. The densities of these networks conform to expectation. Boyle’s network is 50% denser than runner-up Margolies. In fact, the rank ordering of the network’s densities matches the candidate’s eventual vote share.

One issue with density is its sensitivity to a network’s size. For example, there is likely a limit to the time and energy individuals can spend maintaining social relationships (Scott 2017) and therefore the maximum density of a social network usually decreases with the size of the network. In fact, Mayhew and Levinger

(1978) use models of random choice to suggest that for large social networks, the maximum observable density is likely closer to 0.5.

But unlike social relations, donation patterns are indirect connections. 7 Individual donors are not in- teracting with one another, but are tied if they shared a common behavior. If every contributor to Boyle’s campaign had also given to Joe Donnelly’s 2012 Senate campaign, that would be sufficient to connect the entire network. The amount of time and coordination needed to maintain a large, dense donor network is far less than the organizational requirements for similar levels of density among a traditional social network.

Therefore, even though Boyle’s network is smaller than his competitors, the density of those network are still comparable. Moreover, empirically we do not observe a negative relationship between network size and END scores.

Boyle’s contribution network is denser and more organized than his opponents, but does he owe the structure of his network to the support of organized labor? Perhaps Boyle, as an elected official, was able to mobilize the same set of donors cycle after cycle in his campaign. Figure 4 compares the isolated networks of Arkoosh and Boyle from Figure 3, but the donors are identified based on whether they were affiliated with

7In network analysis, this is considered a bi-partitie, two-mode, or affiliation network – a network wherein the ties between actors are mediated by their actions toward another category of actors.

13 Figure 3: Existing Network Density in Pennsylvania’s 13th District

●● ●● ● ● ● ●●● ● ● ● ●● ●●● ●●● ● ● ● ● ● ● ● ● ● ● ● ● ● ●●● ●● ●● ●● ●●● ● ● ● ●●● ●●●● ●● ●●● ● ● ●● ● ●● ●● ●● ● ● ● ● ● ● ● ● ● ● ●●● ●●● ● ● ● ●● ● ●● ● ● ● ● ●●● ● ●●● ●● ● ● ●●●● ●●● ● ● ●● ● ● ● ● ● ● ● ● ●● ●●●● ●●●●●●●● ●●● ● ● ● ● ● ● ● ●● ● ● ● ● ●● ● ● ●● ●● ● ●● ●● ●●● ●●●●● ● ● ●●●● ● ● ● ●●● ● ●● ● ●● ●● ● ●●● ● ● ●● ● ● ●●●● ●●● ●● ● ● ● ●●●● ● ● ● ● ● ●● ●● ● ●● ● ●●● ●●● ● ● ●● ●● ● ● ● ● ● ● ● ● ● ● ● ●●●● ● ●●● ● ●●●● ● ●●●●● ●●● ● ● ● ● ● ● ●● ● ● ● ●● ● ●● ● ● ●●●●● ●● ● ● ●●● ● ● ● ● ● ● ● ● ●●● ● ● ●● ●● ● ● ●●●● ●● ●●●● ● ● ● ● ●●● ● ● ● ● ● ● ● ●● ● ●● ● ● ● ● ●●● ●● ● ● ● ●●● ● ● ● ● ●● ●●●● ● ● ●●● ● ●● ● ● ● ●● ●● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●●●● ● ● ● ●● ● ● ● ● ●● ● ● ● ● ●●● ● ●●● ● ● ● ● ●● ● ● ● ●●● ● ● ● ● ●● ● ●● ●● ● ● ● ● ● ● ● ● ● ●●●●●● ● ● ● ● ●● ●●● ● ●● ● ●● ● ● ●● ● ● ● ● ● ● ●●●● ● ● ● ● ● ● ●●●●● ●●● ●● ● ● ●● ● ●● ● ● ●●● ● ● ● ● ●● ● ●●●●●●●●●●● ● ● ● ● ●●●●●● ● ● ● ● ●● ● ● ● ●● ● ●● ●●● ● ● ● ● ● ●●●●●●●●●●● ●● ●● ● ● ●●●●●●●●●●●● ● ● ● ●●●●●● ● ● ●● ●●● ●●●●●●●●● ● ● ●●●●● ●●●●●●●●●● ● ● ●●● ●●●●●●●●●●●●●●●●● ●● ●●●●●●●●●● ● ● ● ●●● ●●●●●●●●●●●●●● ● ●●●●●●●●●●●●●●●●● ●● ●● ●●●●●●●●●●●●●●●●●●●● ● ● ●●●●●●●●●●●●●●●●● ● ●● ● ●●●●●●●●●●●●●●● ● ●●●●●●●●●●●●●●●●●●● ●● ●●●●●●●●●●●●●●●●● ● ● ●●●●●●●●●●●●●●●● ● ● ●● ●●●●●●●●●●●●●● ● ●●●●●●●●●●●●●●●●●● ●● ● ●●●●●●●●●●●●●●●●● ● ● ● ● ● ●●●●●●●●●●●●●●●●●●●● ●●● ●● ● ●●●●●●● ● ● ●●●●●●●●●●● ● ● ● ● ●●●●●●●●●●●● ●● ●●●●●●●●●●●●●●●●●●● ● ●●● ●●● ● ●●●●●● ●●●●● ●●●●●●●●●●●● ● ●●●●●●●●●●●●●● ●●●●● ●● ● ● ●●● ● ● ●●●● ● ● ●●●● ● ●●●● ● ● ●● ● ● ● ● ●● ●● ● ● ● ●● ● ● ● ●● ● ●●● ● ● ●●●● ● ● ● ● ● ●● ● ● ● ● ● ●● ● ● ●●●● ● ● ● ● ● ●● ● ● ● ● ● ●●● ●●● ● ●● ● ● ● ●● ● ●● ●● 0.04 ●●● 0.21 ● 0.07 ●● 0.14 ● 14

● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●●● ● ● ● ● ● ● ● ● ● ● ● ● ●●● ● ● ● ● ● ●●●●●●●●●● ● ● ●● ● ●●●●●●●●●●●●●●●●● ●●● ●●●●●●●●●●●●●●●●● ●● ● ● ●●●●●●●●●●●●●●●●●● ●● ● ● ● ● ● ●● ● ●●●●●●●●●●●●●●●●●●● ● ● ●● ● ● ● ● ●● ● ●●●●● ● ● ● ●●●●●● ●●●●●●●● ● ● ● ● ● ● ● ●● ●●● ●●●●●●●●●●●●●●●●●●● ● ● ●●●● ● ● ● ● ● ●●●● ● ● ●●● ●●● ●●● ● ● ●● ● ● ●● ● ● ●●● ● ●●●●● ● ●● ●● ●●●●●●●●●● ● ●● ● ● ●● ●● ● ●● ●●●●●●●●●●●●● ● ●●●● ●● ● ● ● ●●● ●● ● ● ● ● ● ●●● ● ● ● ●● ●●●●●●●●●●●●●●●●●● ● ●●● ● ● ●● ● ●●●●● ● ● ● ● ●● ● ● ● ● ●●●●●●●●●●●●●●●●●● ● ● ● ●● ●●●●●●●● ● ● ● ● ● ●●● ● ● ● ●●●●●●●●●●●●●●●●●●●●●● ● ● ●●●●●●●●●●●● ● ●● ●●● ● ● ● ●●●●●●●●●●●●●●●●● ●● ● ● ● ●●●●●●●●●●●● ●●●●●●●●●●●● ● ● ● ● ●●●●●●●●●●●●●●● ● ● ● ● ● ●●●●●●● ● ● ●●●●●●●●●●●●●● ● ● ●●●●●●●●●●●●●●● ● ● ●●●●●●●●●● ● ● ●●●●●●●●●●●●●●●●● ● ●●●●●●●●● ●● ● ● ● ●●●●●●●●●● ●●●●●●●●●●●●● ● ● ● ●●●●●●●●●●●●●●● ● ●● ●●●● ● ● ● ●●●●●●●●●●●●●●●●●●●●● ●● ●● ● ● ●●●● ● ●●●●● ● ●●●●●●●●●●●●●●●●●●●●● ● ●● ● ● ● ● ● ●● ● ● ●●●●●●●●●●●●●●●●●●●●● ●● ●●● ●●●● ● ● ● ●●●●●●●●●●●●●●●●●● ● ● ● ● ● ● ● ●● ● ●●●●●●●●●●● ● ● ● ● ● ● ●●● ●●●● ● ● ● ● ●● ●● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ●● ● ● ● ● ● ● ● ● 0.18 ● ● 0.39 ● ● 0.17 0.24 ●

Note: This figure provides the actual networks of Arkoosh, Boyle, Leach, and Margolies donors in 2012. The first row presents all donors – the crescent groups are donors who did not contribute to 2 similar candidates. The second row zooms in on the networked cluster to show more group structure. The network density measures are provided below each figure. each candidate’s primary base of support (medicine for Arkoosh, labor for Boyle). For individual donors, I code this based on their listed employer or employment from FEC records. Those who listed their occupation as nurse, doctor, physician, etc. were considered medical, while those who listed construction or a building trade were coded as labor. This is admittedly a conservative measure given the number who leave this field blank or report being retired. PAC contributors are categorized by industry by OpenSecrets (OpenSecrets

2018).

Comparing these two networks, we can see that the labor unions and those employed in unionized in- dustries are more central to the structure of Boyle’s network. To address centrality more formally, I consider the eigenvector centrality or eigencentrality8 of each donor in the network. The specific average eigencentrality for actors in and outside of this constituency are provided in the legends. For Arkoosh, the medical community is, on average, actually less central than other non-affiliated contributors, where for Boyle, unions and laborers are more central. This suggests that not only is Boyle’s network of supporters more coordinated

(a higher density), but organized labor is also a more central player in his network.

Finally, Figure 5 extends the behavior of Boyle’s network back to before he was first ran for the state legislature in 2004. This network of Philadelphia trade unions has consistently coordinated their contributions in primary elections and has been doing so since before Boyle began his political career. The combined evidence strongly suggests that Boyle’s support was more coordinated than his opponents, based firmly in organized labor, and durable beyond his own political career.

3.1 Summary

In this section, I described the difficulties associated with measuring group support. I next developed an alternative approach – Existing Network Density (END) scores – to infer group support indirectly from patterns of coordination in donor networks. I then applied it to a case where qualitative accounts provided clear expectations about groups support and organization. Boyle’s more organized network, structured firmly around organized labor, suggests that END scores do detect group support. In the next section, I estimate the effect of END scores on a candidates likelihood of winning an open-seat primary to systematically address the influence of group support.

8Eigenvector centrality, or eigencentrality, is one of many network centrality measures (Borgatti 2005). Eigencentrality is particularly useful for measuring the centrality of actors in dense networks. This measure is scaled to range from 0 to 1, where 0 would be an individual with no ties to the network, and 1 is the most centrally positioned actor. An actor with a high eigencentrality is influential in the network because they are connected to other actors who are themselves highly central.9

15 Figure 4: Val Arkoosh’s & Brendan Boyle’s Existing Networks

● Medical − 0.29 ● Union − 0.67 ● Other − 0.33 ● Other − 0.48 ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●●●● ● ● ● ●● ● ● ● ● ● ●●●●● ●●●● ● ● ● ● ● ● ●●●●● ● ●● ● ● ● ● ● ●● ● ●●● ● ●●●●●●● ● ●● ●●●●●●●●● ● ● ●● ● ● ● ●● ● ● ● ● ●●●● ●●●● ● ●● ● ●●● ● ● ● 16 ● ●● ●●●● ● ● ● ●● ● ●● ● ●● ● ● ● ● ● ● ● ●●●●● ● ●●●●●●●● ● ● ● ● ● ● ● ● ●● ●●● ●●●●●●●● ● ● ● ●●●●● ● ● ● ● ● ●●●●●● ●● ● ● ●● ● ●●●●● ● ● ● ● ●● ● ● ●●● ● ●●● ● ●●● ● ●●●● ●● ● ● ●●● ● ● ● ●●● ● ● ● ●● ● ●●●● ● ●●● ●●● ●●●●● ●●● ● ● ● ●● ● ●● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●●● ●●● ● ● ●● ● ● ● ●● ● ●● ● ● ● ● ● ● ● ●●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●

● ● ● ● Figure 5: Brendan Boyle’s Existing Networks 2002 – 2012

17 4 The Effect of Network Support

4.1 Model Specifications

My empirical analysis addresses the effect of END scores on the likelihood of winning a consequential open-seat primary. A consequential primary is one in which the presidential candidate of that party won at least 45% of the vote in the previous election in that district. I only consider consequential primaries because these are the races in which the winner has a plausible chance in the general election and are therefore worth contesting. An open-seat primary is one in which no incumbent is seeking the nomination. These races are

“where that action is” (Gaddie and Bullock 2000), with the majority of congresspeople entering through open seat contests. Open seats also eliminate the confounding influence of incumbency on electoral outcomes.

In only present analysis from races with no incumbent from either party, but the results are the same for primaries to challenge incumbents in competitive districts.

I estimate a series of logistic regressions considering the likelihood of winning a primary election controlling for a range of potentially confounding variables. In different specifications, I include measures of a candidate’s primary fundraising, number of donors, and prior elected experience. Candidate fundraising is one of the strongest predictors of candidate success in both primary and general elections for Congress

(Jacobson 1980). Having the support of a dense network of 1000 contributors is arguably more beneficial than a dense network of 10 contributors. Therefore, I interact network size and END scores in some model specifications. Party donors are those who contributed to one of the national party committees. I include the number of party donors to control for the influence of the national party in primaries and for comparison with previous work (see Hassell 2016; 2018). Candidate quality is a binary variable for whether a candidate has held previous office. While the data allows for a more granular levels of previous experience, more detailed measures are often found to explain little additional variation (Jacobson and Kernell 1981).

4.2 Data

Data for this analysis comes from four sources. Individual-level campaign contribution data comes from

Bonica’s (2015) Database on Ideology, Money in Politics, and Elections (DIME). This database consists of over

130 million political contributions made by individuals and organizations to elections between 1979 and

2014. Bonica uses entity resolution techniques to create unique identifiers for both committees and individual donors across election cycles. This allows me to bridge donation behavior between election cycles.

18 House primary election results between 1982 and 2010 come from Pettigrew, Owen, and Wanless (2014) database, and from the Federal Election Commission (2018) for 2012 and 2014. This data provides both the vote share received by each candidate in the race, but also whether or not a candidate was an incumbent seeking re-election. The vote share of that candidate’s co-partisan presidential candidate in the previous election comes from Pettigrew et al. (2014) and the Daily Kos’s Elections Archive (2012). These two variables are necessary for determining whether a seat is open or competitive for a particular party.

The data for prior elected experience also comes from Pettigrew et al. (2014) and was gathered byhand for 2012 and 2014. Model specifications controlling for candidate quality only consider these elections. Can- didate quality is a binary variable for whether a candidate has held previous office. While the data would allow us to make a more granular scale of quality given different levels of previous experience, more nuanced measures explain little additional variation (Jacobson and Kernell 1981).

To merge these different sources of data, I rely on a candidate’s FEC identification number. Pettigrew et al.’s data, however, do not provide this information. Therefore, I rely on a fuzzy matching approach to find the nearest match in FEC records based on the candidate’s name, state, district, and year. After verifying the fuzzy matches, a small number of candidates’ IDs were located by hand.10

4.3 END Score Overview

Figures 6 provides visual summaries of the END scores used in the empirical analysis to follow. The first panel provides the distributions of END scores for winning and losing candidates competing in open- seat primaries between 1982 and 2014. The distribution for winning candidates is roughly symmetric. The disproportionate number of observations at extremes of the distribution for losing candidates is driven by candidates with extremely small networks (n≈3), where the average network is significantly larger (n≈75).

The results from the analysis hold if these observations are omitted.

The second panel provides the average END score for these winners and losers in each election cycle.

Fitting the initial hypothesis, in all election cycles the average END score for a winning candidate is greater than candidates who lost. The average difference in END scores between winning and losing candidates in consequential open-seat primaries is approximately 0.1. A one standard deviation increase in END scores is approximately 0.2. To interpret the magnitude of the below regression models, I will refer to these differences as a moderate and large increase in existing network density, respectively. This plot also shows variation in

10A more detailed description of this process can be found in my online Appendix, §3.

19 END scores over time, with the average network being generally less dense over time. This is likely because the size of the networks has been increasing, depicted by the solid black line. To account for this trend, all models besides the bi-variate model include year fixed effects.

4.4 Results

The main results from the logistic regressions are presented in Table 1. In the first specification, (1),

I provide just the bi-variate relationship between existing network density and the likelihood of winning a consequential open-seat primary. In the bi-variate case, a moderate increase in END score increases the odds of winning a primary by 28%. A larger, one standard deviation increase in END scores increase the odds by 65%. The sizable, positive, and statistically significant coefficient supports the central hypothesis that organized group support benefits candidates at the ballot box.

In the second specification, (2), I include controls for the logged value of total fundraising in the primary and the total number of candidates competing in the primary, but the effect remains large and positive.

Fundraising is one of the strongest predictors of candidate success in primaries (Jacobson and Kernell 1981;

Hassell 2016; 2018) and the size of the field would perforce reduce the likelihood of winning in expectation.

Not surprisingly, increased fundraising performance was associated with increased likelihood of primary victory and the size of the field of candidates negatively affects the odds. The density of a candidate’s existing network, however, has an independent effect. Even the moderate increase boosts the odds of winning by

40%, and the more sizeable increase nearly doubles the likelihood (193%), controlling for these other factors.

In Hassell’s investigation of Senate (2016) and House (2018) primaries, he reports that the number of donors a candidate shares with their party’s national fundraising committees is positively associated with electoral success. He argues that this measures national party support for a candidate. I include this measure of party support in model (3). The size and influence of the effects are nearly identical to model(2).

While I can replicate a similar effect when Hassell’s measure is considered separately, the number of national party donors does not have an independent effect once END scores are included, further suggesting that the influence of END scores comes from party network support. This also fits with Hassell’s more qualified findings for general House primaries – his most robust effects are in Senate primaries and House primaries for competitive seats in the general election.

Having the support of a larger dense should be more beneficial to a candidate than a smaller dense network. In other words, the support of 10 coordinated donors signals less of an electoral benefit than the support

20 Figure 6: END Scores for Winning & Losing Candidates

2.5 Winners Losers 2.0

1.5 Density 1.0

0.5

0.0

0.0 0.2 0.4 0.6 0.8 1.0

END Score

0.7 150 ● Winners ● ● Losers ● 0.6 ● Size 125

● ●

0.5 ● ● ● 100 ● ● ● ● ● ● ● ● ● ● ● ● 75 0.4 ● ● ● ● ● ● ● ● ● Average END Score Average

● Network Size Average ● ● 50 0.3 ● ●

25 0.2

82 84 86 88 90 92 94 96 98 00 02 04 06 08 10 12 14

Year

21 Table 1: Existing Network Density’s Effect on Likelihood of Winning a Primary

Primary Win (1) (2) (3) (4) (5) ∗∗∗ ∗∗∗ ∗∗∗ ∗∗∗ ∗∗∗ END Score 2.503 3.307 3.124 2.384 3.220 (0.215) (0.306) (0.311) (0.343) (0.695)

∗∗∗ ∗∗∗ ∗∗∗ ∗∗∗ Logged Funds 1.067 0.977 0.846 0.802 (0.075) (0.084) (0.094) (0.123)

∗∗∗ ∗∗∗ ∗∗∗ ∗∗∗ Field Size −0.280 −0.270 −0.264 −0.238 (0.017) (0.018) (0.018) (0.027)

National Donors 0.011 0.004 0.002 (0.008) (0.008) (0.007)

∗ Network Size (N) −0.003 −0.001 (0.002) (0.002)

∗∗ END*N 0.015 0.008 (0.005) (0.006)

∗∗∗ Prior Office 0.751 (0.171)

∗∗∗ ∗∗∗ ∗∗∗ ∗∗∗ ∗∗∗ Intercept −1.596 −11.849 −10.869 −9.210 −10.853 (0.090) (0.904) (0.990) (1.070) (1.563)

Observations 2,437 2,437 2,437 2,437 1,146 pseudo-R2 0.082 0.448 0.456 0.464 0.499 Log Likelihood −1,526.635 −1,119.076 −1,108.006 −1,097.121 −1,097.121 Akaike Inf. Crit. 3,057.270 2,278.152 2,258.011 2,240.243 2,240.243 Year Fixed Effects X ✓ ✓ ✓ ✓ ∗ ∗∗ ∗∗∗ Note: p<0.05; p<0.01; p<0.001 Standard errors clustered by primary contest

22 of a 1000 coordinated donors. To test this additional implication, I interact the density of the network with the number of individuals in that network in the fourth specification (4). As anticipated, this interaction is positive and significant. If we hold the size of the network fixed at the average (72) a moderate increase in density would increase the odds of winning by similar amounts as model (2) (40% and 100%). But were two candidates supported by equally dense networks, but one network was twice that of the average, that candidate’s odds of winning would be 50% greater. What is more, the effect of network size controlling for density is negative. For candidates with END scores less than .2, the size of the network is negatively related to the odds of winning the primary. This is further evidence that group support is important – large, unorganized support is actually an electoral handicap.

In the final specification (5), I add a final control for whether or not the candidate had previous elected experience. As noted prior, the data on electoral history is only available after 2000, reducing the sample by more than half. Fitting with the literature on candidate quality, candidates with electoral experience are more likely to win open seat primaries. However, even with this additional control, the effect of END scores remains robust. A .1 and .2 increase in a candidate’s existing network density increases the odds of winning their primary by 46% and 113%, respectively. In sum, after controlling for measures of candidate quality and viability, the support of durable networks of supporters still has an substantively and statistically significanat impact on candidates’ primary prospects.

The average END scores for candidates has been decreasing over time (see Figure 6). Figure 7 address the potential for time trends and presents the results from model 2 estimated one election at a time. Across time there is a consistent positive effect which is statistically significant by conventional standards in 14of

17 election cycles. While there is a visible shift in the magnitude in cycles following 1998, all years have the effect from the full model (3.307) within their 95% confidence intervals.

Finally, it is worth considering whether the effects of network support is consistent across parties. The effect is positive and statistically significant for each party under all of the specifications from Table 1.The second panel in Figure 7 presents the predicted probabilities from model specification (5) estimated for each party individually. There are some differences between the Democrats and the Republicans, however, both parties generally benefit from more organized networks of support.

Together, the results of this section support the hypothesis that candidates in congressional primaries benefit from the support of dense, durable networks of support. Candidates with greater END scores are more likely to win the nomination in consequential open-seat primaries. These effects are present over time,

23 Figure 7: Likelihood of Winning an Open-Seat Primary by Year and Party

● p<=.05 9 ● p>.05; β>0 8 7 ● ● 6 ● Coefficients

β 5 ● ● ● 4 ● ● ● ● 3 ● ● ● ● ● ● 2 ● 1 Specification (2) 0 −1

82 84 86 88 90 92 94 96 98 00 02 04 06 08 10 12 14

Year

1.0 Republican Democrat

0.8

0.6

0.4

Probability of Primary Win 0.2

0.0

0.0 0.2 0.4 0.6 0.8 1.0

END score

24 across parties, and robust to controls for underlying candidate viability. One concern is that these dense networks are not causing their candidates to perform better in primaries, but are merely falling behind the eventual nominee. Next, I summarize evidence suggesting networks are driving this relationship.

5 Bandwagoning or Gatekeeping: Dealing with Endogeneity

Is the support of dense networks helping primary candidates succeed, or are more successful candidates attracting the support of dense networks? As gatekeepers to scarce campaign resources, I argue that party networks are able to shape primary outcomes in favor of their preferred candidates and not simply bandwagoning behind the inevitable winners. While a causal argument can be extended only so far with observational data, the following pieces of evidence suggest networks are driving this relationship.

First, recent studies have largely found donor motivations to be expressive rather than instrumental, re- flective of their political preferences (Barber 2016a; 2016b; Bonica 2018). This is not to suggest that donors are not strategic. They are responsive to competition (Hill and Huber 2017) and give more frequently in races with the potential to increase their party’s seat share (Boatright 2017). However, they “appear to give out of desire to support causes they believe in rather than extract material benefits from politicians” (Albert et al.

2018). Victor and Koger (2016) take a similar view of interest groups. They argue that campaign donations are better understood as “an investment in an ongoing relationship and an expression of common underlying characteristics between lobbyists and legislators,” and not an attempt to buy votes or access. This too suggests that interest group support is not motivated by bandwagoning. In his study of House (2018) and Senate

(2016) primaries, Hassell finds little evidence of bandwagoning by national party donors. Using a Granger causality model, he finds that early national party support drives later fundraising success, rather than following early frontrunners, suggesting that network support was actually causing increased perceptions of viability. In sum, most evidence suggests that primary donors do not exhibit bandwagoning tendencies and instead support those aligned with their goals.

These results conform to recent qualitative accounts of congressional primaries. In another project, my co-authors and I find that in open-seat congressional primaries “support from organized groups is critical,” but rather than coordinating behind one candidate “different groups support their own champions” (Bawn et al. 2015). Not only did groups regularly support candidates with whom they had established relationships, but were rarely able to bandwagon behind frontrunners had they been so motivated. With voters unable to rely

25 on party identification or media coverage of these races, political insiders reported that “many nominations remain free-for-alls until the day of the primary.”

Tomore systematically assess the potential of reverse causality, I explore the relationship between fundraising totals and Existing Network Density (END) scores. With public polling in congressional primaries ex- ceedingly rare, fundraising is best predictor of primary elections (Hassell 2016; Jacobson 1980). As shown in

Figure 8, while there is a slight relationship between END scores and fundraising, the relationship is negative.

In other words, the strongest predictor of candidate viability is actually negatively correlated with network support, all be the relationship statistically insignificant.

If the perceptions of candidate viability (as measured by fundraising) predict future support from dense networks, then these endogeneity concerns would be well founded – this would suggest that network support is bandwagoning behind the presumptive nominee. In comparison, if early support from dense networks predicted future fundraising, it would suggest that bandwagoning donors were following the lead of dense networks. Formally, network support can be said to Granger-cause candidate fundraising if previous values of network support predict future fundraising, but previous values of fundraising do not predict network support when both lagged values are considered (Woolridge 2012; see Hassell 2016).

To test this, I estimate Granger causality models to determine whether a candidate’s END score Granger- causes future fundraising success. To do so I calculate a candidate’s fundraising and network density at different stages in the primary. If bandwagoning were to occur, it would be most apparent toward the end of the primary when a frontrunner emerged. I therefore measure the total fundraising and Existing Network

Density scores based on donations made in the last three months (90 days) of the primary. I then generate lagged measures from donations made 90, 180, and 360 days before the primary.

Table 2 provides the results from the Granger causality tests. The dependent variables are the fundraising totals and END scores from donations made in the last three months of the primary. The three pairs of independent variables are fundraising totals and END scores from lagged donations. In each specification, lagged

END scores predict future END scores and lagged fundraising predicts future fundraising, but neither lagged values predict future values of the other variable. This result suggests that there is no significant relationship between a candidate’s fundraising and their END scores. To demonstrate that network support is causing electoral outcomes, early END scores would ideally predict future fundraising, yet they do not. However, early fundraising, and thus early perceived viability, do not increase a candidates network density. These results suggest that neither variable Granger causes the other, or that dense networks are not bandwagoning

26 Table 2: Granger Causality Tests of Fundraising and Network Density

Funds<90 END<90 Funds<90 END<90 Funds<90 END<90 (1) (2) (3) (4) (5) (6) ∗∗∗ END90 0.183 0.291 (0.160) (0.020)

∗∗∗ Funds90 0.569 0.003 (0.028) (0.004)

∗∗∗ END180 0.362 0.263 (0.200) (0.024)

∗∗∗ Funds180 0.437 0.005 (0.033) (0.004)

∗∗∗ END360 0.184 0.186 (0.333) (0.035)

∗∗∗ Funds360 0.184 0.006 (0.045) (0.005)

∗∗∗ ∗∗∗ ∗∗∗ ∗∗∗ ∗∗∗ ∗∗∗ Intercept 4.136 0.429 5.789 0.420 8.137 0.446 (0.322) (0.041) (0.396) (0.047) (0.688) (0.072)

Observations 1,606 1,534 1,014 968 382 369 R2 0.343 0.222 0.294 0.198 0.225 0.191 Adjusted R2 0.336 0.213 0.282 0.184 0.188 0.152 Year F.E.s ✓ ✓ ✓ ✓ ✓ ✓ ∗ ∗∗ ∗∗∗ Note: p<0.05; p<0.01; p<0.001

27 behind presumptive candidates.

One element of network support thus far overlooked is their ability to shape the field in their favor. Given the potential for more than two candidates in a primary, who runs is often as important as who does not. There is a wealth of qualitative information demonstrating the importance of party and interest groups pressures for recruiting and dissuading candidacies (see Masket 2009; Ocampo 2017; Schwartz 1990). How should network support influence a candidate’s decision to compete in their party’s primary? On the one hand, candidate’s with the support of an organized network are more likely to win, so they should be more likely run in the first place. On the other hand, candidate’s with the support of a network should also be more susceptible to the pressures of that network to step aside in favor of another candidate. Unlike the “freebooting political entrepreneur” (Jacobson 2009), whose “desire, skills, and resources...are the most important criteria separating serious candidates” (Herrnson 2011, p. 41), a candidate with network support should be more likely to move to the sidelines when pressured. Candidates with a network may be more likely to drop out today in order to do well in the future. As one party leader explained to Hassell, “when you run against the

‘anointed one’ all you end up doing is pissing off people you might need someday” (2016). In sum, network support likely has conditional effects on a candidate’s decision to run for the nomination.

To explore this, I estimate the effect of END scores on declared candidate’s decisions to drop out. Icon- sider every candidate who filed with the FEC to run for Congress as the universe of potential candidates. This is a conservative estimate of that universe, omitting all those who considered running but never filed the paperwork. Candidates are determined to have dropped out if after filing to run they did not receive any votes.

Admittedly this misses candidates who dropped out after the filing deadlines and considers those who were ejected from the ballot unwillingly as to have dropped out. However, these represent a very small number of the over 28,000 candidates who filed to run for Congress since 1982.

Figure 9 provides the average END scores and fundraising totals for those candidates who dropped out compared to those candidates who received fewer than 10%, between 10 and 20%, etc. of the vote in their primary. Candidates who ran unopposed (100%) are also presented. Together, these two plots suggest that candidates who drop out are on average in better electoral situations – they have denser networks of support and are better funded than those candidates who compete and perform poorly in the primary. This discontinuity implies that those candidates who drop out are not solely motivated to drop out by concerns of electoral viability. Perhaps these candidates are being dissuaded by party networks?

Table 3 presents the results of six logistic regressions estimating the likelihood of dropping out of a pri-

28 Table 3: Relationship between Dropping Out of Primary and Existing Network Density

Drop Out of Primary All Non-Incumbents Open Seats (All) (Low) (High) (All) (Low) (High) ∗ ∗ ∗∗ ∗ END Score −0.227 0.426 −0.579 −0.170 0.856 −0.353 (0.108) (0.181) (0.188) (0.211) (0.433) (0.321)

∗∗∗ ∗∗∗ ∗∗∗ ∗ ∗∗∗ ∗ Network Size (N) 0.003 0.147 0.003 0.001 0.198 0.001 (0.0004) (0.024) (0.0004) (0.0004) (0.050) (0.0004)

∗∗∗ ∗∗∗ ∗∗∗ ∗∗∗ ∗∗∗ ∗∗∗ Logged Funds −0.275 −0.323 −0.357 −0.492 −0.477 −0.544 29 (0.020) (0.048) (0.042) (0.038) (0.118) (0.069)

∗∗∗ ∗∗∗ ∗∗∗ ∗∗ END*N −0.014 −0.162 −0.012 −0.002 −0.249 −0.001 (0.001) (0.035) (0.001) (0.001) (0.087) (0.001)

∗∗∗ ∗∗∗ Constant 0.163 0.192 0.749 2.551 1.632 3.133 (0.255) (0.451) (0.537) (0.453) (1.052) (0.858)

Observations 16,653 2,952 13,702 3,687 608 3,079 Log Likelihood −4,457.860 −1,376.457 −3,017.007 −1,222.779 −300.754 −902.304 Akaike Inf. Crit. 8,957.719 2,794.914 6,076.013 2,487.557 643.508 1,846.608 Year Fixed Effects ✓ ✓ ✓ ✓ ✓ ✓ ∗ ∗∗ ∗∗∗ Note: p<0.05; p<0.01; p<0.001 Standard errors clustered by primary contest mary after filing candidacy statements with the FEC. Again, this is likely a very conservative estimate ofthe candidates who dropped out of the contest – ignoring those who considering running but never filed the paperwork. I present the likelihood of dropping out for all non-incumbent filers (even those challenging incumbents) in the first three columns, and restrict it to only those in open seats in the second three columns.

For each set, I estimate the effect for all candidates (All), those candidates in the lowest quintile of fundraising (Low, less than $20,000), and those the upper quintiles (High, more than $20,000). Breaking them into subcategories by fundraising allows us to consider the effect of network on low and high quality candidates.

The results fit our conditional expectations. Among all non-incumbent filers, the effect of greater network support decreases the likelihood of dropping out of a primary. But if we look at low fundraising candidates, increased network density increases the likelihood of dropping out. Those with political networks but under- performing campaigns know that future runs for office will need support from the network. Therefore, these candidates are more likely to acquiesce in the face of network pressures. In comparison, the successful, high fundraisers are much less likely to drop out (and eventually win). This points to the two-fold influence of these networks – they can clear the field of weaker candidates to benefit their preferred candidates. Similar results, all be them statistically insignificant or barely significant by conventional standards, can be found among the more restrictive set of open seat contests. All of these effects are independent of controls for overall fundraising.

5.1 Summary

This section presented evidence suggesting that networks of support were driving and not reacting to successful candidacies. Donors and interest groups do not demonstrate strong bandwagoning tendencies, and early signs of campaign viability do not drive increased support from dense networks. Moreover, network support appears to drive less viable candidates out of the pool of candidates. These results are not conclusive but do conform to the expectations from field observations.

6 Discussion

These findings call into question our textbook understanding of primary elections. Unlike general elections, primaries are difficult to predict because primary voters’ lack partisanship to influence their vote. And with little media coverage, voters are left with very few considerations to inform their primary voting deci-

30 sions. In fact, voters appear to know little about the policy positions of candidates competing in contested congressional primaries (DeMora et al. 2015) and lack sufficient information to make an ideological decision

(Ahler, Citrin, and Lenz 2015) particularly at lower levels of government (Hirano et al. 2015).

With this lack of information, many conclude that voters must “judge candidates based on their resume”

(Dominguez 2011). Jacobson (2009) describe candidates in primary elections as “freebooting political en- trepreneurs,” whose electoral fates are largely based on their own abilities. Herrnson (2011, pg. 41) similarly argues that “the desire, skills, and resources that candidates bring to the table in the electoral arena are the most important criteria separating serious candidates from those who have little chance of getting elected.”

The sizable effect of Existing Network Density on primary outcomes, however, suggests that organized groups within the extended party network hold significant influence over the nomination of candidates. Im- portantly, these effects are independent of campaign resources, as measured by total fundraising, and candidate quality, as measured by prior elected experiences. By serving as gatekeepers to scarce campaign resources, these groups can help their preferred candidate’s win nomination. And by leveraging their influence within the party network, these groups can recruit and dissuade particular candidates and shape the field in their favor. Yes, the political environment still rewards the talented politician. But the supply of ambitious candidates, while not infinite, far exceeds the number of seats in Congress. This provides parties and the networks of interest groups that constitute them with the ability to shape primary outcomes.

These results also support recent efforts to expand our conception of political parties. Were we to limitour scope to the formal institutions or even just the expanded set of actors coordinating with the national parties, we would overlook numerous sources of influence over an otherwise unstructured primary process. Viewing the influence of groups like EMILY’s List, labor unions, the Club for Growth, or the Chamber of Commerce in congressional primaries as separate from the political parties could provide the misleading impression that organizing forces are absent from these contests and that primaries are little more than “poorly designed lotteries” (Brady 1994), when in fact these groups play an important role in recruiting candidates, helping them win office, and affecting “the groups to be represented among the decision-making elites” (Crotty 1968, p. 260).

One could contest viewing these disparate groups as part of a single party. If these groups are in fact part of a larger party, then we should observe candidates with the support of these groups more solidly incorporated into their respective parties. To address this, I construct networks of all candidates seeking federal office each election cycle and connect these candidates if they share a common primary campaign contributor. These

31 networks are constructed only with campaign contributions from that cycle. By including incumbents this network approximates the structure of the party, with more central actors like party leaders near the center. I calculate the eigencentrality11 of each candidate in that network to estimate how well incorporated a candidate is into the political party. In the first column of Table 4, I estimate the effect of a candidate’s END score on their centrality within the full party network. Fitting with EPN theory, a denser network of support is associated with greater centrality in the full party after controlling for fundraising, prior elected experience, and the number of donors in their network. This suggests that the groups supporting candidates and helping them win nomination are part of a larger party network.

Table 4: Relationship between END Scores, Party Centrality, and Ideology

Party Centrality Ideology ∗ ∗ END Score 0.056 0.322 (0.024) (0.162)

Network Size (N) −0.0002 -0.0003 (0.0002) (0.0002)

∗∗∗ ∗∗ Logged Funds 0.024 -0.055 (0.004) (0.018)

∗∗∗ ∗∗∗ Prior Office 0.033 -0.156 (0.007) (0.028)

∗∗ ∗ END*N 0.001 0.001 (0.0004) (0.191)

∗∗∗ Intercept −0.215 1.408 (0.047) (0.191)

Observations 1,164 1,173 R2 0.527 0.149 Adjusted R2 0.522 0.140 Year Fixed Effects ✓ ✓ ∗ ∗∗ ∗∗∗ Note: p<0.05; p<0.01; p<0.001 Standard errors are clustered by primary contest.

What implications can we draw from a primary process so heavily influence by the activities of organized party networks? I believe that the end result is a ‘Congress of Champions’ – a legislature inhabited by agents

11See footnote 9.

32 of various groups “seeking to capture government for their particular goals” (Bawn et al. 2012). Rather than a Congress of increasingly liberal or conservative partisans, it is a diverse coalition of legislators representing

“networked interest groups with overlapping political agendas” (Desmarais et al. 2015).

If one accepts this view, it would change how we study legislative representation. The single-minded seeker of reelection would be beholden to not only the voters that formally elect them, but also to the coalition of interests that provide them with the resources and support necessary to initially win office. And if these groups are truly motivated by policy outcomes and not by electoral inevitability as suggested in §5, then we should observe group-supported candidates systematically adapting their legislative efforts – committee preferences, co-sponsorship patterns, lobbying relationships – to better serve as faithful agents.

While addressing this question is beyond the scope of this paper, the second column in Table 4 provides initial evidence that these networks are helping elect members more beholden to the party platform. I estimate the effect of END scores on the absolute value of the candidates ideology as measured by Bonica’s CFscores12

– ideological ideal point estimates based off of campaign contributions. By taking the absolute value, Iam essentially measuring the distance between a candidate and the median. I am not arguing that END scores cause candidates to be more ideologically extreme, only demonstrating that those candidates with greater network support tend to be more ideological, after controlling for candidate qualities. This further suggests that candidates with organized networks of support are not only more likely to win nomination, but are likely to behave differently once elected.

Beyond legislating, these findings have larger implications for our study of inequality. Schattschneider

(1960) characterized the normative shortcomings of a group-centered politics over half a century ago: “The flaw in the pluralist heaven is that the heavenly chorus sings with a strong upper-class accent.” The organization and resources necessary for these networks to influence the primary process perforce biases the universe of potential actors with political influence in favor of affluent, established interests. In our attempt to grapple with inequalities in representation, we need to more directly address which types of issues have the organized networks of support necessary to elect a champion. Consider the following examples:

12CFscores allow for direct distance comparisons of the ideal points of a wide range of political actors from state and federal politics derived from patterns of campaign contributions. This is one of the few measures that allows for the comparison of candidates who take office and those who do not. However, there is some debate over the validity of CFscores ability to measure ideology within party. These results should be seen as only initial suggestive evidence (see Bonica 2014, Bonica 2018, but also Tausanovitch and Warshaw 2017).

33 • Why do some districts with large Latino populations elect Latino members of Congress while others do not? Ocampo (2017) finds that networks of political support are critical to organizing support behind Latino candidates.

• Women make up over half of the population, yet are severely under-represented in Congress despite little evidence of an electoral gender penalty (Anastasopoulos 2016). What scholars have found, however, is that party efforts at recruitment are often biased against women (Fox and Lawless 2010; Niven 2006).

• Voters, if anything, express a preference for ‘blue collar’ candidates, yet few if any members of Congress come from working class backgrounds. Carnes (2018) finds that blue collar candidates are recruited less by party officials because they worry about their ability to raise the necessary financial resources.

In each of these situations, organized interests were shown to have influence over the electoral fates of traditionally under-represented populations. If we wish to understand why particular groups are under- represented in Congress, we need to better understand the mechanisms that carry candidates into office. And given historic levels of partisanship and minimal competition in general elections, primary elections provide a critical juncture where these constituencies are being filtered out of the political pipeline. Moreover, if our goal as a society is to have elected bodies both more demographically and substantively representative of the population, then we must turn greater attention to the decision-making processes of these political networks to understand why certain candidates are recruited, supported, and elected, while others are not. Only then can we explain which issues and interests will be championed in Congress.

34 Figure 8: Bi-Variate Relationship between END Scores & Total Fundraising

8886 ● ● ●

●

● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ●● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ●● ● ● ● ● ● ● ●● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ●● ● ●●● ● ●● ● ●● ● 1203 ● ● ● ●● ● ●● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ●● ●● ● ● ● ● ●●●● ● ● ● ● ● ● ● ● ●●● ● ● ●● ● ●● ● ●● ● ●● ● ● ● ● ● ● ● ● ●● ● ●● ● ● ●● ● ● ●● ● ● ● ●● ● ● ● ● ● ●● ● ● ●●● ● ● ● ● ● ● ● ● ● ● ● ●●● ● ● ● ● ●● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ●● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ●●● ● ● ●● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●●● ●● ● ● ● ● ● ● ● ●●●● ● ● ● ● ●● ● ● ● ● ●●● ● ●● ● ● ●●● ● ● ● ● ●● ●●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ●● ●●● ●●●● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ●● ● ●● ● ● ● ● ● ● ● ● ● ●● ● ●● ● ● ● ● ●●● ● ● ●● ● ● ●● ● ● ● ● ● ● ● ● ● ● ●●● ● ●● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ●● ●●●●● ●●● ● ● ● ●●● ●● ● ● ● ● ● ● ●●● ●● ●● ● ● ●● ●● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ●●●● ● ●●●●● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ●●● ●● ● ● ● ● ● ●● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ●●●●● ● ● ● ● ● ● ● ● ● ●● ● ●● ● ● ●● ● ●● ●● ●● ● ●● ● ●●●●●● ● ● ● ● ● ● ● ● ● ● ● ● ●●● ● ● ● ● ● ●● ●● ●● ●● ●● ●● ● ● ● ● ●● ● ● ● ● ● ●●● ●●●●●●● ● ● ● ●● ●● ●● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●●● ● ● ● ● ● ● ●● ●●● ● ● ● ●● ● ●●● ● ● ● ●●● ● ● ● ● ● ● ● ● ● ● ● ●● ● ●●● ● ●● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●●● ●● ● ● ● ● ● ● ● ● ●● ●● ● ● ●● ●● ● ● ● ●●● ● ● ● ● ●●● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ●●● ●● ● ●●● ●● ● ●● ●● ● ●● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●●● ● ● ● ● ● ● ● ● ●● ●●● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●●● ● ● ●● ● ● ●● ● ●● ●● ● ●● ● ● ● ● ● ● ● ●● ● ● ● ●● ●● ● ●●● ● ● ● ● ● ● ● ●● ●● ●●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●●● ● ● ● ● ● ● ● ● ● ●●● ● ●● ●● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ●● ● ● ●● ●●● ● ● ●● ● ● ● ● 163 ● ● ● ● ● ●● ● ●● ● ●● ● ●●●● ●● ● ● ●●● ● ● ● ● ● ● ● ● ● ●● ●●● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ●● ● ● ● ● ● ●● ● ●● ● ● ● ● ● ● ●● ●● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ●●● ● ● ● ● ● ●● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ●●● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ●● ●● ● ● ● ● ●● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ●●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●●● ● ● ●● ● ● ●●● ● ● ● ● ● ● ● ● ●● ● ● ● ● ●● ● ●● ● ●● ●● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ●●● ● ● ● ● ● ● ●●● ●● ● ● ● ● ● ●● ● ● ● ● ● ● ● ●●● ● ● ● ● ●● ● ●●● ● ●● ●● ● ● ● ● ● ● ● ● ● ● ● ● ●●● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ●● ● ● ● ● ● ●● ● ● ● ● ● ● ● ●● ● ● ●● ●● ● ● ● ● ●● ● ● ● ● ●● ● ● ● ● ●● ● ● ● ● ●● ● ● ●● ● ● ● ● ● ● ● ● ● ● ●● ● ●● ●● ● ● ● ● ●● ● ● ●● ● ● ● ● ● ●●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ●●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●●● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●●● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ●●● ●●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●●● ● ●● ● ● ● ●● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● 22 ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● Total Fundraising (1000$) Fundraising Total ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● 3 ● ● ● ● ● ● ● ● ● ● ● ● ● ●

● ●

●

0 ●

● 0.0 0.2 0.4 0.6 0.8 1.0●

END Scores

35 Figure 9: END Scores, Fundraising, and Vote Shares from All Potential Candidates

0.7

0.6

0.5

Average END Score Average 0.4

0.3

Drop 10 20 30 40 50 60 70 80 90 99.9 100

Vote Share (%)

Average Funds (100K $) Average 1

Drop 10 20 30 40 50 60 70 80 90 99.9 100

Vote Share (%)

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