Dark Parties: Unveiling Nonparty Communities in American Political Campaigns

Abstract

Since 2010, independent expenditures have grown as a source of spending in American elections. A large and growing portion comes from “dark money” groups—political nonprofits whose terms of incorporation allow them to partially obscure their sources of income. I develop a new dataset of about 2,350,000 tax documents released by the IRS and use it to test a new theory of political spending. I posit that pathways for anonymous giving allowed interest groups to form new networks and create new pathways for money into candidate races apart from established political parties. Akin to networked party organizations discovered by other scholars, these dark money networks channel money from central hubs to peripheral electioneering groups. I further show that accounting for these dark money networks makes previously peripheral nodes more important to the larger network and diminishes the primacy of party affiliated organizations in funneling money into candidate races.

INTRODUCTION

“This is a sea change in the way that we look at the opportunity to engage in the political process. Most organizations, and certainly we did for a number of years, look at politics as a partisan opportunity, right? You bet on the team that’s closest to what you want to accomplish, and then you try to work with them to get things done. A couple of years ago, we looked at that and said, while that’s gotten some things done, it’s not accomplishing nearly enough for the country, and it’s having all sorts of negative consequences in terms of alienation and polarization…We said we can do better than that.” –Brian Hooks, President of the Charles Koch Foundation, speaking at the 2019 Global Philanthropy Forum. (Greve and Alfaro 2019)

During the 2018 midterms, Democrats, who were desperate to claw back control of Congress, got a boost from an unlikely source—the Koch brothers.

Following her vote in support of a rollback of Dodd–Frank rules on small- and medium-sized banks, Americans for Prosperity, the flagship organization in the Koch network, unveiled a digital ad campaign in support of the then-Senator Heidi Heitkamp of North Dakota (Stolberg 2018). Almost simultaneously, the Koch network announced it would not be supporting Heitkamp’s Republican challenger, Kevin Cramer (despite this, Cramer eventually went on to win the election in November). As Emily Seidel, the CEO of Americans for Prosperity told a reporter, “Why would Cramer or any other Republican feel like they need to listen to this network if they know we’ll support them anyway?” (Severns 2018).

This heretical behavior earned the Koch brothers a strong rebuke from the Republican Party’s ranking elected official, former President Donald Trump. Taking to Twitter early one morning in the months before the November election, Trump referred to the brothers as “a total joke in real Republican circles” (Peters 2018). He further boasted that he had never sought their support because he did not “need their money or bad ideas” (Peters 2018).

The nascent schism between the Koch network, one of the most well-funded and influential set of conservative interest groups, and a sitting Republican President, who at the time was just under 2 years into his first term, highlights an interesting extension to the contemporary academic understanding of political parties. Interest groups are portrayed as at once being at-odds with parties or forming their core. Formal party organizations are either formed to manage the demands of competing interest groups or are entirely led by them.

The theory of the “extended party network” (EPN), or the conceptualization of a party as a “network of policy demanders,” holds that interest groups—rather than politicians—are the nucleus of political parties and work to elect politicians committed to enacting their goals once in office. These interest groups, in turn, work to elect politicians that are “genuinely committed to what the policy demanders want regardless of the wishes of the median voter” (Bawn et al. 2012, 579). Past efforts to observe this “EPN” in action have relied on campaign finance records in order to document the financial linkages between the varying nodes in the network (see Desmarais, La Raja, and Kowal 2015; Herrnson and Kirkland 2013; Kolodny and Dwyre 2018; Skinner, Masket, and Dulio 2012). By cataloging the paths that political money takes as it flows into elections, these works sought to clarify which groups commanded the most influence among all the constituent organizations of the party network.

However, especially since the Citizens United decision of 2010, an increasingly large sum of money has decamped from the transparent realm of funds governed by the Federal Election Commission (FEC). The rise of “dark money”—or political money routed through Internal Revenue Service (IRS)-governed nonprofit organizations who are subject to far less stringent disclosure rules—in American elections means that a substantial percentage of American campaign cash in the course of the last decade has effectively gone underground. How does our view of the EPN differ when this unaccounted money enters into the picture? Does it represent a significant shift in political money’s center of gravity or is it merely the “hydraulic nature” (Issacharoff and Karlan 1998) of money in politics adapting to new rules but whose flow originates in the same source as before?

More broadly, interest groups are alternatively valiant defenders of some favored policy goal or a scourge that threatens the very integrity of a democratic endeavor. Interest groups either short circuit the policymaking process or form the very institutions without which democracy would be unthinkable (to paraphrase Schattschneider 1942). The ways interest groups influence politics are myriad, but one vital pathway is through amassing and distributing money. Similarly, a primary function of formal party organizations is also to raise money in order to protect incumbent elected officials and grow their seat-share in legislative bodies (e.g., Heberlig and Larson 2012). When it comes to fundraising, do interest groups compliment formal party organizations or offer their own parallel structure separate from the party apparatus? Including dark money organizations in this analysis allows one to fully account for all sources of political money and answer this question thoroughly for the first time.

To the aforementioned ends, I developed a database of about 2,350,000 tax returns recently released by the IRS. While information about donors giving money to these nonprofits is redacted from public view, grants made and received from other organizations allow me to link political nonprofit groups to others via financial ties. This allows for a more complete picture of the fundraising networks that pervade in the world of so-called dark money groups and illuminates relationships that are absent from the FEC data employed by the overwhelming majority of those researching campaign finance. I find that far from the spontaneous outpourings of political speech and preference that many proponents of the current campaign finance regime purport them to be (see Samples 2008; Smith 2009), these dark money groups are linked via the flow of substantial amounts of grant money—forming distinct network communities within the larger campaign finance landscape.

Until the development of this dataset, dark money organizations could only be analyzed as separate and distinct units—singletons adrift in a sea of campaign cash. However, analyzing these organizations as independent actors misses a deep level of coordination and connectivity that takes place beyond what is disclosed to the FEC and studied by campaign finance scholars. Studied as a collective, one can begin to view a collective behavior for these organizations, rather than just patterns of giving by individual groups who may be deployed strategically in order to mask greater coordination or obscure any affiliations with more known interest groups. More crucially, dark money organizations must be studied as networks because any lack of transparency along the flow of money obscures accountability further downstream. Dark money organizations function not only to obfuscate donor information when spending directly in an election, but when transferring funds to another politically active group as well. Determining how dispersed or concentrated these networks are is vital for making broader assessments about the health of pluralism in the American politics. As Schattschneider (1960, 35) famously opined “The flaw in the pluralist heaven is that the heavenly chorus sings with a strong upper-class accent.” The ability to muddle funds allows certain well-funded interest groups the ability to create illusions of broad-based support and up-swells of grassroots sentiment. In other words, to extend Schattschneider’s metaphor, it is important to understand how many members of this heavenly chorus are actually singing and how many are simply lip-syncing.

In the age of dark money, parties are no longer the only organizations pulling the purse strings. Aspirants for elected office can now look to nonparty organizations for both cash assistance and support with crucial campaign functions that were once the sole purview of party organizations. As such, party organizations have begun to lose not just their mediator role for political money, but a major tool for disciplining recalcitrant candidates and luring wavering office seekers toward a common set of policies and political rhetoric. Given that dark money organizations are largely ideological interest groups, this shift in the balance of financial power may also incentivize a shift away from the conciliatory and accommodating politics of party coalition building toward the more hard-line, purist politics of groups dedicated to the pursuit of rigid ideological goals.

With the entire funding network illuminated by these new data, I find that a more complex picture of the EPN than the one previously imagined begins to emerge. Party-affiliated organizations still maintain influence as the “coalition managers” of groups in the EPN (Karol 2009). However, when considering the full breadth of political money that includes dark money organizations, parties cease to be the crucial conduits they appear to be when analyzing the network with just the activity reported to the FEC. Importantly, when measuring betweenness centrality—in this case the extent that a party organization represents a critical path for money into candidate races—the primacy of party organizations over dark money organizations disappears when considering financial linkages invisible when utilizing FEC data that campaign finance scholars commonly rely on.

This article lays our several new contributions to both the literature on parties, interest groups, and money in politics. First, it challenges the “parties as kingmakers” theory and shows, via the inclusion of dark money to the known universe of campaign money, that interest groups are more in the driver’s seat than previously imagined. Along these lines, it demonstrates the inadequacy of existing campaign finance data for assessing the role of interest groups in election fundraising. Finally, this article explores several implications these dark money-funded interest group networks have on campaigns and governance. Given that over $1 billion was donated or spent by dark money groups in the 2020 election (Massoglia and Evers-Hillstrom 2021) and in light of the recent Supreme Court’s decision in Americans for Prosperity Foundation v. Bonta, dark money appears to have cemented itself as a permanent feature of American elections going forward. Thus, understanding its influence is crucial for future campaign finance research. Though outside the scope of this article, future scholarship should work toward constructing a more complete topology of political money, something vital to developing accurate and complete inference about the role of interest groups in American politics.

PARTIES AS NETWORKS

Though the founders of the United States wasted no breath nor ink in decrying their ill-effects, political parties have remained a vitally important part of American politics since their emergence during the dawn of the republic. Political parties perform a myriad of functions crucial to the maintenance of government such that American democracy as presently conceived would be “unthinkable” in their absence (Schattschneider 1942). From electing candidates to office to guiding their votes once seated, parties are intimately connected to almost every facet of American government.

Despite their ubiquity, however, parties are notoriously hard to define. Scholars from the classic era such as Key (1964) offer complementary visions of what makes a party as well as what functions a party performs. Aldrich (2011, 5) goes further in describing parties as vehicles for election—“the creature of the politicians, the partisan activist, and the ambitious office seeker and office holder.” Crucially, Aldrich (2011) locates these office-seeking actors at the center of party network. This is the crucial distinction highlighted by Bawn et al. (2012) and what later came to be known as the “UCLA School”—that it was interest groups rather than office seekers who were the true center of gravity that other satellites in the party constellation orbited around (e.g., Cohen et al. 2009; Grossmann and Dominguez 2009; Noel 2014). Most recently, Masket (2016, 114) developed a useful definition of this theory. “Parties are not rigid entities, limited to their appearances in legal definitions or business filings,” he writes. “They are, rather, networks of intense and creative policy demanders…working both inside and outside the government to determine the sort of people who get elected to office and thus change public policy.”

Contemporaneous to the emergence of the “EPN” theory were major changes in campaign finance laws which altered the way the EPN deployed resources during elections. Skinner, Masket, and Dulio (2012) trace the position of 527 groups within party networks by tracing common employees. The authors find that shifts in campaign finance law in the early 2000s led to party organizations turning toward extra-party electioneering organizations to skirt prohibitions on fundraising and spending in federal elections. They also found that the networks of 527’s mirror the hierarchical structures of the parties which they represent.

This interest-group centric view of parties holds that these coalitions work in concert to control nominations—effectively deciding contests before voters even get a chance to decide. Yet, as McCarty and Schickler (2018) point out, this theory has a central deficiency—namely how the incentives of elected officials and formal party leaders line up with those of interest groups such that the agency relationship theorized by the UCLA school can materialize. As works such as Barber (2016) and La Raja and Schaffner (2015) point out, the goals of formal party organizations and interest groups are often at odds with one another. Others, however, reconcile this by placing formal party organizations at the center of the EPN (e.g., Karol 2009; Kolodny and Dwyre 2018)—coordinating the larger orbit of interest groups.

The U.S. Supreme Court’s decision in 2010’s Citizens United v. FEC and the subsequent overhaul of campaign finance law in its aftermath have led some to argue that empowered interest groups have hijacked American democracy and are fueling the rise of extremists by overwhelming the traditional party apparatus. Notably, La Raja and Schaffner (2015) found evidence of this phenomenon in the states by observing variation in their campaign finance laws prior to the decision. However, Dwyre and Kolodny (2014) test the idea with federal data and find that formal party organizations have remained the central actors that coordinate the efforts of others. Others argue that the independent expenditure organizations borne by the decision—notably super PACs (Political Action Committees)—are mere party creatures in wearing different names but fulfilling the same functions are their centralized fore-bearers did prior to 2010 (see Mann and Corrado 2014; Drutman 2015).

Following the 2010 election cycle largely due to the Supreme Court’s decision in Citizens United v. FEC, so-called “outside spending” or advertising purchased and produced by organizations not officially affiliated with a candidate, exploded (Hasen 2016; Jacobson and Carson 2015). While the majority of this spending came from so-called “super PACs,” or PACs unencumbered by traditional spending and fundraising limits as they were in theory “independent” from a candidate’s campaign (see Dowling and Miller 2014), a sizable portion of outside spending came from so-called “dark money” organizations (see Figure 1). These organizations are so named because their method of incorporation—as IRS designated 501(c) nonprofits—carries no legal requirement to disclose their donors. Dark money spending accounted for an average of about 17% of all independent expenditures in each election cycle between 2012 and 2018, according to data collected by the Center for Responsive Politics, a nonprofit that monitors trends in campaign finance.

FIGURE 1. FEC Reported Spending by Dark Money Groups and All Outside Spending (Inflation Adjusted to 2018 Dollars)

Source: Center for Responsive Politics.

Over this same time period, the amount of money ideological interest groups (see Barber 2016) spent in American elections increased. While parties dominated outside spending in the years prior to Citizens United (see Figure 2), the three election cycles following that decision saw ideological group spending dwarf that of all other categories. In those three cycles, ideological interest groups made more independent expenditures than all three of those other categories combined.

FIGURE 2. Outside Spending by Type

Source: Center for Responsive Politics.

As important as how much money these outside groups were spending in elections is also the degree of coordination between them. Are these organizations just conduits for the same donors as before? Moreover, are these groups funneling money through the same channels in the era before the explosion of dark money in the 2010s?

One can answer these questions by looking to the degree of interaction between these groups and also to the extent that they interact with other interest groups. If their activity remains within relatively closed networks, it implies a separate fundraising ecosystem and a departure from established networks of the past. Further, the presence of these groups may alter the relative influence of other previously important nodes and thus shift the scholarly perception as to the true gatekeepers of money in politics.

Until recently, finding the connections in the dark money networks was virtually impossible. Though federal tax law dictates that all nonprofits fill out a yearly disclosure form detailing various facets of their financial operations, prior to June 2016, ¹ these forms were housed in a non-machine readable format as image files. Aside from being tremendously cost prohibitive (prior to the 2016 release, the only method of obtaining a complete universe of these disclosure forms was by spending $2,100 ² for a DVD (Digital Video Disc) of just 1 year’s worth of filings), anyone interested in harvesting data from multiple nonprofits would have to perform the arduous task of manually inputting data from the several hundred fields on each disclosure form. Given that political nonprofits are often also recalcitrant to make their disclosure forms available despite being under legal obligation to do so (see Stevenson 2013), the only way to begin collecting data on the finances of political nonprofits would be to collect Form 990s, as they are known under their IRS designation, directly from the IRS itself.

For this analysis, I collected data from over 2.3 million nonprofit financial disclosure forms filed with the IRS as of September 2019. This provided me with data on about 540,000 unique nonprofits who had filed a disclosure form for activities between the years 2007 and 2016 (typical filing deadlines mean that these disclosures are often filed up to 12 months after the calendar year’s end). From these forms, I have harvested data on cash transfers between one nonprofit group to another to create network linkages. Next, I connect the IRS data to data from the FEC using politically active nonprofits identified by the Center for Responsive Politics as bridges. With these combined data, I am able to document a vastly expanded and more complete picture of the financial network of money in American federal elections (see the “Data Collection” section of the Supplementary Material for a more detailed description of the data gathering process).

Understanding why these new financial data are important for the study of party networks is best demonstrated by looking at a congressional race where dark money played a large role. In the 2012 Virginia Senate race between former Virginia Governors George Allen and Tim Kaine, dark money groups spent over $19 million, $16 million of which was spent opposing the election of the eventual winner Kaine, according to the Center for Responsive Politics. ³ The Virginia Senate race was one of six congressional races in that cycle where over $5 million was spent by dark money groups and one of four races where non-disclosed outside spending totaled over $10 million.

Relying only on FEC disclosures provides a somewhat limited view of the relationships between dark money groups spending in that race. As Figure 3 indicates, a total of 18 outside groups made independent expenditures either in favor or opposing Kaine. Examining only FEC data, these groups appear autonomous and disconnected from one another. Without including IRS data, all appear as outpourings of “spontaneous speech” to use Justice Anthony Kennedy’s verbiage in his opinion to 2003’s McConnell v. FEC which Citizens United would overrule 7 years later. Compare Figure 3 to the diagram of underlying financial connections depicted in Figure 4. Figure 4 depicts all the organizations that spent to oppose Kaine in that race and all the groups that had given money to them (i.e., a second-degree ego graph). Pictured as well are the “communities” that each group belongs to. These communities were determined by a “random-walk” bisection of the network which takes random paths through the various linkages of the network and groups each node based on how densely they cluster with other nodes (for further explanation, see Newman 2010; Pons and Latapy 2005). With the IRS data included, the deeper layer of coordination behind these previously autonomous-looking groups is more clear. These independent groups are actually anything but, as they draw from common pools of funding.

FIGURE 3. Dark Money Spending in 2012 Virginia Senate Race Depicted using Only FEC Data

Note: Unlike in Figure 4, deeper layers of coordination are invisible. Solid lines = negative independent expenditures. Dashed lines = positive independent expenditures. Dotted lines = direct contributions.

FIGURE 4. Dark Money Spending Opposing Democratic Candidate Tim Kaine in 2012 VA Senate Race Depicted with Linked FEC and IRS Data

Note: Unlike in Figure 3, deeper layers of financial coordination are apparent when including IRS data. Letters indicate differing communities detected using random-walk technique. Solid lines = negative independent expenditures. Dotted lines = grants made between nonprofit organizations. Nonprofits making grants to more than two other nonprofits in the graph and nonprofits active in other 2012 races are named.

Eight distinct communities emerge from this method, each revolving around a nonprofit that spent against Kaine. Community D centers around “Americans for Tax Reform,” a long-established right-wing advocacy group founded by conservative activist Grover Norquist. Yet while those browsing FEC disclosure forms would only see the combined approximate $580,000 Americans for Tax Reform spent on negative independent expenditures in the 2012 VA Senate race, the contributions of several well-known industry groups to Americans for Tax Reform such as the Motion Picture Association of America and the National Cable and Telecommunications Association would remain unseen. In the three years prior to the 2012 election, the groups depicted in the above community plot gave a combined $5,649,000 to Americans for Tax Reform, according to the IRS disclosure forms. Similar communities exist around Crossroads GPS (Community C), the largest dark money organization of the 2012, ⁴ and the U.S. Chamber of Commerce (Community E). Crossroads GPS, which was founded by political strategist Karl Rove, spent a combined $71 million in the 2012 election, nearly twice what the second highest spending dark money group spent.

Also interesting is the constellation of nonprofits that form around the “American Future Fund” and “American Commitment” (Community B). With innocuous names typical of dark money groups, these two groups spent over a half-million dollars opposing Kaine’s election. However, the new IRS data show these groups firmly ensconced within the orbit of more recognizable nonprofits—Americans for Prosperity, the Freedom Partners Chamber of Commerce, and American Encore. What is available instantly to researchers with the development of this new dataset was revealed by the Washington Post (see Gold 2014) 2 years after the 2012 election through laborious tracking and analysis of paper tax returns—these groups are part of a maze of nonprofits developed by the Koch brothers.

The aforementioned Koch network is emblematic of the labyrinthine structure that these nonprofit networks that spend money to influence elections assume. Looking at the funding structure for Americans for Prosperity in the 2012 cycle (Figure 5), one sees that similar patterns emerge. Figure 5 shows a first-degree ego graph for that organization—that is, the organizations that made grants to Americans for Prosperity and organizations that received grants from that organization. One can see that Americans for Prosperity shares connections with many other political nonprofits. Most importantly, all of these nonprofits receive funding from two common sources—Freedom Partners Chamber of Commerce and American Encore (formerly known as the Center to Protect Patient’s Rights)—whose giving dwarfs any of the other grants in the network. Similar to the party network graphs, money flows from more central nodes into a disparate network of satellite organizations before making its way to political candidates.

FIGURE 5. Nonprofits Making Grants to Americans for Prosperity (2011–12)

Note: Utilizing just FEC data, these other organizations funding Americans for Prosperity are invisible, especially Freedom Partners Chamber of Commerce, American Encore, and the TC4 Trust, which act as financial backers for several other dark money groups (see Figure 7). Other nonprofits that spend in candidate races are marked as triangles.

To illustrate the validity of the community detection strategy, compare the membership of one network created via this algorithmic process with a similar map (Figure 6), created by the Center for Responsive Politics and featured in the Washington Post in 2014 (Gold 2014). In that network, one can see the flow of money outward from Freedom Partners, the TC4 Trust and the Center to Protect Patient Rights to a myriad of political nonprofits. The data for this analysis were painstakingly compiled by hand with IRS disclosure forms housed in disparate locations. (While the 990 forms must be made available to the public, the law does not mandate they be held in a central location. This has led many organizations either making interested parties arrive in person to obtain them or simply stonewalling requests for these data.) ⁵

FIGURE 6. Diagram of Koch Brothers Funding Network, 2012

Source: Center for Responsive Politics (January 7, 2014).

Compare Figure 6 to the graph of a community created by a five-step random-walk algorithm from the donation network of nonprofits in the 2012 election (Figure 7). The network map created via the community detection algorithm produces an almost identical graph to the one built by the Center for Responsive Politics (the Center to Protect Patient Rights was renamed “American Encore” following the 2012 election and carries that name in Figure 7). Thus, this agnostic method of partitioning the graph produces results equivalent to what experts well-versed in the subject matter would produce. Because nonprofits operate in tight orbits around one another, this graph-partitioning strategy gives us useful communities of interest for further analysis.

FIGURE 7. The Koch Network of 2012 as Drawn via a Community Detection Algorithm using the Combined FEC and IRS Data

Note: Americans for Prosperity, shown in Figure 5, is but one constituent portion of this wider movement of electorally focused organizations. Names of organizations depicted in Figure 6 are bolded and in italics.

CONNECTING THE DARK MONEY NETWORK

Linking nonprofits via grants made to one another, I now apply the same algorithm used to draw the communities in the previous section to all races for Congress during the 2012, 2014, and 2016 cycles (though Citizens United was decided in January 2010, growth in dark money spending did not begin in earnest until the 2012 election given the time necessary to incorporate these groups with the IRS). This method is ideal for detecting communities within the nonprofits grant network as it accounts for the directionality of the linkages as well as being computationally efficient enough to run on a network of this size. It also provides a semi-agnostic method of partitioning the dark money network that can be compared to known linkages between organizations and communities of nonprofits as described by media outlets. In running the walktrap algorithm, I weight the strength of the network ties by the size of the grant given such that larger donations create “stronger” ties than smaller ones.

I begin with network graphs of 2012–2016 congressional elections, of which there were 183,907, 223,724, and 244,343 nodes, respectively. I then subset these graphs to the second-order neighborhoods (i.e., the nonprofits they received money from and the nonprofits they gave money to) for each of the nonprofits that purchased advertising in a given election cycle. In addition, I excised the vertices representing politicians, so advertising in support or opposition of a common candidate did not create a network linkage. This methodology produced 636 unique communities for 2012, 613 for 2014, and 901 for 2016. Further, I expand the previous network using FEC data to include super PACs receiving money from a dark money network. For instance, in the 2017 special election in Georgia’s 6th Congressional District, out-of-state dark money groups contributed about $3.75 million to super PACs involved in the race (Davis 2017). Funneling money into an already existing super PAC is a common practice for these networks. Using FEC records, I link these networks of super PACs and dark money groups to the ultimate destinations of their money—the congressional candidates their ads either support or attack.

Some communities in this network were relatively dense, with a maximum of 93 organizations in one community, whereas most others were relatively sparse with 2,040 communities containing only two or fewer organizations. Overall, the average number of nonprofits per community was 1.47 with a median value of 1. Given the relatively few steps the walktrap algorithm took in determining communities and given that not all independent expenditure-making organizations received support from nonprofit entities, such a large number of singletons is to be expected. However, the conservative approach taken in delineating communities ensures that one can be quite confident that each community drawn by the algorithm represents an actual network of nonprofits and independent expenditure-making organizations. ⁶

Adding these additional financial data from the IRS disclosures changes the structure of the overall network. As shown in Figure 8, about 10% of the dyads that were in the same community when utilizing only FEC data were not in the same community once the IRS data were added. In 2016, a full 15% of these dyads switched from both nodes belonging to the same community to each node being grouped separately.

FIGURE 8. Percentage of Dyads Remaining in the Same Community when Graphing the Network with FEC Data and Combined FEC and IRS Data

Neglecting these communities gives one a skewed view of just how dominant the spending of these groups are. The prevailing wisdom, as typified by works such as Mann and Corrado (2014) or Drutman (2015), is that despite the Citizens United ruling, the top independent expenditure-making organizations are dominated by party affiliated groups. That hold true when considering just FEC data. As depicted in Table 1a, in the list of the top 10 independent expenditure makers in the 2012 race, one can see a list dominated by party affiliated groups with the exception of American Crossroads, Crossroads GPS, and Americans for Prosperity. However, when accounting for these interwoven communities, the picture shifts dramatically. Now the interest groups in the orbit of both Crossroads GPS and Americans for Prosperity dominate all other groups in terms of independent expenditures made in that election cycle except for Mitt Romney’s super PAC. Americans for Prosperity’s network spent more than President Obama’s super PAC Priorities USA Action and more than both the Republican and Democratic Congressional Hill Committees (see Table 1b). Given the high degree of financial coordination between these groups, looking at their total spending gives a more realistic view of their influence in American elections.

Table 1. When Observing the Spending Levels of Dark Money Organizations as a Whole, Their Spending Levels Can Often Rival the Most Prominent Party-Affiliated Organizations

Note: Dark money networks are denoted with a “***.”

DOES DARK MONEY RESHAPE THE EXTENDED PARTY NETWORK?

The emergence of dark money as a potent spending force in American elections also allows us to reassess our conceptualizations of the EPN. Previous work utilizing FEC data to identify the EPN (e.g., Desmarais, La Raja, and Kowal 2015; Grossmann and Dominguez 2009; Herrnson and Kirkland 2013; Kolodny and Dwyre 2018; Manento 2019) excludes possible deeper levels of financial coordination that take place outside of the jurisdiction of federal election law. Accounting for these unseen financial linkages could reorder understanding of the EPN and force previously peripheral groups to the center. Conversely, these dark money networks may not be integrated into the EPN—operating peripherally and outside the orbit of central coordinating entities. Both scenarios would lead scholars to develop an updated understanding of the relative influence of interest groups in the party network framing.

To measure this, I borrow an approach used by Koger, Masket, and Noel (2009) and examine network centrality measures for all organizations spending in the 2012, 2014, and 2016 election cycles. I create two separate directed networks, one using only data from the FEC and a second that incorporates both FEC data and my IRS dataset of financial transfers. For the second graph, I combine the FEC data with a one-degree ego graph accounting for funds received and grants made for all dark money groups active in each of the two cycles. This approach gives a conservative estimate to financial coordination between groups while limiting spurious linkages that may result from extending the ego graph backwards a second or third degree. With both graphs, I weight linkages between each organization by the dollar amount of the financial transfer. Finally, I prune the network to exclude all connections formed by a transfer of $5,000 or less. This reduces the size and complexity of each network to a level where it is computationally feasible to analyze it as well as removing spurious connections.

Like Koger, Masket, and Noel (2009), I examine the degree centrality of each node of the network, but I extend their approach to include a weighted in-degree and out-degree measure to account for the amount of money passing through each connection—the intuition behind this approach being that both collecting and disseminating more money from fewer sources is a better proxy for overall node importance than just the number of connections. I also include a betweenness centrality measure (see Newman 2010) as a measure of how important each node is to the flow of political money in the network. Finally, I utilize the PageRank centrality of each node (Brin and Page 1998), a measure of network centrality ideal for this purpose in that both graphs are directed. This centrality measure helps identify the most important nodes of the network while obviating the issue of other centrality measures (see Borgatti 2005) assigning high values to nodes linked to other nodes with a high out-degree—as would be the case when dealing with campaign finance data. Furthermore, PageRank centrality is optimized to handle directed networks better than eigenvector centrality, an older measure of centrality from which PageRank centrality was derived.

As shown in Table 2, the graphs of both networks produce similar statistics—though the addition of IRS data produces a much denser graph for obvious reasons. The average path length in both networks is roughly similar—as is the largest component of each. In addition, running a five-step walktrap community detection algorithm on both networks produces similar modularity scores for each.

TABLE 2. Network Statistics

I next look to the most central nodes of each network, beginning with the network created using only FEC data (Table 3). Looking at the top 15 most central nodes as determined by the PageRank Centrality algorithm, I find a familiar listing of the most important organizations of the 2012 and 2014 elections. At the top of the list in 2012 were fundraising committees for both presidential candidates (Romney Victory Inc and Obama Victory Fund 2012), as well as Act Blue, a small-donor giving platform for Democratic candidates. Other top entries were the four Hill Committees as well as the Republican and Democratic National Committees. In 2014, we see a similar group as well as the fundraising organizations for both former Speaker of the House John Boehner and Republican Senator Tom Cotton (AR)—both prolific congressional fundraisers in an important midterm election year. The only issue-oriented groups appearing in both years were NORPAC, a political action committee promoting U.S.–Israeli relationships, EMILY’s List, which sponsors Democratic women’s congressional campaigns, and Club for Growth, a conservative organization. Ranking these organization by betweenness centrality, which prioritizes a node’s importance as a connector of other nodes, yields a similar list—though importantly one that elevates the ranking of several state-level political parties.

TABLE 3. Centrality Measures, FEC Data Only, Sorted on PageRank Centrality

Interestingly, this list does not shift appreciably when adding in the IRS data from political nonprofits. As Table 4 illustrates, the most central nodes of this expanded network are quite similar to those from the original. In this expanded dataset, party affiliated committees are still the most central as measured by their PageRank centrality score. When looking at the betweenness centrality measure, a similar list emerges—again with more state party organizations rising to the top.

TABLE 4. Centrality Measures, FEC + IRS Data, Sorted on PageRank Centrality

This would seem to indicate that despite the Citizens United ruling and the emergence of dark money, party-affiliated organizations still retain their central place in the EPN. Party-affiliated organizations still acted as the main coordinators for campaign money into candidate races in the two cycles following that 2010 decision. It would appear then that the party-centric theory of the EPN has garnered new evidence and that fears of parties losing their control over the EPN are somewhat inflated.

However, the inclusion of the IRS data to account for money flowing outside the jurisdiction of the FEC complicates this understanding of the role parties play as conduits for political money. Looking at the two centrality measures depicted in Tables 3 and 4, I perform a difference-of-means test between both the PageRank and betweenness centrality measures for party and dark money organizations. Party organizations are those designated as such by the Center for Responsive Politics in their taxonomy of interest groups, a taxonomy used by works such as Barber (2016) and others. Dark money organizations are those categorized as such by the Center for Responsive Politics. ⁷ Expressed mathematically, the centrality measure for organization i is modeled as a function of whether or not it is a party organization, according to the following models:

(1) $$ \begin{array}{rl}PageRank\hskip0.3em Centralit{y}_i=\alpha +{\beta}_1{(Party\hskip0.3em Organization)}_i+\epsilon & \end{array} $$

and

(2) $$ Betweenness\hskip0.3em Centralit{y}_i={\displaystyle \begin{array}{l}\alpha +\hskip2px {\beta}_1{\left(Party\hskip0.3em Organization\right)}_i+\epsilon .\end{array}} $$

When measuring PageRank centrality, as shown in Figure 9 and detailed in Table 5, party organizations remain more central than nonparty organizations both with and without including the IRS data. Given the nature of the measure, one might expect this. In studying Twitter conversations involving Spanish politics, Borondo et al. (2014) find that politicians’ accounts were the most central (as measured by PageRank centrality) to these online discussions. Looking at the most central nodes as measured by PageRank in Table 4, one finds not just party organizations, but the party organizations most heavily involved in electoral politics such as Hill committees and presidential campaign committees. Given a weighted directed network, it is logical that such nodes would be not just the ultimate destinations of political money, but a source of funds for other campaigns. PageRank centrality measures the degree to which a given node is an influential reference to other nodes.

TABLE 5. Measured with PageRank Centrality, Party Organization Remain More Central to Fundraising Regardless of Whether IRS Data Is Included

Note: $ {}^{***}p<0.001 $ ; $ {}^{**}p<0.01 $ ; $ {}^{*}p<0.05 $ .

FIGURE 9. Party Organizations Have a Higher PageRank Centrality than Dark Money Organizations in Both Datasets

Note: Error bars show a 95% confidence intervals.

However, the interesting wrinkle when adding the IRS data is the shift in betweenness centrality. This measure of centrality indicates the degree to which a specific node is an influential gatekeeper in the network, not an influential referrer. Nodes with a high betweenness centrality act as conduits to other more disconnected nodes in the network—forming crucial bridges between them. Song and Yeo (2017) provide an intuitive example of this metric in looking at the betweenness centrality of world airports to measure their relative importance in the world air-travel network. While airports in the United States served a higher number of flights, airports in Asia had far higher betweenness centrality, which means that a disruption at one of these airports would have a greater influence on world air traffic.

Applying this measure to the financial networks of U.S. congressional elections, party organizations have a higher betweenness centrality than dark money organizations when examining just FEC data, a distinction that is statistically significant at the p < 0.05 level. When one adds the IRS data, however, this distinction disappears and suddenly, parties lose their primacy in connecting other nodes (see Figure 10 and Table 6). Thus, parties may not be the crucial gatekeepers that can open or close the spigot of political cash that scholars examining only FEC data once thought they were. ⁸ The ability of these networked interest groups to affect arenas such as the nomination process via primary elections or the formation of intraparty organizations (e.g., Rubin 2017) following an election is a subject ripe for future inquiry.

TABLE 6. Measured with Betweenness Centrality, Party Organization Are No Longer More Central to Fundraising Regardless of Whether IRS Data Is Included

Note: $ {}^{***}p<0.001 $ ; $ {}^{**}p<0.01 $ ; $ {}^{*}p<0.05 $ .

FIGURE 10. Party Organizations Have a Higher Betweenness Centrality when Measuring Only with FEC Data

Note: The distinction disappears once IRS data is added. Error bars show a 95% confidence intervals.

Similarly, if one uses an alternate specification, another picture begins to emerge than the one painted by past scholars of the EPN. Looking toward degree centrality—a basic measure of how many organizations either gave money to an organization or how many organizations a specific group gave money to—one sees a different image of the EPN. Several scholars have used this rather crude metric to measure the influence of groups in the EPN previously (e.g., Grossmann and Dominguez 2009; Koger, Masket, and Noel 2009; Kolodny and Dwyre 2018; Skinner, Masket, and Dulio 2012). I modify this tactic with an improved metric—the weighted degree centrality (Barrat et al. 2004)—to weight this tally by the amount of money that formed each connection. As shown in Tables 7 and 8, when looking at the amount of money flowing through various nodes in the network, political nonprofits begin to emerge in dominant positions. ⁹

TABLE 7. Centrality Measures, FEC + IRS Data, Sorted on Weighted In-Degree Centrality

Note: Bold = dark money organization.

TABLE 8. Centrality Measures, FEC + IRS Data, Sorted on Weighted Out-Degree Centrality

Note: Bold = dark money organization.

Examining weighted in-degree centrality (Table 7), American Encore, the central funder for the Koch network depicted in Figure 5, occupies a spot just under both 2012 presidential campaigns and just above the Democratic National Committee. The Sierra Club and American Crossroads, both 501(c)(4) nonprofits, occupy the 9th and 10th spots, respectively, above both the Republican and Democratic Congressional Hill Committees. A similar ranking occurred in 2014, with the Sierra Club, NextGen Climate Action Committee, and the National Rifle Association all making the top 15 list. The presence of some nonprofits such as the Schwab Charitable Fund and the East Bay Community Foundation represents a deficiency with the IRS data—namely that while these organizations’ support for politically active groups can be known, the exact amount each spends on political activity is not a matter of public record.

Measuring out-degree centrality (Table 8), one finds three Koch network organizations in the top 15 during the 2012 cycle—the three financing organizations depicted in Figure 5. In addition, both the Sierra Club Foundation and Planned Parenthood also spent several million dollars on grants to groups that were later active in the 2012 cycle. In 2014, several nonprofit groups sit prominently with party Hill Committees—though again, this is not to say that the entire sum depicted eventually wound up in a candidate race. Interestingly, Tom Steyer, founder of the NextGen Climate Action Committee and a mega-donor to environmental causes and Democratic candidates, was himself one of the principal financiers of organizations active in the 2014 election.

In both Tables 7 and 8, one should also note the PageRank centrality measure of all these organizations. Because of the insularity of the dark money communities described in the previous sections, the PageRank centrality of these organizations was at or near zero—indicating that they did not link to other nodes with many in-bound linkages. By this measure, these interest groups occupy a peripheral part of the EPN—in contrast to the central role theorized by Bawn et al. (2012) and more in line with the theory advanced by works like Hassell (2017) and earlier works like Aldrich (2011).

CONCLUSION

Given that such large sums of political money fall outside the purvey of the FEC, past researchers’ reliance on these data may have been providing an inadequate accounting of political money and thus inadequate theories about the role of money in contemporary politics. Relying just on FEC data provides an unrealistically atomistic description of independent expenditure groups. When fundraising is done via a nonprofit, common donors and benefactors cannot be established and thus each organization appears to operate in its own vacuum. Furthermore, because IRS disclosure requirements are so different from FEC requirements, information about an election may not be known until several years later.

Accounting for the finances of political nonprofits allows for a more complete, but nonetheless a still partial picture of the routes money takes into American elections. While party organizations remain influential in the fundraising network, their role as vital conduits has been diminished by the new pathways for political money carved following the Citizens United decision. Further, the magnitude of capital that dark money organizations can command shows that the influence of party organizations may be rivaled by the sheer sums these outside spending groups are able to deploy. The strong financial networks that dark money organizations form and their lack of interaction with more established PACs suggest that perhaps these organizations are content to operate at the periphery of the EPN where they are less vulnerable to influence by other members of the coalition.

These subnetworks of dark money organizations may explain rifts in legislative party coalitions such as the one described between President Donald Trump and the Koch brothers in the Introduction. While parties are able to coordinate the deployment of electoral resources, these subnetworks are able to raise equivalent, or in some cases superior, amounts of money. Independent expenditures may only be part of the story; Skocpol and Hertel-Fernandez (2016) and Skocpol and Williamson (2016) document the vast networks of volunteer support and candidate development that these interest group networks are also able to provide.

Further, the fact that party organizations do not retain their prominence in the fundraising network when considering the financial flows of dark money organizations highlights an important extension of work by Skinner, Masket, and Dulio (2012) studying 527 groups operating in 2000s era elections. Those authors found that those organizations often employed the same people as formal party organizations—making them essentially party organizations by just another name. Unfortunately, disclosure laws for 527 groups are much stricter than those covering 501(c)(4) organizations meaning that one is unable to compare personnel as in that article. However, were both of these groups drawing from the same donor pool, it is unlikely that parties would cease to be more central parts of the network than dark money organizations. Were dark money groups just a structure to avoid disclosure, these groups would shunt any money they collected immediately back into formal party managed organizations to be spent either on campaigns or infrastructure building initiatives. The fact that this phenomenon is not observed, even in such an opaque environment, indicates something structural is changing.

When studying party dynamics, scholars are right to focus the “intense policy demanders” discussed by Bawn et al. (2012). But studies on the cohesiveness of this intraparty network remain absent from the literature (though see Reuning 2020 and Yang et al. 2015). This research can, and should, expand beyond the typical arena of money in politics research—donations and spending in candidate campaigns—and examine the myriad other forms of politics dark money groups engage in. Examples of other arenas might include social media advertising, promotion of state-level legislation, or even organizing grassroots efforts such as protests and contact campaigns for elected officials.

Examining the spending of some of these dark money organizations through various election cycles helps illustrate the purpose that all these funds eventually serve. During the 2014 congressional midterm elections, Crossroads Grassroots Policy Strategies, a 501(c)(4) organization, spent about $27 million making it the second most active political nonprofit in that cycle after the U.S. Chamber of Commerce. Crossroads GPS, as the group typically goes by, worked in tandem in that cycle with American Crossroads, a super PAC whose donations are disclosed to the FEC. Indeed, the two organizations even share the same President—former Deputy Secretary of Labor Steven J. Law during the George W. Bush administration.

Crossroads GPS’ spending during that cycle was entirely on independent expenditures, with most opposing various Democratic candidates for the U.S. Senate. The vast majority of their expenditures, about $21 million in total, went to Main Street Media Group, a political media consultancy with close ties to both former George W. Bush adviser and Crossroads groups founder Karl Rove and Senator Mitch McConnell of Kentucky (Blumenthal 2014). Main Street Media Group’s connection to the Crossroads groups and political nonprofits in its orbit is so strong, that the ad buyer did not maintain a website and listed its address in FEC records as a P.O. Box in a Washington, DC suburb. While political ad buyers often charge significant markups above the baseline price of television advertising (Martin and Peskowitz 2018), Crossroads GPS also purchased over 5,000 ads in races during that cycle (Wesleyan Media Project 2014).

In 2016, 45Committee, a 501(c)(4) nonprofit allied with the Trump campaign, made about $21 million worth of independent expenditures during that campaign cycle. Like with Crossroads GPS, about $15.5 million of that money went to Del Cielo Media, a political media consultant. This media buyer has connections to other dark money organizations including the National Rifle Association (Massoglia 2020). Del Cielo, in turn, is used by a larger media buyer, Smart Media Group, to disguise its involvement in other dark money campaigns (Massoglia 2020). Unlike Del Cielo, Smart Media Group handles television ad buys for mainstream Republican candidates, most notably for Marco Rubio and Richard Shelby’s Senate election campaigns which spent about $27 and $6 million, respectively, through that buyer. ¹⁰

While common campaign vendors indicate a certain level of coordinated strategy (Nyhan and Montgomery 2015), it is important to note what the dark money groups the above groups typify did not spend their money on—payroll for campaign professionals outside of election cycles, party infrastructure maintenance, and core campaign activities such as Get Out The Vote (GOTV) and fundraising. By contrast, about 50% of the National Republican Senate Committee’s total spending in the 2014 cycle went to such activities as did 40% of the Democratic Senatorial Campaign Committee. Dark money groups are primarily independent expenditure-making machines, eschewing activities like providing legal services, personnel management, and other essential campaign tasks that remain under the purview of formal party organizations (Jacobson and Carson 2015), and concentrating solely on advertising. This would fit with other works on dark money (Oklobdzija 2019 and Wood and Spencer 2016), who found evidence that avoiding cross pressures from one’s social context may be a powerful motivator for opting to give in secret. Fairly innocuous party building and campaign activities would incur less backlash against a donor than a highly charged political ad or one that stakes out a controversial issue position. As such, a nondisclosing political nonprofit becomes the perfect vehicle for such a donor and these groups would thus shy away from other activities.

As Wood (2022) demonstrates, voters use information about campaign finance transparency to inform their votes in candidate races, punishing candidates who rely on nondisclosing organizations for support. Further, Dowling and Wichowsky (2015) find that voters discount the advertising of outside groups when they learn about the group’s financiers. As such, dark money organizations are most effective in information-poor environments—such as low-profile elections or elections in areas of country with sparse media coverage (see Hayes and Lawless 2018 or Darr, Hitt, and Dunaway 2018)—spending by these groups in such scenarios should be higher, a fertile avenue for future research. However, given that political advertising is high visibility by design, groups engaged primarily in this activity should theoretically be more motivated to “go dark.”

Future research should further examine the extent to which interest group networks have created parallel structures to those developed by political parties. This may provide a better understanding of the cohesiveness of the EPN and how that network responded to changes in electoral institutions. In regard to the realm of campaign money, a financial network where donor dollars must pass eventually through a formal party mediator has far differing implications than one where donors can inject that money directly into a candidate race via some other organization. As this article demonstrated, the organization of this network has great implications on exactly the extent to which a party, in fact, gets to decide.