Partisan Patterns of Presidential Preprimary Funding after Citizens United

Paper presented at the State of the Parties: 2016 and Beyond Conference, Ray C. Bliss

Institute of Applied Politics, University of Akron, November 9-10, 2017.

Karen Sebold, Joshua L. Mitchell, and Andrew Dowdle, University of Arkansas

ABSTRACT

Since the Supreme Court’s 2010 Citizens United decision, super PACs have played a larger role

in funding presidential nomination bids at the expense of the “official” campaign organization

that are set up by the candidates themselves. These outside organizations are less tightly

regulated than their official counterparts, and there is concern about the disproportionate role

they allow a small number of wealthy contributors to play in the fundraising process, which may

also exacerbate existing geographic disparities in the financing of these contests. To measure if

these differential geographic patterns actually do exist between individuals who give directly to

presidential nomination campaigns as opposed to super PACs, we used outside group and

candidate reports filed with the FEC during the 2016 presidential preprimary. Using spatial

regression analysis, we find that there is a relationship between both types of contributions

within a given community. There is also a relationship between both median income and levels

of income inequality within a county and contributions to official campaign organizations.

Surprisingly though, we find a negative relationship between both median income and income

inequality and contributions to super PACs once the model is fully specified. This finding

highlights the need for additional studies about the backgrounds and motivations of presidential

super PAC donors.

1

Introduction

An exigent concern for political scientists in recent years is to determine whether the U.S.

political system values the interest of the affluent over that of middle and working-class voters

(Bartels 2008; Gilens 2012). One commonly identified cause of this disparity is the U.S.

campaign finance system which relies heavily on large contributions from affluent donors

(Gilens 2012; Confessore, Cohen and Yourish 2015), though, as Bonica et al. (2013) point out,

the picture is a complex one. Whether campaign contributions “buy” votes or set the agenda on

certain issues is an intricate question that is not easily answerable (Hall and Wyman 1990;

Hadani and Schuler 2013). Even without a direct linkage between contributions and policy, the

perception of disproportionate influences still can delegitimize the political process and

discourage popular participation (Alex-Assensoh 2005).

One of the places where the disparity between the upper, middle, and lower classes is

evident is in the funding of the presidential nomination process. Prior to 2012, political action

committees (PACs) were almost invisible entities in presidential nomination contests, especially

during the preprimary stages. They typically contributed money later in the campaign to the

likely nominee and senators who lost but were returning to Congress. When outside groups did

spend money, it was in isolated incidents and the effects of the efforts on nomination outcomes

were mixed (Fowler, Spiliotes, and Vavreck 2004).

The Supreme Court’s ruling in the 2010 Citizens United v. Federal Election Commission

(FEC) case allowed PACs to participate directly in the political process. This case ruled that the

2

limits on electioneering by outside groups imposed by the Bipartisan Campaign Reform Act of

20021 violated the First Amendment free speech rights of these organizations. More specifically,

this case allowed for outside organizations (which are not limited in terms of the amount of

money they can receive from an individual donor) to participate with fewer restrictions in

fundraising and electioneering expenditures than official campaign organizations set up by

candidates (Dwyre and Braz 2015).

This article proposes that the growing role of these independent entities, commonly

known as “presidential super PACs,” in financing presidential nomination contests has

geographical implications as well. This study explores the extent to which individual donation

patterns can predict super PAC donation patterns, along with other known campaign finance

determinants, or if high-income areas simply dominate both processes in a similar manner. 2

Finally, this research assesses the extent to which these vary depending on party.

Using spatial analyses of U.S. counties, this study examines the 2016 presidential

nomination preprimary process.3 By doing so, this study advances the political geography of

campaign finance literature by discerning the differences in the direct and super PAC donor

pools at this early stage of the campaign. Previous research has demonstrated that differences in

the electoral order of states in the presidential nomination process and the geographic bases from

1 The Bipartisan Campaign Reform Act is also commonly known as “McCain-Feingold,”

2 While super PACS may be involved in a variety of contests and activities, we limited our

analysis to those who spent money on candidate advocacy, either positive or negative, in either,

or both, of the two parties’ 2012 and 2016 presidential nomination contests.

3 This is the year prior to the Iowa Caucuses, e.g., 2011 for the 2012 nominations.

3

which candidates draw electoral support can lead to different policy outcomes such as increased

levels of spending on government projects in that state (Taylor 2010; Berry, Burden, and Howell

2010; Hudak 2014).

Presidential Nomination Campaign Finance Prior to and After Citizens United

Following the Watergate Scandals of the 1970s, Congress implemented several reforms

designed to improve public disclosure and regulate the influence of large individual donors in

presidential elections. To ensure that all viable candidates have the financial means to compete, a

voluntary matching system was put in place that subsidized small loan donations by matching

contribution under $250 if candidates agreed to accept restrictions, such as caps on spending in

individual states and total nationwide expenditures, to receive the matching funds. One of the

concerns that Congress addressed in the Revenue Act of 1971 was to encourage candidates to not

simply raise money in one or two locations, but instead required them to raise at least $5,000 in

small donation (i.e. $250 or less) from at least 20 states, which is required to qualify for

participation in the matching funds system.4

Several changes during the late 1990s and early 2000s began to erode the voluntary

matching system and increased the role that large contributors had in fundraising for the

candidate’s campaign organizations (Malbin 2006). Still, these donors were limited to

contributing $1,000 in a presidential nomination contest prior to 2002 and to a cap of $2,000,

adjusted for inflation in each contest, after the passage of McCain-Feingold. The Citizens United

decision though created a loophole by which wealthy individuals could give sums significantly

more than $2,000 to presidential super PACs running supplementary campaigns on behalf of a

4 Alexander (1976) provides a good contemporary account of campaign finance reform.

4

candidate that were independent of the official candidate (Dwyre and Braz 2015), though

Christenson and Smidt (2014) and Katz (2016) have documented that their spending patterns

closely mirrors that of the official campaign organizations. Even though the voluntary matching

system was showing cracks (Adkins and Dowdle 2008) , these factors have tilted the playing

field to such a degree that even candidates who relied on small donations and were strong

advocates of public financing of elections, such as Bernie Sanders, rejected it (Kiely 2016).

It is important to note that none of the individuals who relied on these entities for a

sizeable majority of their fundraising support in 2016, such as Wisconsin Governor Scott

Walker, former Florida Governor Jeb Bush, and former Texas Governor Rick Perry, fared well

in that presidential nomination contest. Still, even though the impact of super PAC spending is

questionable, there is little disagreement by observers (Smith and Powell 2013; Dawood 2015)

that this increased electoral activity by super PACs has allowed them to play more of a role in

the electoral process while diminishing the role of political parties and traditional PACs (Smith

and Powell 2013). Furthermore, this type of political organization has the potential to be even

“louder” in presidential races than political parties and traditional PACs who have in the past

devoted most of their resources to state and congressional races (Stratmann 2005).

This study proposes that the growing reliance on presidential super PACs may have a

geographical aspect as well. Both Brown et al. (1995) and Gimpel et al. (2006) have

demonstrated presidential nomination and general election fundraising tends to be dominated by

a few urban areas. In recent years, fundraising in the crucial early stages of the presidential

nomination process for the candidates’ campaign organizations have already become

increasingly dominated by a few states (Sebold et al. 2012) . We will address whether

presidential super PACs have exacerbated this trend toward a geographic concentration of money

5

during the 2016 Republican and Democratic presidential nomination contests and further

heightens their dominance in the presidential preprimary. For our study, we limit our analysis to

fundraising in the year prior to the election, also commonly referred to as the preprimary season,

or the “money primary,” (e.g. the entire year 2015 represents the preprimary period for the 2016

presidential nominations). We focus on this period because it tends to be an accurate predictor

of who will eventually win their party’s nomination because it influences the views of the media,

the political elite and the public as to candidate viability prior to the Iowa Caucuses (Adkins and

dowdle 2002; Goff 2004; Smidt and Christenson, 2012) as well as providing well-financed front-

runners to survive potentially devastating early defeats such as George W. Bush suffered in the

2000 New Hampshire Primary (Adkins and Dowdle 2005) . The latter benefit has become

increasingly more important as more states move their contests earlier in the election year,

making money raised during the nomination year itself too late in arriving to influence the most

important contests (Mayer and Busch 2004; Adkins and Dowdle 2005). And as Malbin (2006b)

concludes, small donors, unlike their big money counterparts, typically wait well into the

election year to give to their candidates.

To examine the possible implications of this rapidly changing system of financing

presidential nomination contests, we will first summarize previous studies addressing geographic

trends in presidential fundraising and the literature examining contributors at the individual level.

Then we will address how these factors work together to provide us with a model that address

the geographic disparity between those areas in which individuals give to presidential super

PACs as opposed to those areas where contributions are limited to donations directly to the

candidate organizations themselves or where neither type of contribution is likely to occur.

6

The Geography of Presidential Fundraising During the Preprimary Season

If winning politicians value these early dollars in the way that they value votes, we argue

that these trends may also have geographical effects as well on public policy. Political scientists

have conducted numerous studies detailing how the effects of differential levels of electoral

support in presidential primaries influences the decisions of politicians. Gurian (1986) concludes

that presidential nomination hopefuls can identify potential states that might be favorable to their

candidacy and then direct campaign resources to these areas. Subsequent research also

demonstrates that winning presidential candidates reward these states with greater appropriations

and other favorable policy decisions (Taylor 2010; Berry, Burden, and Howell 2010; Hudak

2014). Obviously certain states such as Iowa and New Hampshire benefitted concretely in areas

such as ethanol subsidies because of their crucial positions in these contests (Squire 2008).

It also seems reasonable to believe that they also reward groups and areas that have

backed them financially as well.5 Once again, a number of works have looked at how geographic

patterns in financing presidential elections. Scholars have identified geographic islands of

disproportionate levels of financial support for presidential general election fundraising (Gimpel

et al., 2006) and for the preceding presidential nomination period for both parties (Brown et al.,

1995; Mitchell et al. 2015). These areas tend to be located on either coast and in Texas cities

such as Austin with higher income levels and greater urbanization, even controlling for

differences in population.

5 We do not test this proposition here, since as Hudak (2014) has demonstrated it with

establishing the tie between electoral support and policy decisions, the subject is best answered

in a book-length work.

7

These previous studies focus exclusively on contributions made directly to the official

campaign organizations for individual candidates. However, they do not measure money to

independent groups that may be conducting supplementary campaigns independent of these

office-seekers. As we will discuss in more detail later in this article, the presence of independent,

outside groups tended to be minimal before 2012. The Citizens United decision though created

the potential for this indirect financial support for presidential candidates to play a much larger

role in these contests because it clearly signaled to potential donors, groups and candidates that

limiting contributions to these outside groups and spending was unconstitutional. Since wealthy

potential donors may be more likely to be located in areas with a greater concentration of wealth,

we propose that donations patterns should be disproportionately higher in these areas.

Why Utilize a Political Geographic Framework Instead of an Individual-Level Approach to

Understanding Super Pac Donors?

A logical question to pose is whether donors are best examined as individuals rather than residents

of a larger community. Even within the most affluent and politically active communities, they are

likely to be a small minority of residents. Why should we look then at this question in this manner?

The first reason is one that we have previously mentioned: Politicians reward those states

that have supported them during the nomination process (Hudak 2014). However, it is important

to remember how few individuals vote in these contests. For example, Ted Cruz’s 51,000 voters

in the 2016 Republican Iowa Caucuses, which is about three percent of the state’s population, set

a record for the most votes any individual has ever received in either party’s caucuses. In this case,

politicians recognize that a relatively small number of individuals can matter.

The second reason is the lack of individual-level information about these donors. In 2012,

the FEC stopped requiring that campaigns and other organizations report information for

8

individual donors such as the occupation of donors and their street address. However, the FEC

still requires donors to report their zip code, which allows us to look at characteristics of the

community in which they live.6

Still, we believe the individual-level research can have a significant impact in terms of

geographic trends among areas where presidential super PAC donors are more likely to reside.

While some knowledge exists regarding the geographical trends in contributions to presidential

nomination (e.g. Mitchell et al. 2015) and general election campaigns (Gimpel et al. 2006), there

has been no scholarly assessment of the contribution patterns for presidential super PAC donors

that participate in these contests. However, a number of studies have documented factors that

influence contributions made directly to aspirants running for presidential nomination.

Methodology

Political Geographic Factors

For our first independent variable, we include the total amount given to presidential

campaigns within a county per capita (table x shows a summary table for each variable used in

this study). This factor examines whether the donations come from areas with a high amount of

donations to the official campaign organizations set up by presidential candidates. Fundraisers,

whether working for presidential campaigns or super PACs, have finite time and money to spend

on donor solicitation, and they must maximize their efforts by focusing on areas and donors that

6 For this paper, our examination of contribution patterns occurs at the county level because

some of the information we will utilize in this study is not currently available at the zip code-

level.

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will provide the greatest payoff. Therefore, they focus a great amount of time on fundraising in the

places they will be most successful (Adkins and Dowdle 2002) and seek out those habitual

contributors who contribute the maximum amount each election (Brown, Powell, and Wilcox

1995). We believe these patterns will drive the solicitation patterns of fundraisers from presidential

super PACs during the preprimary period, which is an important consideration since few people

contribute “large,” unsolicited donations (Brown et al., 1995; Rudolph and Grant 2002). While

this trend may be changing with the increased usage of the Internet and social media to reach out

to new voters and contributors, most individual donations through this medium have been small

contributions (Malbin 2013).

In part, however, we look at this factor because we want to be able to determine a key

question: Are there are significant differences between communities in which super PAC donors

live compared to the areas in which their counterparts contributing directly to presidential

candidate organizations live? If there is no difference, super PACs simply buttress the already

existing political advantages that these communities have because of their support to presidential

candidate organizations. However, a significant difference means that there are communities that

also may benefit from this additional form of electoral support.

We also want to control for interest in this election and propensity to contribute. To

measure these factors, we include: The total amount given by individuals per capita who gave to

one of the official presidential candidate organizations during 2015 for the 2016 preprimary. We

use this measures for two reasons. First, campaigns are more likely to target individuals with a

recent history of contributions (Hassell and Monson 2014). Second, this measure allows us to

control for both heightened levels of interest in these particular contests and the possibility that

some contributors may resemble Dalton’s (2013) “super citizens” who take advantage of the

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increasing numbers in which people can participate in politics beyond simply voting. In the next

section, we discuss other variables that likely will exhibit an influence.

Political Geography Hypothesis 1: There should be a positive correlation between the

amount donated to official presidential campaign organizations and the amount of money

given to Super PACs..

Political Geography Hypothesis 2: : There should be a positive correlation between the

amount donated to presidential super PACs and the amount of money per capita

contributed to a presidential super PACs in a given county.

Demographic Factors

The first set of indicators that we utilize to address the predictors of political geographic

contributions are demographic factors. Understandably, scholars have considered multiple

factors that can influence political participation; with one of the more widely accepted factors

being demographic factors (Souraf 1992; Rosenstone and Hanson 1993; Francia et al. 2003;

Verba, Schlozmna, and Brady 2006; Gimpel, Lee, and Kaminski 2006). To measure these

factors, we include: population, percentage white, and median age.

For population, we rely on the population density of the counties where presidential donors

reside as reported by the U.S. Census Bureau. Donors are more likely to come from areas that are

more heavily populated, (Brown, Powell, and Wilcox 1995; Gimpel, Lee, and Kaminski 2006;

Cho and Gimpel 2010; Bramlett, Brittany, Gimpel, and Lee 2011). In fact, the geographic

implications for these individual level decisions are reflected in a 2011 report by the Center for

Responsive Politics that tracks the participation of contributors by state and identifies California,

New York, and Texas as the top three states habitually who donate the most individual campaign

contributions to presidential nomination candidates in the 1996, 2000, and 2008 contests.

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Furthermore, people who live in populated areas are more likely to be solicited because of the

numerous interest groups that exist in the urban areas where the networks of people and channels

of influence are established. People in urban areas are more likely to belong to several groups,

providing more opportunity for fundraisers to solicit contributions (Rudolph and Grant 2002).

Living in a populated area also makes it more likely that a person will be stimulated to participate

in politics because of the socialization of participation that is emphasized by the social and political

networks in urban areas (Rosenstone and Hansen 1993; Verba, Schlozman, and Brady 1995;

Francia et al. 2003), although these findings are not concrete as it has also been found that

urbanization may actually decrease participation (Verba and Nie 1972).

We hypothesize that counties with a higher percentage of white residents and those with a

higher median age will have higher levels of contribution while there will be an inverse

relationship between the percentage of Hispanic residents and donor participation. For this

variable, we rely on the total number of individuals within a county that are Caucasian and the

median age of the residents, as reported by the U.S. Census Bureau in the most recent Census

reports. Race tends to play elements in who is solicited to contribute. Both Sorauf (1992) and

Brown et al. (1995) find that campaigns are more likely to solicit white and older donors. King

(2009) demonstrates that even in Barak Obama’s 2008 campaign, which garnered a record number

of small donations and was greeted enthusiastically by many African-American voters, had a

difficult time in raising money from African-American donors. Similarly, previous studies

(Gimpel et al. 2006; Brown et al. 1995; Francia 2003) show a relationship between certain

demographic factors, both at the individual and community levels, such as age and donating to

campaigns. We propose that higher per capita levels of education within communities should

increase the number of donors. Verba, Brady and Nie (1993) found that better-educated citizens

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participate to a greater extent in politics. Donors also do not exist in a vacuum, and fundraisers are

more likely to target potential individual donors who are highly educated professionals for

solicitation (Souraf 1992; Brown, Powell, and Wilcox 1995). For these factors, we include the

total number of residents with college degrees and the median age of the county residents. Finally,

the median age per county can predict that a positive correlation will exist between median age

and the number of contributors.

Demographic Hypothesis 1: There should be a positive correlation between population

density and the amount donated in a given county per capita.

Demographic Hypothesis 2: There should be a positive correlation between percentage

of white residents and the amount donated in a given county per capita.

Demographic Hypothesis 3: There should be a positive correlation between the average

age of residents and the amount donated in a given county per capita.

Demographic Hypothesis 4: There should be a positive correlation between the

percentage of residents with college degrees and the amount donated in a given county

per capita.

Economic Factors

Scholars have also considered other actors that can influence political participation; with

one of the more widely accepted factors being economic factors (Souraf 1992; Verba, Brady and

Nie 1993; Brown, Powell, and Wilcox 1995). To measure these factors, we first include household

income, and wealth inequality. The economic variables we look at measure income levels in the

counties where these donors reside. For this variable, we rely on median household income by

county as well as the Gini coefficient wealth inequality measure as reported by the US Census.

Badger (2013) reports that, in general, campaign donations typically come from communities

13

where incomes and accrued wealth are higher than the national average. Mitchell et al. (2015) also

demonstrate that wealthy urban centers such as the greater Houston, Austin, and New York City

metropolitan areas are one of the primary reasons why these three states have played such an

outsized role in funding recent presidential nomination contests. Verba, Brady and Nie (1993) also

found that the wealthier citizens participate to a greater extent in politics, and Brown et al. (1995)

find that donors to presidential campaign organizations tend to be disproportionately upper-middle

and upper class individuals. We expect that as the median income rises, the number of contributors

should as well. However, an accurate measure of community income can be tricky to establish

since donors are typically atypical in their communities (Mitchell et al. 2015). Even if donations

are more frequent from New York City or Austin, the average resident of even the most affluent

cities typically does not contribute. To address the complexities of these issues, we utilize two

variables: average county income and income inequality. We believe that as both increase, that the

number of affluent people, and potential super PAC donors, in that community should increase.

Economic Hypothesis 1: There should be a positive relationship between the median

income and the amount donated in a given county per capita.

Economic Hypothesis 2: There should be a negative relationship between the level of

income inequality and the amount donated in a given county per capita.

Data and Methods

Data Collection

In this study, we use the Federal Election Commission (FEC) administrative records for

contributions made to presidential candidates and to super PACs in 2016 during the presidential

preprimary. To perform our analyses, we geocoded each FEC record of contributions to

presidential candidates and super PACs. The FEC stopped reporting on specific addresses of

14

contributors, and instead, now only reports the city, state, zip code, and amount donated along

with the donor’s name. Therefore, rather than geocoding into individual points (specific

addresses), we geocoded the contributions into polygons, which took the form of counties in our

analysis. Upon doing this, we produced descriptive statistics, including the total number of

contributions per county, the total amount donated by county, in addition to various other

measures.

Data Analysis

For our dependent variables, we use FEC records to identify (1) the aggregate amount of

money contributed to super PACs per capita by county in the 2016 preprimaries and (2)

the aggregate amount of money contributed to super PACs per capita by county. We then

examine both variables as well by looking at the total number of contributions to Republican

presidential candidates per capita and the total number of contributions to Democratic

presidential candidates. Our independent variables were discussed above in the previous section.

To examine the extent to which individual donation patterns can predict super PAC

donation patterns along with other known campaign finance determinants, we rely on spatial

regression and OLS modelling. Typically, OLS models are not appropriate due to the presence of

spatial autocorrelation (Scala, Johnson, and Rogers 2015). Spatial models help control for the

likelihood of spatial autocorrelation.

Two considerations are of fundamental importance in spatial analyses, the type of spatial

model employed and the spatial weights matrix specification. First, for modelling, spatial lag,

spatial error, and geographically weighted regressions are commonly used models. A lag model

is consistent with a diffusion or contagion effect, or the concept that a neighbors’ behavior has a

direct effect on an individual’s behavior and not the result of “measured or unmeasured

15

variables” (Cho and Gimpel 2012, 453) and can tame spatial autocorrelation (Scala et al. 2015).

Conversely, an error model makes the assumption that spatial patterns are caused by

“unmeasured variables” (Cho and Gimpel 2012, 453). Geographically weighted regression

(GWR) is often the preferred choice rather than the spatial lag or error model and takes into

account that geographic neighbors can vary by size (Cho and Gimpel 2012).

Spatial modeling choices should not be theoretically based, but driven by the proper use

of statistical diagnoses (Tam Cho and Gimpel 2012). Preliminary diagnostic tests of the

LaGrange multipliers reveal that OLS model can be used, but the robust estimators do suggestion

spatial dependency might be occurring. Furthermore, our Breusch-Pagan test suggests

heteroskedasticity is occurring. Thus, ultimately we rely on a spatial lag model. Campaign

finance scholars rely on a similar modeling approach used by used by other campaign finance

studies (e.g. Gimpel, Lee and Kaminski 2006; Tam Cho 2003). We use this model to control for

the likely influence of spatial dependency among the various geographic units, which is likely

given that we are examining the geography of campaign finance (Tam Cho 2003; Gimpel and

Schuknecht 2003; Tam Cho and Gimpel 2007; Cho and Gimpel 2012; Cutts et al. 2014). As

Calvo and Escolar (2003, 195) conclude “(c)ontrolling for spatial effects means modeling the

assumption that values in adjacent geographic locations are likely to be linked to each other by

some underlying spatial structure.”

Second, the spatial weight matrix specification is another consideration. How the weight

matrix is specified is important when using spatial analysis (Tam Cho 2003), however, these

matrices may be difficult “to reconcile these specifications with a substantive story or theory”

(Tam Cho 2003, 372). Gimpel, Lee, and Kaminski (2006) relied on the eight nearest neighbors

16

approach weight matrix when analyzing zip codes.7 Other researchers have employed different

weight matrices, including the inverse distance model set at 50 miles (Tam Cho 2003). However,

these studies rely on zip codes, which takes on a different spatial process than counties, which

have uniform borders rather than distance and manifest themselves in contiguity. Contiguity

means two features such as counties share a common border of “non-zero length” (Anselin and

Rey 2014, 36).

For our study, we expect there to be spatial dependency among neighboring counties due

to similarities in many economic, demographic, and campaign contribution features. Therefore,

we defined our weight matrix with queen contiguity, which defines space as two features that

share a common border and vertex (Anselin and Rey 2014). However, specifying weight

matrices can be an arduous process, so we also employed other matrices, including the nearest

neighbor’s approach with varying levels of defined neighbors, the inverse distance model (which

considers a distance decay effect) with the band width set at varying levels.8 Queen contiguity is

the matrix that fits best with our data given the polygon nature of counties.

Results

Table 1 reports the results of our baseline OLS and spatial lag models. The baseline

OLS model is intended to show, define, and determine what error correction model to include.

The next model is considering all the campaign contributions combined.

{INSERT TABLE 1 HERE}

7 To calculate spatial error and the weight matrices we used GeoDa, a statistical software created by Luc Anselin. It

is available at http://spatial.uchicago.edu/ 8 For a detailed discussion of weight matrices, refer to Anselin and Rey (2014).

17

According to our baseline individual donations OLS model, several variables are

significant. For PAC donations, each additional dollar per capita donated to a PAC increases a

county’s individual donations by 1 dollar. Looking at income, each increase in household income

within a county increases the number of PAC donors by 3.5. Wealth inequality is also

significant. For each unit increase in the Gini coefficient, donations to candidates increase by a

relatively large value. For our next model, the results are similar but wealth and inequality are

negative. Due to the likelihood of spatial dependency, we also ran a spatial lag model. The

results are similar to the OLS model.

Finally, we ran a geographically weighted regression to assess the raw effect of PAC

donations on individual donations (Figure 1) and individual donations on PAC donations (Figure

2). Geographically weighted regression assesses how relationships vary spatially among

differing locations. Put differently, this model assumes that results are non-stationary and move

away from the global value at differing points. This is unlike OLS, where the results are linear.

Thus, these maps show where there is a strong association between individual donations and

PAC donations.

Discussion

The initial results of this study seem promising and we believe they contribute to the

understanding of the determinants of campaign contributions and the relationship between the

direct money raised by candidates and the indirect money raised by the relatively new source of

campaign fundraising and spending, the super PACs. While recent previous work (Gimpel, Lee,

and Kaminski 2006; Mitchell et al. 2015) demonstrated that contributions to presidential

campaigns have a narrow geographic spread, we find that contributions to super PACs during the

preprimary period are concentrated in even more relatively isolated “islands” of wealth. Nearly

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seventy percent of zip codes contain zero super PAC contributors. There actually seems to be

almost a contingent relationship between the need to have direct contributors before super PAC

activity can occur. The bivariate correlation is so strong that only one-in-five hundred zip codes

have some presidential super PAC activity but record no direct contributions to campaigns. If these

behaviors were statistically independent, ten percent of zip codes should fall into this category.

However, the factors that influence variation in the amounts being contribute to both types

of organizations are less clear. While we tested a number of demographic variables, the only

statistically significant independent variables were the median income of a county and income

equality. Like Mitchell et al. (2015), we found a positive association between county level income

and direct contributions. We also conclude that higher levels of income inequality within

communities correlate with higher amounts being contributed to campaigns. This relationship does

seem to validate the findings that communities that tend to contribute a large amount of money to

campaigns are affluent islands of upper-class and upper middle-class wealth.

In contrast, we find that a negative relationship between the two county-level income

variables (i.e., median income and the Gini coefficient) and the amount of money contributed to

presidential super PACs, once other factors are accounted for in our model. There are important

caveats to these preliminary findings especially that these results only include one election cycle.

A number of avenues exists in future research to improve on these findings. One of the short-term

limitations we faced was that most of the data we had was at the county level, which is a unit that

is too populous and too diverse in some urbanized parts to give us a completely accurate measure

of what was happening in these communities. In the future we will test the spatial error OLS model

utilizing zip code level data and believe this effort will provide us with better insight into the

existence of these islands of high contributor activity. We would also like to include variables that

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measure different aspects of political culture and institutional structures in future studies, as well

as incorporating some measure of donor solicitation and outreach. However, it seems evident that

there is still much that we do not know about the background and motivations of presidential super

PAC donors. The most important step is probably to survey these donors in a manner similar to

Francia et al.’s (2003) study of congressional donors. Only by combining macro-level and

individual-level data will we get a better picture of who gives to presidential super PACS and why

they do so.

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Table 1. Spatial Models Employed in this Study

Table 1. Regression Models

Variables Spatial OLS

Ind.

Spatial OLS

PAC

Spatial Lag

Ind.

Spatial Lag

PAC

Pac Donations 1.00*

(.003)

----- 1.00*

(.004)

-----

Individual Donations ----- .95*

(.003)

----- .95*

(.003)

Income 3.55*

(1.30)

-3.25*

(1.26)

3.55*

(1.30)

-3.25*

(1.26)

Inequality 763800*

219162

-770948*

(213474)

763748*

218880

-770847*

(213221)

Population Density -.678

(2.16)

.468

(2.11)

-.681

(2.16)

.470

(2.11)

Education -1555.22

(70153

6922.1

(68341)

-1517

(70062)

6905.1

(683255.2)

Nonwhite -8.029

(388.5)

16.07

(378.5)

-8.20

(388.1)

16.2

(378.04)

Age 623.9

(1602)

-250.3

(1561)

627.8

(1601)

-254.2

(1560)

Constant -425243)

(123992.1)

412302

(120793)

-425351

(123846.5)

412371.6

(120793)

Rho ----- ----- -.0008

(.008)

-.0006

(.008)

N 3194 3194 3194 3194

Adj R-Square .95 .95 .95 .95

Jarque-Bera .00 .00 .00 .00

Robust LM (error) .89 .80 --- ---

Robust LM (lag .96 .88 --- ---

28

Figure 1. GWR of Dependent Variables Individual Donation Amount Per Capita and Pac

Donation Amount Per Capita

Figure 2. GWR of Dependent Variables PAC Donation Amount Per Capita and Individual

Amount Per Capita