Power of the PAC

THE POWER OF THE PAC
The Effect of District Income on PAC Spending
Mike Holtz and Jonah Ragsdale
The Politics of Congressional Elections
12/15/2014
An Analysis
Does the affluence of a districtchangethe way money is funneled into its elections? An analytic
study of income and PAC contributions,in relation to the origin of contributions

1
Intro
$3,665,416,368. That’s how much was spent in the 2014 federal elections (Choma,
Novak 2014), equivalent to giving every man, woman and child in the United States 11
McDonalds double cheeseburgers. Each year, billions of dollars are spent on federal elections -
paying for everything from TV spots, to field offices, to staff salaries. And it has a major effect,
too. According to the Center for Responsive Politics, 8 out of 10 Senate candidates who spend
the most money win their elections. In the House, that number is 9 out of 10 and growing
(Biersack 2010). While it is possible that these statistics show correlation rather than causation,
there is no doubt that money matters in politics.
One of the fastest growing areas in campaign finance is the realm of political action
committees (PACs). Rising to prominence in the 1970s, PACs were created to circumvent
certain campaign finance laws and give outside groups and individuals a way to get involved in
the political process. As restrictions on PACs have loosened over the years - especially through a
string of high profile Supreme Court cases - they have started to play a bigger role in American
politics. According to the Center for Responsive Politics, outside groups were responsible for
13% of all reported election spending in 2014 (Choma 2014). In some races, PACs actually spent
more than the campaigns themselves.
With major influence, elections aren’t the only area PACs are gaining ground in. Using
large donations as a bartering tool, PACs provide a lobby in a way that extends further than their
influence on voter choice. Alan Simpson notes, “Too often, Members' first thought is not what is
right or wrong or what they believe, but how will it affect fundraising. Who, after all, can
seriously contend that a $100,000 donation does not alter the way one thinks about—and quite
possibly votes on—an issue?” (Has Money 2014).

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With PACs’ impact growing, it is important to understand where they chose to spend
their money and which races benefit the most. Why do certain candidates receive more PAC
money than others? What explains the growing out-of-district donor bases for many candidates?
Few people have looked to district income as a possible factor in the PAC decision-making
process. Does the wealth of a district influence the amount a given candidate will receive? Are
candidates from poorer districts more likely to seek PAC money to compensate for smaller donor
bases? We believe that, upon further exploration, analysis will show this to be true. In our
research, we seek to find this answer - to explain one of many possible reasons why PACs
contribute the way they do.
Theory
The battle for a voice in Washington is one that is fought by more than just a candidate
running for Congress. PACs and interest groups also play an extremely important role,
contributing to the election process. Every campaign needs funding, and tracking the source of
that money provides valuable insight into who holds influence in the halls of Congress.
In the world of campaign finance, as the amount of money has increased, the number of
donors has also decreased in response to the Citizens United ruling. According to the Center for
Responsive Politics, since 2010 the number of individuals giving $200+ has decreased from
817,464 to 666,773 (Choma 2014). In a 2012 study of campaign contributions, Harvard Law
professor Lawrence Lessig found that only 0.26% of the population donated $200 or more to
political campaigns, even though these donations account for nearly ⅔ of all campaign
contributions (Has Money 2014). According to Lessig, there are less relevant political donors in
the United States than there are people named “Lester.” More interesting, perhaps, is where these
donors live. These donors are centered in a limited number of urban zip codes, namely in New

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York, Washington, Chicago, and Los Angeles. For candidates in these districts, there is a large
donor base to turn to, but for candidates in less wealthy districts, finding constituents to donate
any money - let alone the millions needed to win an election - can be difficult. Though these
districts may have less expensive media markets and material costs, there is still a hefty price of
running any campaign. We suspect that these candidates find other ways to make up for the lack
of donations. These may include dipping into their personal fortunes or courting campaign
donors from other districts. However, until 2014, donors faced aggregate limits that prevented
them from donating to dozens of campaigns. With this in mind, we believe candidates in lower-
income districts are more likely to turn to political action committees to make up their
deficits. With more value to their money, PACs - including party committees, issue advocacy
organizations, and independent-expenditure groups (i.e. Super PACs) - can take advantage of
these low income areas to potentially earn a greater return on their investment by a low cost
election win.
If we are correct, and candidates in lower-income districts are more likely to seek out
PAC money, this can shed light on the legislative priorities of candidates, the nature of campaign
funding and how PACs allocate funds. However, before anyone can explore the implications of
an income-PAC money connection, it is important to first discover whether such a connection
exists. To do this, we will compare House campaign finance data from 2008, 2010 and 2012 to
district income data to see if our hypothesis holds its ground.
Literature Review
While research on the link between district income and PAC donations is scarce, separate
research on both PAC behavior and district income sheds a light on the possible connection
between these two factors.

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Though they have only been major players in the American political arena for the past
few decades, PACs have established a lasting niche in the world of campaign finance, and have
been the subject of intense examination. On a basic level, FEC records show the growth of
PACs, from the first PAC during World War II - creatively named the “Political Action
Committee” - to a boom of PACs in the 1980s, to the rise of “Super PACs” after the Citizens
United decision in 2010.
Throughout this time, academics have also weighed in - focusing specifically on what
makes PACs donate to certain candidates. Some of the most prominent research relates to
incumbency. According to a study by Professors Kevin Grier and Michael Munger in the Journal
of Politics, over 80% of PAC donations in the 1980s went to incumbents (Grier 1991, 617).
Subsequent studies have reaffirmed this notion of a seeming incumbency advantage for
donations (Endersby 1992). Other research has found additional factors. For instance, a 1994
study by Professors Thomas Romer and James Snyder found that committee assignments and
committee chairmanships were huge factors in attracting PAC donations. Romer and Snyder
found such a strong correlation between PACs and certain committees, that sometimes PACs
would drop candidates after years of support if they decided to switch committees. This data is
supported by Grier and Munger, who, through a series of chi-square tests, shed light on a strong
relationship between certain industries and donations to relevant congressional committees.
Since the 1980s, various studies have found correlations between PAC donations and
voting records, party ID, candidate viability, and other factors. While some PACs have been
upfront about their criteria for choosing candidates, many are shrouded in secrecy and their
methods are often unknown. Scholars have sought to plug in the holes and draw conclusions,
leading campaign finance expert Janet Grenzke admits there is “considerable controversy”

5
surrounding certain claims, and that many of the conflicting studies are subjective in nature
(Grenzke 1989, 2). In essence, while studies have uncovered a lot about the mindset of PACs,
much is still unknown about why PACs spend money the way they do and what impact it has.
To understand any correlation between PACs and district income, it is important to not
only understand the mindset of PACs, but to also understand the role of income in the election
process. Currently, the U.S. political system operates with a relatively small relevant donor base-
what Lawrence Lessig called the “Lester” pool. Less than 1% of Americans donate $200 or more
to political campaigns, accounting for over 80% of political donations (Has Money 2014). And
according to data from the Public Citizen’s Congress Watch, 86 percent of those contributions
are from households earning $100,000 per year or more. These donors are generally wealthy,
well educated, and concentrated in a small number of congressional districts. But, according to a
2008 study by James Gimpel and Frances Lee, what’s more shocking is how many districts do
not have major donor bases. “We were quite astonished to see that there are major sections of the
country that give almost nothing,” Gimpel said (Drutman 2008). “There are a great many
congressional districts where there just isn’t much wealth.” The key, according to Gimpel, was
cross-district donations.
Johns Hopkins Professor Lee Drutman pointed to this discrepancy between districts,
noting that, as of 2004, 4 in 5 congressional districts received more money from out of district
than from within their district (Drutman 2008). And in 18% of congressional districts, over 90%
of donations are from outside donors. This trend dates back to at least the 1980s, and has not
changed much since (Grenzke 1988). Gimpel’s 2008 study - one of the most in-depth studies on
the topic - stressed not only that donor’s give money across state lines, but that “inter-district
funding flows are guided by partisan networks” (Gimpel 2008). This notion, alongside aggregate

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limit laws in place until early 2014, suggest that PACs and other political organizations could be
responsible for helping prop up candidates in low-income districts. We hope to explore this
possibility further.
Data and Methods
Despite limited research on the topic, our hypothesis was formulated using reasonable
speculation that candidates in poorer districts receive less campaign donations from constituents.
As a result, one would expect PAC contributions to play a greater role as money from outside
districts is an increasing occurrence in districts across the country. Our testable hypothesis was
that districts with lower mean incomes have higher amounts PAC spending and contributions as
opposed to wealthier districts. We operated off the assumption that where there is less money,
money is worth more and therefore PAC money has more value in less wealthy districts.
In order to measure the relationship between district wealth and PAC spending as a whole,
building a financial representation of a district is necessary to conclude a significant impact.
While there are many economic variables that could be used to assess the affluence of a
given district, we chose mean household income as our independent variable. Mean income
measures the average household income of constituents. It has been used in previous studies of
geographic income levels, and serves as a general indicator of district wealth. To evaluate district
income levels, we turned to the United States Census Bureau. In its collection efforts, the Census
Bureau records household income levels from across the country, and calculates the estimated
mean income by congressional district. We gathered the mean household income data from every
congressional district during four congressional election years - 2006, 2008, 2010, and 2012. To
ensure consistency in our model, these numbers were then adjusted for inflation to reflect their
real value in 2012 dollars (US Census Bureau 2006-2012).

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Because our theory relies on a potential relationship with PAC spending, data on
spending by political action committees for each congressional district was collected as well.
This data was collected from Stanford University’s Database on Ideology, Money in Politics, and
Elections (DIME) (Bonica 2013). For these data points, we set specific guidelines for what
money would be considered in our formula. In order for PAC contributions to be considered in
the model, only money spent in general elections on candidates that participated in the general
election were considered. This included multiple third party candidates that received PAC money
in the general election, but excluded PAC money given in contested primaries. These figures
were then added to create an aggregate measure for each congressional district across the four
year analysis. Like the mean income, the PAC contributions were adjusted for inflation and
converted into 2012 value. In addition, the mean income and average PAC contribution were
calculated for the nation as a whole during each of the four years.
For our experiment, we chose to run three different linear regressions using PAC
contributions as the dependent variable and mean household income as the independent variable.
In other words, the variable being directly adjusted was the mean income of the district, while
the effect on PAC contributions was analyzed to determine possible connections. For all three
tests we used the same basic statistical model, while adjusting some features for outside factors
in the latter two tests. In the first model, we ran the two variables alone in a linear regression for
an unadjusted control. This pure model runs a smaller margin of error, and therefore provides the
most direct and unaffected relationship. The last two models, however, were adjusted for
possible discrepancies one might see across years and districts. The second model was adjusted
to reflect the differences across years, providing a less pure correlation with a similar margin of
error, but allowing for a potentially more realistic outcome than the first model. The final model

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was adjusted for the differences in congressional districts during each year. This increased the
margin of error significantly, but was the most realistic regression model due to its compensation
for variables not reflected in the original test.
Ultimately, all three models provide interesting insight into the income-PAC spending
relationship. In order to prove our hypothesis through these methods, we would have to see
significant and consistent negative correlation between the increase in district income and PAC
spending.
Results
TABLE 1 GOES HERE
In table 1, the three models and their results are shown in order along with their
adjustments (indicated in the lower rows). The total (N) remained constant with the number of
congressional districts analyzed in each test. The first row represents the linear regression
coefficients with the margins of error listed in parenthesis. The constants listed are the
contributions received by congressional candidates. We have indicated statistically significant
variables with asterisks.
In the first trial (the pure model), the regression produced a positive coefficient of 2.3
with a .9 margin of error. This result showed that rather than the negative regression we
expected, the correlation was positive as both fields increased. Instead of a decrease in income
leading to higher PAC contributions, an increase in district mean income led to increases in PAC
money being spent and contributed to a district. This result - though not drastic - is statistically
significant as it is well above the margin of error, showing that instead of a negative correlation,
there is evidence that a positive relationship could exist. Our dependent variable then produced
the constant 527,816.8 with a margin of error of 57,818.3 giving us once again a statistically

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significant result. As reflected in the graphs below, the pure model showed trend lines close to
horizontal with slightly positive slopes in 2006 and 2012. While this model did not agree with
our hypothesis, it did show a slightly positive relationship between the mean income and PAC
spending in congressional districts.
FIGURES 1-4 INSERTED HERE
While the first model was run without adjusting for outside variables, we chose to re-run the
model adjusting for certain differences that would potentially impact the relationship. The
second model was adjusted for potential differences between the four years comprising our data.
In doing this adjustment, we created a more consistent model than the first. This accounts for
factors such as the 2010 Citizens United decision or changes in district income over time. In this
second test, we found the results to once again be positive with a regression coefficient of 1.4.
However, the margin of error stays the same at .9 leading us to believe this outcome is no longer
statistically significant. While these results are similar to those of the first test, they are not
substantial enough to conclude that there is a relationship between the two variables. The
dependent variable, however, still remains statistically significant. With a constant of 478,333.5
and a margin of error of 55,113.4, we can still conclude that the outcome is significant enough.
The last test provides what is possibly the most realistic examination of an association
between the two data sets. In this test, not only did we adjust for the potential differences across
years, but we also adjusted for potential district differences across the years by accounting for
860 fixed groups. In doing this, not only do we add a more impactful adjustment, but we also
allow for a more realistic examination of both data sets. In this regression, we found a much
stronger correlation between mean income and PAC contributions with the regression coefficient
rising to 4.7. However, our margin of error significantly increased due to the adjustments,

10
leaving us with a stronger result but an inconclusive one as well. The margin of error of 3.5 led
us to believe that the relationship was still positive, but could not be considered a substantial
outcome. The constant in this model then fell to 301,161.7, but like the coefficient, was then
subjected to an extremely high margin of error at 233,236.3, once again leaving us with an
indecisive product. While maintaining a positive relationship, there is very little evidence to
support that our adjusted variables didn’t lead to the increase in the coefficient.
By examining all of these outcomes, it becomes clear our hypothesis proved to be
incorrect. While we predicted that poorer districts would receive more PAC money to fill
potential gaps in local donations, our results found the opposite to be true. In all three models,
the regression coefficients proved to be positive, rather the negative coefficient our hypothesis
suggested. While two of the coefficients were not statistically significant enough to prove that
positive correlation is definite, the fact that all three were still slightly positive sheds significant
doubt on the veracity of our hypothesis. While not conclusive, there is minimal evidence that a
negative correlation exists between mean income and PAC contributions.
While our adjustments led to different outcomes, our coefficient remained slightly
positive in the final two tests. And though the margin of error significantly increased in the last
test, the fact that the coefficient remained positive could be proof that this relationship will
remain positive as more data is included or as other variables and adjustments make the model
more realistic.
However, because the pure model without adjustments was the only significant outcome,
it is reasonable to assume that there is no evidence for a correlation between the wealth of a
district and the PAC contributions to any given congressional district.
Discussion

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Through this study, we had a chance to look at an area of American politics that is
growing rapidly but escapes the attention of most Americans: Campaign finance. We began with
a hypothesis that candidates in less wealthy districts - districts with few elite donors - would turn
to out-of-district PACs for funding. We predicted that lower-income districts would see more
PAC donations than their wealthier peers. And while our research did not rear enough evidence
to support this hypothesis, our results bring an interesting addition to the discussion on campaign
finance.
From other funding sources, to PAC decision factors, to imperfect testing methods, there
are several possible reasons why our hypothesis might not have held its ground, and they are all
worth exploring.
One of the main reasons why our data might not have shown PAC money flowing
heavily to low-income districts is because candidates in these districts turn to individual outside
donors instead. While PAC donations have been growing lately, it is important to remember that
they still account for less than 13% of all election donations (Choma 2014). The majority of
donors still give the old fashioned way - writing checks to individual campaigns. And though the
nation’s top donors are concentrated in only a select few congressional districts, research shows
that they donate to campaigns across the nation. As mentioned in the Literature Review, Gimpel
and Lee’s 2008 study found that over ⅔ of House campaign contributions came from out of state
(Gimpel 2008). Originally, we had brushed this evidence aside, in light of aggregate limits that
prevented donors from giving to more than a few campaigns. However, the major flow of outside
funding from high to low income districts, coupled with our findings that PAC donations are
likely not the main vehicle of these transfers, suggests that, despite aggregate limits, individual
donors have been able to successfully prop up candidates in less wealthy districts.

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This idea is particularly interesting since aggregate limits were abolished in 2014 with the
Supreme Court’s decision in McCutcheon v. FEC. In a personal interview, Sean McCutcheon,
the multimillionaire donor behind the landmark case, explained to us that he hoped the decision
would allow Americans like himself to contribute to dozens of elections across varying states,
claiming that “free speech benefits all Americans, regardless of income or political views”
(McCutcheon 2014). With this in mind, the McCutcheon case could lead to even more inter-
district donations.
Another factor that could have led to the lack of evidence for our hypothesis is the
various other considerations that influence PAC financing decisions. While it is possible, as we
predicted, that candidates from lower-income districts are more likely to seek PAC donations, it
is ultimately up to the PACS - rather than the candidates - to decide where their money goes.
With the dozens of possible components, including candidate ideology, viability, voting record,
and incumbency, district income is likely not at the forefront of the PAC decision-making
process.
A final element worth considering when reviewing our analysis is the way the experiment
was conducted. While our methods clearly showed no evidence of a correlation between district
income and PAC donation amounts, it is still possible that other dependent variables could have
yielded a different result. For instance, we could have examined the percent of total spending in a
district that came from PACs. It is important to realize that many lower income districts, like
rural Missouri, likely have lower campaign costs (i.e. building rents, supplies, ads, etc.) and
cheaper media markets than a district in New York or San Francisco. So, if a candidate in a low-
income district received $1 million in PAC donations, compared to $2 million in a high-income
district, the high-income candidate would be associated more with PAC spending by our metrics,

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even though the low-income candidate may have taken a larger percent of her donations from
PACs.
In the final analysis, we believe that this study is a potential launching point for further
research. We sought to examine an area of campaign finance where little previous research has
been done, and found interesting results. While our hypothesis proved to be incorrect, our
analysis shed light on the cross-section of income and PACs. In the future, by looking at PAC
contribution percent’s, or simply examining post-McCutcheon data from 2014 and beyond, we
believe that there is room for more exploration, more discovery, and deeper insight into the ever-
expanding world of campaign finance.

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References
Biersack, Bob. "The Big Spender Always Wins?" Open Secrets. Last modified January 12, 2010.
Accessed December 15, 2014. http://www.opensecrets.org/news/2012/01/big-spender-
always-wins/.
Bonica, Adam. "Database on Ideology, Money in Politics, and Elections." Stanford University
Libraries. Last modified 2013. http://data.stanford.edu/dime.
Choma, Russ. "Money Won on Tuesday, But Rules of the Game Changed." Open Secrets. Last
modified November 5, 2014. Accessed December 15, 2014.
http://www.opensecrets.org/news/2014/11/money-won-on-tuesday-but-rules-of-the-
game-changed/.
Choma, Russ, and Viveca Novak. "Overall Spending Inches Up in 2014: Megadonors Equip
Outside Groups to Capture a Bigger Share of the Pie." Open Secrets. Last modified
October 29, 2014. Accessed December 15, 2014.
http://www.opensecrets.org/news/2014/10/overall-spending-inches-up-in-2014-
megadonors-equip-outside-groups-to-capture-a-bigger-share-of-the-pie/.
Drutman, Lee. "The Rise of the Political Donor Class." Pacific Standard. Last modified August
26, 2008. Accessed December 15, 2014. http://www.psmag.com/navigation/politics-and-
law/the-rise-of-the-political-donor-class-4305/.
"Federal PAC." Human Rights Campaigns. Accessed December 15, 2014.
http://www.hrc.org/resources/entry/federal-pac.
Gimpel, James G., Frances E. Lee, and Shanna Pearson-Merkowitz. "The Check Is in the Mail:
Interdistrict Funding Flows in Congressional Elections." American Journal of Political
Science 52, no. 2 (April 2008): 373-94.
Grenzke, Janet. "Comparing Contributions to U.S. House Members from Outside Their
Districts." Legislative Studies Quarterly 13, no. 1 (1988): 83-103. Accessed December 7,
2014.
http://www.jstor.org/stable/pdfplus/439946.pdf?&acceptTC=true&jpdConfirm=true.
Grenzke, Janet M. "PACs and the Congressional Supermarket: The Currency is Complex."
American Journal of Political Science 33, no. 1 (February 1989): 1-24.
Grier, Kevin B., and Michael C. Munger. "Comparing Interest Group PAC Contributions to
House and Senate Incumbents, 1980–1986." The Journal of Politics 55, no. 03 (August
1993): 615-43.
"Has Money Taken Over American Politics?" National Public Radio 14 Mar. 2014: n.
pag. NPR. <http://www.npr.org/2014/03/14/288631511/has-money-taken-over-american-
politics>.

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McCutcheon, Shaun. Personal interview. 17 Apr. 2014.
Mean Income by Congressional District. N.p.: US Census Bureau, 2006-2012. Accessed
December 1, 2014. http://factfinder2.census.gov.
Romer, Thomas, and James M. Snyder, Jr. "An Empirical Investigation of the Dynamics of PAC
“Contributions.” American Journal of Political Science 38, no. 03 (August 1994): 745-69.

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Tables & Figures
Table 1
(1) (2) (3)
Mean income
(inflation-adjusted)
2.3*
(0.9)
1.4
(0.9)
4.7
(3.5)
Constant 527816.8*
(57919.3)
478333.5*
(55113.4)
301161.7
(233236.3)
N (total) 1726 1726 1726
N (groups) -- -- 860
Year fixed effects? No Yes Yes
District fixed effects? No No Yes
Entries are linear regression coefficients with standard errors (clustered on district in
parentheses). The dependent variable is the amount of PAC contributions received by
congressional candidates, adjusted for inflation. * indicate p < 0.05 (two-tailed tests).

17
Figure 1
This shows the plots for district mean income in relation to PAC spending for 2012 with the
trend line
0
500000
1000000
1500000
2000000
2500000
3000000
3500000
0 20000 40000 60000 80000 100000 120000 140000 160000
PACSpending
District Mean Income
2012 Mean Income in relation to PAC Spending

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Figure 2
trend line
0
500000
1000000
1500000
2000000
2500000
3000000
3500000
0 20000 40000 60000 80000 100000 120000 140000 160000
PACSpending
2010 Mean Income in Relation to PAC Spending

19
Figure 3
trend line
0
500000
1000000
1500000
2000000
2500000
3000000
0 20000 40000 60000 80000 100000 120000 140000 160000
PACSpending

20
Figure 4
trend line
0
500000
1000000
1500000
2000000
2500000
3000000
3500000
0 20000 40000 60000 80000 100000 120000 140000 160000
PACSpending

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