The Political Geography of Campaign Contributions in American Politics
James G. Gimpel
Frances E. Lee
Joshua Kaminski
University of Maryland
Department of Government
3140 Tydings Hall
College Park, Maryland 20742
Abstract: Here we examine the geographic origins of individual campaign contributions to the
Republican and Democratic parties and their candidates from 1992 to 2004. How potential givers
are situated in space matters to whether they contribute. Prospective donors are not only people
with resources and incentives to participate, but also those who are part of a network in which
social influence can be brought to bear in the solicitation of contributions. We show that there is
a geographic pattern to giving independent of wealth, age, occupation and other individual
characteristics that predict donations. We also show that the Republican and Democratic donor
bases are much more geographically similar than their bases of electoral support.
Key words:
campaign contributions, political parties, campaigns and elections, political
geography, spatial data analysis, social networks.
1
Scholars and reformers have primarily taken an interest in campaign
contributions because of concern about inequality of voice and influence. From this
perspective, campaign contributions can transform economic inequalities in society into
political inequalities in government policymaking, undercutting the basic democratic
principle of one person, one vote.
There are, however, other important reasons to study campaign giving.
Campaign contributions are not just a potential influence on elected officials, important
because of their effect on policy outcomes. Contributions are interesting in their own
right as a form of political participation, one that has great consequences for the conduct
of campaigns and the outcome of elections (Abramowitz 1988; Coleman and Manna
2000; Jacobson 2003; Herrnson 2003).
Research has explored how demographic
characteristics affect the propensity of individuals to contribute, as well as how
campaigns and campaign coverage mobilize contributions (Brown, Hedges, and Powell
1980; Francia et al 2003; Mutz 1995; Verba, Schlozman & Brady 1995).
But politics in the U.S. is affected by more than the preferences and behavior of
individuals. It is also shaped by geography. Indeed, the outcomes of presidential
campaigns and the composition of the Congress are determined by where people live as
well as by the preferences of individual voters. Parties routinely win control of the
Congress without gaining a majority of the total two-party vote in congressional races,
and presidents often win office without winning majorities of the national vote (Amy
2002; Campbell 1996). The key to winning is not just who votes, but where voters live.
2
And in order to win votes, parties and candidates need financial resources for their
campaigns.
The goal of this paper is to set campaign contributions in geographic
context in order to answer two important questions about campaign contributions.
First, where do parties and major party candidates raise their funds? Much ink
has been spilled over the course of the last few election cycles about the rivalry between
“red” and “blue” electorates and the extent to which the parties have either distinctive
or intersecting bases of mass support. Contributing is a demanding form of political
participation that is not uniformly distributed throughout the population (Brady,
Schlozman and Verba 1999; Verba, Schlozman, Brady and Nie 1993). While giving is
undeniably associated with wealth, there are those who give well beyond their means
and legions of affluent people who never contribute anything. If the major parties have
dissimilar geographic bases of electoral support, is it equally likely that they have
different sources of financial support? In an era in which the two major parties appear
to be well matched in their capacity to raise funds, determining where the gold is most
profitably mined is an essential contribution to practical and scientific knowledge about
politics.
Second, does geographic context exercise an independent influence on campaign
contributions? The geographic distribution of campaign contributions could be merely
an artifact of the geographic distribution of individuals with the resources and
inclination to give.
However, there is good reason to expect otherwise, that how
potential givers are situated in space affects their propensity to contribute. Individual
contributors commonly act as part of a local network; their contributions are a
3
consequence of social influences that prompt higher levels of giving (Brady, Schlozman
and Verba 1999; Verba, Schlozman and Brady 1995, chap 5; Tam Cho 2003). Candidates
and parties know this, of course. Campaigning and fundraising always involve a great
deal of traveling in order to tap into local social, party, governmental, and professionaloccupational networks, including physicians, lawyers, teachers, students, community
groups, and labor unions (Hinckley and Green 1996). If so, there will be geographic
patterns to giving that are independent of wealth, age, occupation, party affiliation and
other individual characteristics. Networks, campaigns, and fundraising efforts are all
set in geographic contexts, and areas where contributors are effectively mobilized will
donate more than would be expected on the basis of the attributes of its individual
residents alone.
We examine the geographic distribution of individual donors’ contributions to
parties and campaigns1 for each election year between 1992 and 2004, drawing on
official contribution data from the Federal Election Commission. These data provide us
with a unique vantage point for understanding the geographic distribution and origin of
contributions.
We find that—despite significant differences in the geographic
distribution of the parties’ mass support—the two parties draw on financial bases of
support that are quite similar geographically. Donors to both parties’ campaigns reside
primarily in densely populated, wealthy areas of the country, and each party
successfully taps these bases even when they are located in local or state political
1
The analysis includes contributions both to the two major parties and to candidates aligned with them.
We do not simply mean contributions to the two official party organizations, the RNC and the DNC.
4
contexts that are electorally inhospitable to the party.
Spatial regression models,
however, reveal that the distribution of individuals with the resources and inclinations
to give cannot fully account for patterns of giving. Both parties appear to draw upon
networks of donors that are tied together geographically, so that the map of giving is not
just a map of interested and efficacious individuals.
Instead, parties capitalize on
geographically based networks of contributors who give more than would be expected
on the basis of their individual demographic and political profiles.
Motives for Giving and the Relevance of Geography
Geography is likely to shape the behavior of campaign contributors because it
contextualizes the key motives that lead individuals to participate in politics. James Q.
Wilson (1973) distinguishes among three motives for political participation: material,
purposive, and solidary. People participate in politics in order to gain tangible benefits
for themselves (material), to achieve ideological or policy goals (purposive), and to enjoy
the psychological pleasures of working with like-minded people or rubbing shoulders
with “the movers and the shakers” (solidary). Geographic context and spatial proximity
are likely to shape individuals’ willingness to make campaign contributions for each of
these motives, despite the many advances in communications technology that have
facilitated the formation of more far-flung political organizations.
First, spatial proximity makes it more likely that people will perceive a common
material interest in government policy and organize to advance it. Recent studies have
shown that geographically concentrated firms are more likely to organize (Busch and
5
Reinhardt 2000), and—with respect trade policy—are more likely to gain protectionist
concessions from government (Busch and Reinhardt 1999; McGillivray 2000). Proximity
lowers the barriers to organization by facilitating communication and the formation of
social ties.
Taken as a whole, however, individual donors are less motivated than political
action committees (PACs) to contribute to campaigns in order to advance material
interests. PACs contribute primarily to ensure access to Washington officeholders in
hopes of using that access to obtain tangible gains for their organizations (Herrnson
2003). Patterns of giving reveal that PACs are generally less interested in affecting the
outcomes of elections than in ensuring that they can gain a friendly hearing from anyone
who holds the office (Grenzke 1988). Individual donors appear to be driven more by
purposive and solidary goals, both of which are also influenced by geographic context.
With respect to political campaigns, purposive giving means supporting the
policy goals of a candidate or party and trying to make a difference to the outcome of
the election.
For the same reason that proximity helps individuals and businesses
perceive a common material interest and overcome barriers to cooperation, it also
facilitates organization for broader policy or ideological goals.
Individuals are more likely to develop a purposive motive for giving—i.e., to
believe that a party or a candidate will promote good public policy—if someone they
trust communicates those policy goals to them.
Trust enhances information
transmission, making people more willing to share information with one another and
creating richer information flows (Morgan 2004).
Building trust, however, requires
6
prolonged socialization and regular reassurance of the other person’s sincerity, and thus
distance creates a significant barrier to the interpersonal contact necessary (Morgan
2004). As a result, the bonds of trust that parties and candidates need to communicate
effectively and cultivate support are more likely to exist on a local level.
Candidates and parties take advantage of these local networks of trust to elicit
financial support. Capitalizing on their high standing in the community, local notables
are put in charge of fundraising efforts, and they often serve as event sponsors for years
at a stretch, continually reinforcing ties.
People are more likely to give money to
someone they know and trust (Claggett and Pollock 2004), and a considerable portion of
citizen participation is stimulated by recruitment efforts (Brady, Schlozman and Verba
1999). Of course, some donors are self-starters. Informed and issue oriented, they give
money to achieve favored political ends without prompting. But many donors are
induced to give solely because someone they trust told them it was important and asked
them for help (Francia et al 2003).
Finally, geographic context has very important effects on solidary motives for
contributing to campaigns. People often contribute to campaigns simply out of a desire
to feel that they are part of a political team fighting for a cause or a member of a high
status social group. This type of contribution is directed at improving one’s own social
ties or standing, giving “to please, or not to offend, [to] cement social relationships”
(Brady, Schlozman and Verba 1999, 155). Solidary feeling is greatly heightened by faceto-face contact.
Distance precludes regular personal interaction, diluting social
7
influences (Latané 1981; Latané, Nowak and Liu 1994; Latané and Liu 1996; Latané Liu,
Nowak, Bonevento and Zheng 1995) and weakening solidary motives for participation.
In order to exploit solidary motives for campaign contributions, candidates and
party activists have to connect with people face-to-face.
People who want to feel
important by contributing to campaigns will not do so in response to a direct mail
solicitation. They will want to be asked, preferably in person, often by the candidate
him- or herself (Alexander 1992, 50). Similarly, people who are involved in politics
because they enjoy being a part of a team will want to socialize with teammates. Rallies,
receptions, coffees, meet-ups, ice cream socials, rubber chicken dinners and other such
gatherings are organized to generate and leverage social pressure to contribute to
campaigns.
Geography thus contextualizes solidary motives because social ties are
strongest in proximate settings, and there is an obvious limit to how far most people will
travel to enjoy solidary experiences.
In summary, geographic proximity affects each of the motives that lead people to
participate in politics generally and, in particular, to contribute to campaigns. Networks
of interest, purpose, or solidarity are all more likely to form where the barriers of
distance are lower. Parties and candidates capitalize on the social relations grounded in
physical places in order to elicit contributions. Contributing to campaigns, as well as the
amounts contributed, are therefore not merely traceable to the geographic distribution of
wealthy and efficacious individuals, but also to the nature of social relationships.
8
Data Source
Our data originate from the U.S. Federal Election Commission (FEC), the official
agency charged with monitoring and regulating campaigns in U.S. federal elections.
The FEC maintains separate data collections for individual contributions and corporate
and PAC contributions.
For our purposes here, we examined only the individual
contribution files, not the PAC/corporate donation files. Our first step was to separate
out individual contributions to the two major parties and the candidates aligned with
them from all other individual contributions.
Because the FEC files contain information on the location of each contributor, we
were able to aggregate contributions to their zip codes of origin.
The zip code
information available in the FEC files was generally complete and of high quality,
missing from less than 2 percent of all contribution cases.2 Zip codes thus become our
principal unit of observation, presenting the advantages of greater variation and much
finer granularity than the county.
Fortuitously, we were also able to draw upon
demographic and economic information available at the zip code level from the 1990
and 2000 U.S. Censuses, with observations for the intercensal years interpolated to
account for growth and change.
Data on congressional campaign competitiveness and seat status (incumbent
present or open seat) was included for the states and districts within which zip codes are
located. Although data on party registration or presidential preference are not available
2
Cleaning these files took several months, but we were eventually able to reduce the number of unreported
zip codes to less than 1 percent of the file for each year. This was manageable because often we found
multiple contributions had come from the same individuals, but only one contribution report contained that
individual’s zip code.
9
at the zip code level, we have incorporated information on the political complexion of
the county within which each zip code is located. This gives us at least an approximate
sense for whether the contexts in which contributors are embedded are predominantly
Republican, Democratic, or competitive between the two parties.
The Two Parties’ Bases of Financial Support
Using a geographic boundary file for zip codes, we are able to use a Geographic
Information System (GIS) to map the spatial distribution of contributions for each party
in off-year and on-year elections. To conserve space, we do not present maps for every
year, but include some two exemplary ones in the following pages. Two choropleth
maps (Figures 1 and 2) give readers a sense for the distribution of contribution amounts
and areas of contributor concentration. While the maps are primarily for illustration,
and areally dense zip codes are difficult to see, they provide an intriguing picture of the
geographic origin of individual contributions.
Generally, what we find most remarkable about the maps of the geographic
origins of contributions to the Republican and Democratic parties is their substantial
similarity when compared to the ubiquitous red and blue maps showing the electoral
distribution of party support. Figures 1 and 2 indicate that Republicans and Democrats
both received large shares of their contributions from urban areas on the three coasts,
particularly the Philadelphia-New York-Boston corridor, Southern California, and the
major Great Lakes cities of Chicago and Detroit.
Although Republicans exhibit a
broader geographic base than Democrats in the Upper Midwest and in the Plains and
10
Mountain states, many of the same areas appear on both maps in the highest contributor
segments (darkest colors).
[Figures 1 & 2 about Here]
Both parties draw substantial contributions from areas that are typically
unreceptive to their candidates in elections. Democrats receive significant contributions
from locations in North Carolina, Georgia, and along the Gulf Coast from Houston to
Florida’s panhandle—all states in which Democrats rarely win statewide. Republicans,
meanwhile, raise very substantial sums from Boston, New York City, the DC suburbs,
the San Francisco Bay Area, Chicago, Detroit, the Twin Cities, and other areas not
usually thought of as Republican strongholds.
Figure 3 illustrates basic statistical parameters that describe the overall spatial
distribution of contribution amounts for the Democratic and Republican Parties in 2004.
The mean centers of the two distributions are located on the map as the red and blue
points. Specifically, the mean center for Democratic contributions is approximately 100
miles away from the point that marks the mean center of Republican contributions. The
point marking the mean center of the Democratic distribution is located approximately
at the city of St. Louis, whereas the Republican point is somewhat more southern and
shifted slightly to the west.
The red and blue boxes for Figure 3 indicate the dispersion in the distribution –
the coordinates marking one standard deviation around the mean center points. The
Democratic distribution spreads more in an East-West direction than the Republican,
reflecting the pull of the contributors on the east and west coasts who give generously to
11
Democrats.
The Republican distribution is noticeably more southern.
The
centrographic values plotted on this map underscore our point about the substantial
similarity of the two parties’ contributor bases, while exhibiting some association with
patterns of electoral support.
[Figure 3 about Here]
Just how dependent are the parties on the major metropolitan areas for their
funding? Figure 4 illustrates this reliance by showing the percentage of 2000 and 2004
campaign contributions coming from each decile of zip codes categorized by population
density. The blue shaded lines track the percentage of Democratic contributions from
each density decile; the red lines shows the percentage of Republican contributions from
each decile; and the black line indicates the percentage of the nation’s total population
that resides in each decile. Approximately 29 percent of the U.S. voting age population
resides in zip codes located in the highest density decile. In 2000, Democrats drew
nearly 50 percent of their total contributions from that decile alone, and Republicans
drew 32 percent. In 2004, Democratic reliance on the highest density decile moved up
by 4 percent (to 53 percent), and for Republicans it edged up about one percentage
point.
Taken together, in 2004, the top two deciles generated fully 67 percent of
Republican contributions and 79 percent of Democratic contributions, whereas the top
two deciles only accounted for 54 percent of the voting age population. Democrats
outpaced Republican fundraising in the top density decile, but Republicans outpaced
Democrats in every other decile, most notably in the 7th and 8th deciles – primarily lower
12
density neighborhoods on the suburban fringe (see Figure 4).
Most of the deciles
generate a smaller percentage of the parties’ funding than they represent of the nation’s
population; only the most densely populated areas out-give their share of the
population. Both parties are thus disproportionately reliant on the cities and major
metropolitan areas for funding, with the disparity more pronounced for Democrats than
Republicans.
[Figure 4 about Here]
Figure 5 illustrates that both parties turn to the richest Americans for the bulk of
their funds, and citizens residing in the wealthiest areas provide those funds far out of
proportion to their share of the nation’s population. Here we have plotted the percent of
contributions to each party (y axis) by each decile of median income (on x axis). The red
lines show the percentage of Republican contributions from each income decile for 2000
and 2004; the blue lines denote the percentage of Democratic contributions from each
decile; and the black line describes the percentage of the nation’s voting age population
in each income decile. Both parties overwhelmingly depend upon the richest decile of
population for their funding, even though the zip codes in this decile only account for 18
percent of the nation’s voting age population. In 2004, the Democrats garnered 60
percent of their contributions from the top decile of median income alone (see Figure 5).
Republicans were not far behind, collecting 49 percent of their contributions from these
same locations.
Below the top two deciles, every other income decile provides a
percentage of contributions to the two major parties that is smaller than their share of
the population.
13
[Figure 5 about Here]
The descriptive data presented in these figures suggest that the geographic
distribution of campaign contributions largely follows the distribution of wealthy and
efficacious individuals. Both parties turn to the major cities and their surrounding
metropolitan areas for the bulk of their funding, and they both rely on the nation’s
wealthy elite. In the next section, however, we explore whether this is all there is to the
story, or whether geography exercises an independent influence on people’s propensity
to contribute to campaigns, separate from their individual resources to do so.
Variables and Hypotheses for Multivariate Analysis
If geographically-based networks induce individuals to contribute to campaigns,
and their absence depresses their willingness to do so, there will be geographic patterns
in campaign contributions that cannot be explained by differences in individual
economic and demographic characteristics (Tam Cho 2003). Because we expect these
patterns to be decidedly local—existing only in areas close enough to one another so that
the people in them can have regular contact with one anther—these patterns cannot be
detected from examinations of national maps.
In order to test for such patterns,
multivariate spatial regression is needed to model the geographic distribution of
campaign contributions (Anselin 1988). The dependent variable in these models is the
total contribution to a party from a zip code in a campaign year. Separate models are
estimated for each political party for every campaign year between 1992 and 2004.
14
The presence of local networks enhances the incentives of individuals to
participate in politics for material, purposive, and solidary reasons. In localities where
these face-to-face ties are stronger and denser, we expect to find higher levels of
campaign contributions than in areas where they are weaker and fewer. Unfortunately,
it is exceedingly difficult to find a direct measure of these highly local, face-to-face
networks in their immense variety—formal and informal, party organizations and
officials, government employees, interest groups, labor unions, civic associations, and
networks formed around workplaces, community centers, places of worship, and
families.
To obtain a proxy measure of the variation in these networks, we include a
variable designed to capture highly local patterns in giving. We employ a spatial lag—a
weighted sum of the contributions in the eight nearest neighboring zip codes within 60
miles3—to pick up the degree to which the contribution level in one zip code resembles
the contribution levels in its nearest local vicinity. Although any cut-off point is to some
extent arbitrary, distances greater than 60 miles present a formidable barrier to the
formation of regular face-to-face networks and interactions.
Furthermore, this
specification has the desirable property that it encompasses shorter distances in areas
where population is denser. The eight nearest zip codes will be closer together in
densely populated areas, reflecting the additional burdens of traffic and congestion
faced by donors and prospective donors in these areas.
If this variable affects
contributions in the expected way, it will exhibit a consistently positive coefficient,
3
The spatial lag was calculated using the statistical software GeoDa™.
15
showing that even after we control for all the other factors likely to affect contributions,
local geography exhibits an additional, important effect on a neighborhood’s giving
patterns.
In addition to local network ties, the level of party and electoral competition may
also affect campaign giving. Purposively motivated individuals will likely be prompted
to give more when they feel that their contribution will make a difference to the
outcome. They will be more likely to feel this way if there is a competitive local race
where the outcome is unknown and could seemingly go either way. To capture these
effects, we include a variety of measures of party competition: whether the zip code lies
within a state or congressional district with a competitive race (as measured by
Congressional Quarterly Weekly Report’s annual campaign coverage), whether there is an
incumbent present, and the overall party competitiveness of the county as measured by
the most recent presidential election results. If competition prompts additional giving,
we would expect to find positive coefficients for both parties’ contribution levels when
there is no incumbent present, when a local race is competitive, and when the parties are
evenly balanced.
The descriptive data above show that financial resources are a major predictor of
campaign contributing. In this model, we introduce several variables to capture wealth
and other advantages that make it easier for some neighborhoods to give more to
campaigns than for others. First, zip codes are not all equal in population. The average
zip code has a population of 6,000, but all else being equal, zip codes containing smaller
populations will have fewer resources to contribute than zip codes with larger
16
populations. To capture these differences, voting age population is included as an
indispensable control variable. Second, there are two variables capturing differences in
income: the median income of the zip code, and, because the very wealthiest are the
biggest contributors, the percentage of the zip code’s population earning $150 thousand
or more annually.
Third, older people, i.e., empty nesters, have had a lifetime to
accumulate assets, and retirement may yield both more time to spare for political
involvement and more money to spare for contributing. Fourth, race is still associated
with wealth and political efficacy. Zip codes that are predominantly white will also be
more likely to have politically active campaign contributors.
another important resource that increases political efficacy.
Finally, education is
Highly educated
individuals are more likely to get involved in politics than the poorly educated, so we
include in the model the percentage of the population that has a 4-year college degree. If
these educational and financial resources have the expected effect, we would expect that
they would all be positively associated with contributing to campaigns.
Finally, some occupational niches may contribute to greater involvement in
politics.
In geographic areas with a strong concentration of a particular type of
occupation or industry, these interests may be better able to organize and mobilize to
participate in politics. Previous studies have shown that particular occupations such as
farming and manufacturing have been associated with giving (Verba, Schlozman, Brady
and Nie 1993; Aguilar, Fordham and Lynch 1997; Day and Hadley 2002; Miller and Sifry
2000). In order to capture these effects, the models include measures of the prevalence
17
of four distinctive occupations:
percent of the population engaged in farming, in
manufacturing, and who are self-employed or professional/executive.
The regression equation can be written as follows:
Contribution = α + β1 Population + β2 HighIncome + β3 MedianIncome + β4 Elderly + β5 Education
+ β6 Farm + β7 Manufacturing + β8 Executive + β9 SelfEmployed + β10White + β11 SenateCompetition
+ β12 SenateIncumb + β13 HouseCompetition + β14 HouseIncumb + β15CountyVote + β16 CountyCompetition
+ ρWContribution + error
The term, ρ, is the coefficient for the spatially lagged dependent variable, WContribution.
An appendix describes the precise measures, sources, and definitions for each of the
other variables included in the models.
Methodological Issues in Spatial Regression
The adjacency of the observations in the dataset, and the likely relationship
between zip codes that lie proximate to one another, poses special challenges from a
statistical standpoint because we must be cognizant of spatial structure. By spatial
structure, we mean the presence of spatial interdependence across our units of
observation.
Standard ordinary least squares regression models assume no spatial
dependency among observations.
Moran’s I is a means of measuring the extent of “spatial dependence” in zip code
contribution patterns (Griffith 1987; Anselin 1988). Moran’s I gauges the extent of spatial
dependence because it is formulated to discriminate between different arrangements of
x values on a planar surface. From an interpretive standpoint, the values of Moran’s I
18
are centered around zero. High positive values indicate positive spatial autocorrelation,
showing that neighboring values are alike.
High negative values exhibit negative
autocorrelation, exhibiting dissimilarity in nearby values, following the pattern on a
checkerboard. Values approaching zero indicate a random pattern, or no significant
spatial structure (Tam Cho 2003; Goodchild 1986).
A formal statement of Moran’s I is as follows:
I = ∑i ∑ j wij ( y i − z )( y j − z ) / ∑i ( y i − z ) 2
Where wij is an element of a row-standardized spatial weights matrix,
contribution level and
4
y is the
z is the average contribution level of all counties. If spatial
autocorrelation is present, then models that do not take into account the spatial
relationships among units cannot be trusted to provide accurate parameter estimates in
4
To calculate Moran’s I, we must specify the nature of the weights matrix – the
matrix that specifies how the individual units of observation are related to one another.
Taking each county as a distinct unit, we calculated the eight nearest neighbors for all
observations using the statistical software GeoDa™. This produced an N x N matrix
specifying the spatial relationship between each observation to its nearest neighbors,
and to all other zip codes in the nation. The one additional adjustment we made to the
nearest neighbor criterion was to exclude zip codes from having neighbors if the closest
neighboring observations were more than 60 miles away. This helped us to avoid the
problem that some adjacent zip codes in sparsely populated locations are unacceptably
far away to be considered “neighboring” observations. In summary, a zip code was
presumed to be influential on another zip code if it was among its 8 nearest neighbors
and less than 60 miles away. We then converted the distance matrix to a binary
contiguity matrix (1,0). The conversion of the distance matrix to a binary contiguity
matrix results in a N x N matrix, wij, of 1s and 0s, with 1s indicating linkages to the
neighboring observation(s), 0 indicating no such linkage. The spatial weights matrix,
once constructed, is then standardized such that the row elements sum to one (Anselin
1988). This same weights matrix is used to calculate the spatially lagged dependent
variables placed on the right-hand-side of the subsequent regression analyses.
19
regression models (Tam Cho 2003; Anselin 2002).
However, the spatial lag variable included in the model reduces the spatial
autocorrelation in the data to tolerable levels. Without its inclusion, for example, the
value of Moran’s I for Democratic contributions in 2004 is 0.46, exhibiting positive spatial
dependency; for Republicans, the value is lower, but still 0.31. If we test both of these
results against the assumption that each set of x values was drawn independently from a
population of normally distributed values, we can reject the null hypothesis of no spatial
dependence at the p<.0001 level of significance.5
The inclusion of the independent
variables but no spatial lag reduced the level of spatial autocorrelation, but only
modestly (Moran’s I=0.21 for Republican contributions and 0.35 for Democrats, both
significant at p≤.001). With the inclusion of the spatial lag variable and the other
independent variables identified in Tables 1 and 2, however, Moran’s I values diverge
from 0 by no more than .07 for any year in the study, indicating that that the spatial
dependence has been significantly reduced by the inclusion of this critical explanatory
variable.
Findings
Tables 1 and 2 display the results of the spatial regression models of Democratic
and Republican Party contributions for each year between 1992 and 2004. The findings
5
Tested under a randomization assumption: that the sample of x values was randomly
chosen from among the n! possible arrangements of the observed x values among the
total number of zip codes, then estimating the probability that such a randomly chosen
arrangement would have a Moran’s I as extreme as the one actually observed; also leads
us to reject the null hypothesis.
20
provide substantial support for the hypothesis that local context affects campaign
contributing.
[Tables 1 and 2 about Here]
The spatial lag variable takes a positive and statistically significant coefficient for
both parties for each year of the study.
This effect indicates that contributions from
neighboring zip codes exert strongly positive and significant effects on contributions in
the typical neighborhood.
Strictly speaking, the source of this spatial pattern is
unknown, and we cannot determine it with spatial data analysis (Tam Cho and Rudolph
2005). Nevertheless, the pattern is completely consistent with the idea that cross-zip
code contribution networks are present and greatly facilitate giving (Tam Cho 2003).
The significance and magnitude of the spatial lag in these results reveals that
contributions are geographically clustered in ways that are not predicted by income,
age, education, occupation, or the prevailing local political winds.
Notably, the spatial lag coefficient in the model for Republicans contributions is
consistently lower than the coefficient in the model of Democratic contributions. This
finding suggests that GOP fundraising is less dependent on local networks to prompt
giving. This may partly reflect the Republican party’s earlier embrace of direct mail
techniques to reach potential contributors not plugged into local networks and its more
successful development of a database of individual hard money contributors. It may
also suggest that Republican contributors are motivated more by individual
characteristics, including ideological commitment, wealth, and interests, than by
participatory norms associated with group membership and affiliation.
21
Neighborhoods with more resources contribute considerably more to both
parties. Population, income, education, and the other measures of ability generally
make a large and statistically significant positive difference in the models of both
Republican and Democratic contributions. Contribution amounts to both Democrats
and Republicans always rise with population size, although its effects are greater for
Republican contributions than for Democratic contributions.
The models measure the effects of income on campaign contributions in two
ways: by examining the presence of wealthy residents (percent earning $150,000 or
more) and by identifying the median household income of each zip code. The results
show that affluence steeply influences contributions to both Republicans and Democrats.
In 2004, for instance, just a one percent increase in the percentage of the population
earning more than $150,000 per year is associated with a $3,699 increase in contributions
to the Democratic Party. Republican contributions are even more strongly driven by the
presence of high-income households. Republicans gained $4,848 in 2004 for every one
percent increase in high- income households, a 31% boost over the wealth premium
enjoyed by Democrats that year. The weakest increase in Democratic contributions
associated with rising affluence occurs in 1994, perhaps not coincidentally, also the
election immediately following President Clinton’s 1993 tax increases on the top 1.2% of
earners. Once we control for the percentage of residents at the highest end of the income
distribution, however, rising median income is actually associated with diminished
contributions to both parties. Areas with upper-middle incomes may be slightly less
likely to contribute to the Democratic Party than either the very affluent or lower middle
22
income neighborhoods. And as median income increases, Republican contributions
decrease at an even higher rate than those to Democrats, suggesting that Democrats
have a marginally larger base of middle-income contributors.
Senior citizens contribute more to both parties, consistent with the expectation
that their asset accumulation and higher level of interest in politics would lead to
increased contributions. In the 2004 cycle, the effect is greater for the Republican than
for the Democratic Party: a ten percentage point increase in the local population over
age 65 is associated with a $5,430 increase in contributions to the GOP, but only a $3,704
increase for the Democrats. Notably, these are dramatically steeper effects than for the
2000 cycle, suggesting that zip codes with elderly residents contributed much more in
2004 than in 2000.
Because of their advantages in wealth, education, and political efficacy, an
increase in the percentage of white residents in the local population is also linked to
increased contributions to both Democrats and Republicans. The variable is positive
and statistically significant in every model for both parties.
Even though African
Americans overwhelmingly affiliate with the Democratic Party and Latinos strongly tilt
in the Democratic direction, the Democratic Party sometimes gains even more from
increases in the percentage of the local white population than Republicans do.
Education is also positively associated with campaign contributing. Education
increases political efficacy and thus political involvement generally, and campaign
contributing is no exception. The effect is greater and more consistent for Republicans
than for Democrats. Areas with a higher percentage of persons with a 4-year degree
23
give more to Republicans in all campaign years, and the coefficient is statistically
significant in all but two of these years. For Democrats, the effect is smaller but usually
positive and statistically significant, although the variable takes on a negative coefficient
in 2000, 2002, and 2004.
The geographic concentration of specific occupational sectors usually leads to
increased contributions to both parties.
Having a high percentage of individuals
engaged in the same type of economic enterprise in a local area may assist them in
organizing and acting on their political interests. Farming areas are associated with
higher contribution levels to Democrats. That Democrats would do so well in these
locales stands in contrast to their reputation for doing poorly among rural American
voters.
A strong presence of manufacturing had a positive and usually statistically
significant impact on contributions to both parties throughout the 1990s. In 2004, the
variable is positive for both parties, but stronger and only statistically significant for the
GOP, perhaps reflecting a propensity for management interests in manufacturing to
contribute more than labor interests.
A high percentage of professionals and executives in the local population
benefits both parties. Their presence is never associated with a statistically significant
decline in Democratic contributions, whereas it leads to increases in 1994, 1996, 1998,
and 2004.
Similarly, an increased proportion of professionals and executives was
associated with positive, statistically significant gains for Republicans in each campaign
year from 1992 to 1998, and again in 2004.
24
Finally, the presence of self-employed workers exerts a positive influence in the
models of campaign contributions for both parties in each campaign year, and the
coefficient is large and statistically significant in four of the models for each party. This
particular occupational group is associated with much more contributing in 2004 than in
previous elections. Specifically, a one percent gain in the percentage of self-employed
workers is associated with a $1109 gain in contributions to Democrats, and a $2,354
increase to the GOP, over much smaller amounts in 2000.6
The competitiveness of local campaigns has no consistent effect on either
Republican or Democratic fundraising. The presence of a competitive U.S. Senate race
depresses contributions as often as it elicits them, and coefficients with both positive and
negative signs reach statistical significance in the models for both parties. Nor does the
presence of an open-seat race for the U.S. Senate consistently increase contributions from
zip codes within the state. Similarly, the competitiveness of a local House race, or the
status of the seat (open or incumbent running) does not have much effect in either the
Republican or Democrat models. A competitive local House race is usually associated
with more contributions to both parties, but the effect is only statistically significant in
two Republican campaign years.
Taken together, these various indicators show that contributors give even when
6
Apparently, the source of this surge lies mainly in changes in election law governing
the way in which small independent business owners could contribute. With BCRA’s
passage in 2002, small business owners could no longer write so-called “Inc. checks”
from business-related checking accounts, but were required to donate from personal
accounts – thereby making their contributions reportable to the FEC as individual
contributions. The former Inc. check contributions were not classified in this manner in
previous years.
25
there are few locally competitive federal elections. Parties and incumbent politicians
work hard to cultivate reliable donors who will contribute regardless of the local
political climate. Moreover, so many contributors live within politically lopsided
jurisdictions, often in safely Republican suburbs or safely Democratic cities, that
contributions are not especially sensitive to the competitiveness of local political races.
Discussion
The political geography of campaign contributions reveals that location matters
greatly in a way that is little understood and matters relatively little in a way that is
widely thought to be important in American elections. With respect to the latter, the
geographic origins of campaign contributions little resemble the patterns of electoral
support so apparent in maps of election returns. In this sense, geography is much less
significant for understanding campaign contributions than it is for understanding
voting.
Both parties must go to the same sorts of places to drum up campaign
contributions. Both parties raise almost half of their funds from neighborhoods with a
median income in the top decile.
For both, campaign donations are harvested
disproportionately in metropolitan areas, not rural hinterlands. There are, of course,
differences between the parties’ fundraising patterns that reflect differences in their
bases of electoral support. Republican campaign prospecting, for example, is somewhat
more fruitful in less densely populated areas than that of Democrats, and Democrats
rely more heavily on urban centers than Republicans. But the similarities between the
26
parties’ geographic bases of financial support are far more impressive than the
differences.
However, this study suggests that geography is far more consequential in a less
widely understood way: how distance constrains the formation and maintenance of
social networks that influence individuals’ propensity to participate in politics. Motives
for participating in politics are shaped by social networks through a variety of
pathways. People are more likely to perceive that they share a common material interest
in government when they come face-to-face with others who share that interest.
Geographically concentrated occupations and industries are thus more likely to form
effective organizations than interests that are not adjacent. Furthermore, people are
more likely to organize for purposive goals when they are tied together by bonds of trust.
Trust is more likely to develop in local contexts where people have repeated, ongoing
interactions with one another, and it is harder to forge these bonds over long distances
where frequent interaction is impossible. People are more likely to organize for solidary
reasons when great distances do not prevent them from enjoying one another’s
company. All of these networks are valuable resources for politicians and parties to
exploit as they build support and solicit campaign donations.
The findings in this study are consistent with this theory emphasizing the
importance of social networks for understanding campaign contributing (Tam Cho
2003). Spatial regression analysis reveals that contributing to parties and campaigns is
affected by local context, even after controlling for the demographic and political factors
that greatly affect the propensity of individuals to contribute.
Contributors flock
27
together, and contribution patterns cannot be explained solely by the distribution of
resources and other reasons for contributing (Tam Cho 2003). Although some people
get involved in politics independent of any social setting that encourages them to do so,
parties cannot afford to rely on these individuals alone.
In order to raise funds, parties need to connect with the multitudes of highly
local and varied social networks that prompt people to political involvement and
awareness.
Interestingly, the findings suggest that although social networks are
important for both parties, Democrats need them even more than Republicans.
Republicans appear to benefit from the presence of local networks, but also seem to have
a more consistent and reliable donor base outside these networks than Democrats.
Democratic contributions are more variable and afford a less reliable fundraising base,
making it all the more important for Democrats to develop their connections to the social
networks at the grass roots. A silver lining for Democrats is that their concentrated base
of support in urban areas is likely to enhance the effectiveness of fundraising strategies
that draw upon geographically discrete social networks.
Whether the GOP can
construct more effective network outreach from a less concentrated base of support
remains to be seen.
Sources
Abramowitz, Alan I. 1988. “Explaining Senate Election Outcomes.” American Political
Science Review. 82: 2: 385-403.
Alexander, Herbert E. 1992. Financing Politics: Money, Elections, and Political Reform.
Washington DC: CQ Press.
28
Aguilar, Edwin Eloy, Benjamin O. Fordham and G. Patrick Lynch. 1997. “The Foreign
Policy Beliefs of Political Campaign Contributors.” International Studies Quarterly
41: 355-366.
Amy, Douglas J. 2002. Real Choices/New Voices. 2nd edition. New York: Columbia
University Press
Anselin, Luc. 1988. Spatial Econometrics: Methods and Models. (Boston: Kluwer).
Anselin, Luc. 2002. “Under the Hood. Issues in the Specification and Interpretation of
Spatial Regression Models.” Agricultural Economics. 24: 7: 247-267.
Anselin, Luc. 1995. “SPACESTAT Version 1.80 User’s Guide.” (Morgantown, WV:
West Virginia University Regional Research Institute).
Brady, Henry E., Kay Lehman Schlozman and Sidney Verba. 1999. “Prospecting for
Participants: Rational Expectations and the Recruitment of Political Activists.”
American Political Science Review 93: 1: 153-168.
Brown, Clifford W., Jr., Roman B. Hedges, and Lynda W. Powell. 1980. “Modes of Elite
Political Participation: Contributors to the 1972 Presidential Candidates.”
American Journal of Political Science 24: 2: 259-290.
Burns, Nancy. 1994. The Formation of American Local Governments: Private Values in Public
Institutions. New York: Oxford University Press.
Busch, Marc L. and Eric Reinhardt. 1999. “Industrial Location and Protection: The
Political and Economic Geography of U.S. Nontariff Barriers.” American Journal of
Political Science. 43:4: 1028-1050.
Busch, Marc L. and Eric Reinhardt. 2000. “Geography, International Trade, and Political
Mobilization in U.S. Industries. American Journal of Political Science 44: 4: 200: 703719.
Campbell, James E. 1996. Cheap Seats: The Democratic Party’s Advantage in U.S. House
Elections. Columbus: Ohio State University Press.
Claggett, William and Philip H. Pollock. 2004. “Recruiting More but Contributing Less:
the Puzle of Campaign Contributions in the Pre- and Post-Reform Eras. Paper
presented at the Annual Meeting of the Midwest Political Science Association,
Chicago, IL, April 15-18.
29
Day, Christine L. and Charles D. Hadley. 2002. “Who Contributes? Similarities and
Differences between Contributors to Emily’s List and WISH List.” Women and
Politics 24: 2: 53-67.
Finifter, Ada. 1974. “The Friendship Group as a Protective Environment for Political
Deviants.” American Political Science Review 68: 3: 607-625.
Francia, Peter L., Paul S. Herrnson, John C. Green, Linda W. Powell, and Clyde Wilcox.
2003. The Financiers of Congressional Elections: Investors, Idealogues, and Intimates.
New York: Columbia University Press.
Goodchild, Michael F. 1986. Spatial Autocorrelation. Norwich, UK: Geo Books.
Griffith, Daniel A. 1987. Spatial Autocorrelation: A Primer.
Association of American Geographers.
Washington, D.C.:
Grenzke, Janet. 1988. “Comparing Contributions to U.S. House Members from Outside
Their Districts.” Legislative Studies Quarterly 13: 1: 83-103.
Herrnson, Paul S. 200 3. Congressional Elections: Campaigning at Home and in Washington.
Washington, DC: CQ Press.
Hinckley, Katherine A. and John C. Green. 1996. “Fundraising in Presidential
Nomination Campaigns: The Primary Lessons of 1988.” Political Research
Quarterly 49:4: 693-718.
Huckfeldt, Robert, Paul E. Johnson and John Sprague. 2004. Political Disagreement: The
Survival of Diverse Opinions within Communication Networks.
New York:
Cambridge University Press.
Jacobson, Gary C. 2003. The Politics of Congressional Elections. New York: Pearson
Longman.
Latané, Bibb. 1981. “The Psychology of Social Impact.” American Psychologist 36: 343356.
Latané, Bibb and James H. Liu. 1996. “The Intersubjective Geometry of Social Space.”
Journal of Communications. (Autumn) 46: 4: 26-34.
Latané, Bibb, A. Nowak and J. Liu. 1994. “Measuring Emergent Social Phenomena:
Dynamism, Polarization and Clustering as Order Parameters in Social Systems.”
Behavioral Science 39: 1-24.
30
Latané, Bibb, J. Liu, A. Nowak, M. Bonvento and L. Zheng. 1995. “Distance Matters:
Physical Space and Social Impact.” Personality and Social Psychology Bulletin 21:
795-805.
Martin, Paul S. 2003. “Voting’s Rewards: Voter Turnout, Attentive Publics and
Congressional Allocation of Federal Money.” American Journal of Political Science
47: 1: 110-127.
McGillivray, Fiona. 1997. “Party Discipline as a Determinant of the Endogenous
Formation of Tariffs.” American Journal of Political Science. 41: 584-607.
Miller, Ellen S. and Micah L. Sifry. 2000. “Who Gives?:
Contributors.” American Prospect 11: 22: 10-11.
Surveys of Political
Mondak, Jeffrey J. and Diana C. Mutz. 2001. “Involuntary Association: How the
Workplace Contributes to American Civic Life.” Paper presented at the Annual
Meeting of the Midwest Political Science Association, Chicago, IL.
Morgan, Kevin. 2004. “The Exaggerated Death of Geography: Learning, Proximity and
Territorial Innovation Systems.” Journal of Economic Geography 4:1: 3-14.
Mutz, Diana C. 1995. “Effects of Horse-Race Coverage on Campaign Coffers: Strategic
Contributing in Presidential Primaries.” Journal of Politics 57: 4: 1015-1042.
Tam Cho, Wendy K. 2003. “Contagion Effects and Ethnic Contribution Networks.”
American Journal of Political Science 47, 2 (April): 368–387.
Tam Cho, Wendy K. and Thomas Rudolph. 2005. “Untangling the Spatial Structure of
Political Participation.”
Working Paper, University of Illinois, UrbanaChampaign, Department of Political Science.
Verba, Sidney, Kay Lehman Schlozman, Henry E. Brady and Norman H. Nie. 1993.
“Citizen Activity: Who Participates? What Do They Say?” American Political
Science Review 87: 2: 303-318.
Verba, Sidney, Kay Lehman Schlozman and Henry E. Brady. 1995. Voice and Equality:
Civic Voluntarism in American Politics. Cambridge, MA: Harvard University
Press.
Wilson,
James
Q.
1973.
Political
Organizations.
New
York:
Basic
Books.
Table 1. Total Democratic Contributions by Zip Code, 1992-2004
Total 2004
Total 2002
(Constant)
Network effects
Contributions of Neighboring Areas
Resources
Voting Age Pop of Zip Code
Percent Earning $150k Up
Percent over age 65
Median HH Income (in 1000s)
Percent with 4 Year College Degree
Percent White
Incentives
Senate Competitiveness from CQWR
Presence of Senate Incumbent
House Competitiveness from CQWR
Presence of House Incumbent
Previous Competitiveness (County)
Total 2000
Total 1998
Total 1996
Total 1994
Total 1992
-6268.04
(1292.71)
-39960.66**
(5369.37)
-8865.94**
(1245.06)
-9549.77**
(1773.64)
-5598.03**
(850.95)
-5744.45**
(1160.50)
-4801.47**
(763.98)
0.90**
(0.01)
0.82**
(0.01)
0.84**
(0.01)
0.78**
(0.01)
0.79**
(0.01)
0.79**
(0.01)
0.85**
(0.01)
0.88**
(0.07)
3698.76**
(153.71)
370.44**
(82.43)
-345.63**
(69.46)
-723.70**
(111.19)
448.44**
(35.55)
0.38**
(0.02)
1686.63**
(48.21)
83.16**
(22.90)
-228.65**
(16.60)
-133.72**
(28.68)
128.77**
(8.83)
0.74**
(0.03)
2796.57**
(80.67)
146.97**
(36.84)
-299.55**
(25.46)
-267.09**
(44.66)
181.42**
(13.09)
0.33**
(0.02)
952.73**
(34.95)
35.27
(17.71)
-162.85**
(11.98)
53.89*
(20.73)
73.41**
(6.31)
0.38**
(0.02)
1272.06**
(46.82)
54.85*
(22.07)
-225.05**
(14.92)
152.41**
(25.46)
86.82**
(7.73)
0.19**
(0.01)
500.91**
(25.81)
112.91**
(13.66)
-22.97**
(5.26)
82.90**
(15.57)
31.51**
(4.94)
0.25**
(0.02)
804.90**
(39.97)
144.67**
(19.98)
-31.74**
(7.95)
166.15**
(22.35)
52.13**
(7.60)
456.08
(344.84)
1184.48**
(319.57)
199.21
(462.31)
664.93
(338.66)
-24.66
-1161.63*
(501.92)
-1026.47*
(465.89)
222.62
(766.39)
631.81
(463.81)
-50.01**
147.42
(241.71)
57.87
(239.45)
405.07
(340.56)
376.17
(240.73)
-29.30*
383.12
(279.55)
72.19
(278.82)
459.27
(304.39)
123.00
(272.81)
-38.00**
-400.52*
(185.70)
-627.98**
(186.88)
193.11
(211.21)
46.64
(178.18)
-23.21**
-590.45
(285.80)
374.06
(285.58)
61.52
(311.08)
91.85
(275.96)
-28.55**
2211.35
(1374.36)
-146.75
(1205.44)
3490.05
(2177.22)
-1417.94
(2499.68)
-97.10
1
Partisan Context
Previous Republican Vote (County)
Interests
Percent Farm
Percent in Manufacturing
Percent Professional and Executive
Percent Self Employed
(38.95)
(10.08)
(17.47)
(8.24)
(9.63)
(6.37)
(8.69)
-19.17
(54.68)
-22.85
(14.14)
-66.39
(26.14)
-44.87**
(12.32)
-75.91**
(13.90)
-52.07**
(9.14)
-91.70**
(12.94)
355.01**
(75.03)
108.45
(67.10)
295.50**
(96.51)
1109.11**
(265.30)
31,798
.490
.04
40.64*
(19.21)
24.78
(17.19)
31.96
(26.23)
202.45**
(58.74)
31,765
.467
-.05
72.94*
(28.81)
28.55
(25.95)
-54.07
(41.95)
131.04
(95.06)
31,767
.456
-.04
47.58**
(13.87)
61.77**
(12.35)
136.92**
(20.60)
211.98**
(42.15)
31,768
.403
-.05
58.16**
(18.29)
98.36**
(14.95)
185.08**
(26.93)
102.05*
(42.45)
31,767
.422
-.06
54.91**
(12.44)
48.68**
(9.50)
101.47**
(17.61)
22.33
(22.20)
31,768
.415
-.06
70.09**
(18.68)
73.98**
(13.85)
54.81
(24.97)
41.32
(27.42)
31,765
.454
-.07
N
R2a
Moran’s I
Spatially Weighted Regression; coefficients (standard errors)
* p<.05, ** p<.01
2
Table 2. Total Republican Contributions by Zip Code, 1992-2004
Total 2004
Total 2002
(Constant)
Network effects
Contributions of Neighboring Areas
Resources
Voting Age Pop of Zip Code
Percent Earning $150k Up
Percent over age 65
Median HH Income
Percent with 4 Year College Degree
Percent White
Incentives
Senate Competitiveness from CQWR
Presence of Senate Incumbent
House Competitiveness from CQWR
Presence of House Incumbent
Previous Competitiveness (County)
Total 2000
Total 1998
Total 1996
Total 1994
Total 1992
-28483.42**
(4294.75)
-10098.27**
(1417.66)
-17504.40**
(2349.27)
-8958.29**
(821.85)
-15770.85**
(1348.69)
-8874.25**
(699.14)
-5445.16**
(364.26)
0.62**
(0.01)
0.57**
(0.01)
0.58**
(0.01)
0.55**
(0.01)
0.58**
(0.01)
0.58**
(0.01)
0.37**
(0.01)
1.23**
(0.05)
4848.44**
(122.21)
543.07**
(65.21)
-571.72**
(54.85)
145.05
(88.05)
274.02**
(28.16)
0.66**
(0.02)
2314.92**
(52.98)
175.41**
(25.25)
-319.12**
(18.23)
148.02**
(31.70)
106.18**
(9.73)
1.27**
(0.04)
4715.53**
(93.47)
253.96**
(42.50)
-527.34**
(29.46)
69.55
(51.66)
207.79**
(15.21)
0.50**
(0.01)
1165.49**
(29.39)
76.64**
(14.86)
-182.98**
(10.04)
193.71**
(17.48)
67.58**
(5.35)
0.79**
(0.02)
2199.91**
(54.85)
123.03**
(25.75)
-339.98**
(17.48)
371.74**
(29.84)
116.82**
(9.00)
0.33**
(0.01)
629.20**
(23.90)
144.49**
(12.62)
-28.25**
(4.85)
156.96**
(14.43)
29.47**
(4.57)
0.33**
(0.01)
152.93**
(13.73)
79.71**
(6.83)
-9.80**
(2.72)
150.89**
(7.73)
6.54*
(2.54)
2627.46
(1091.18)
-621.45
(953.21)
5374.79**
(1720.81)
-3831.44*
(1975.85)
29.84
1456.77**
(379.42)
2192.20**
(352.91)
436.38
(508.27)
207.41
(372.99)
48.02**
496.98
(204.19)
-166.90
(201.19)
1286.56**
(286.80)
380.90
(201.96)
-4.32*
658.94
(326.81)
812.81*
(325.12)
1009.42*
(353.22)
240.63
(318.00)
-35.39
-143.93
(171.45)
-1112.21**
(172.40)
298.39
(194.48)
26.99
(164.43)
-18.67**
-415.83**
(97.50)
472.35**
(97.81)
171.48
(106.20)
5.19
(94.37)
-4.89**
-1234.78*
(577.80)
-1075.85*
(537.58)
1192.61
(884.07)
967.31
(533.75)
-1.21
3
Partisan context
Previous Democratic Vote (County)
Interests
Percent Farm
Percent in Manufacturing
Percent Professional and Executive
Percent Self Employed
(31.99)
(11.63)
(18.78)
(6.38)
(11.95)
(6.24)
(4.21)
-255.77**
(44.78)
-147.16**
(16.18)
-71.32**
(25.27)
-22.99
(8.63)
0.48
(15.97)
15.90
(8.21)
-0.16
(6.34)
168.48**
(58.55)
200.51**
(52.94)
331.69**
(76.68)
2353.81**
(209.41)
31,798
.361
-.02
-73.15**
(20.99)
30.16
(18.92)
15.77
(28.79)
419.96**
(64.61)
31,765
.350
-.02
18.94*
(11.57)
77.30**
(10.34)
125.42**
(17.26)
199.84**
(35.41)
31,768
.368
-.02
49.04*
(21.33)
135.52**
(17.31)
217.55**
(31.34)
93.33
(49.61)
31,767
.375
-.02
48.28**
(11.47)
43.92**
(8.71)
89.21**
(16.19)
1.68
(20.51)
31,768
.315
-.03
N
R2a
Moran’s I
Spatially Weighted Regression; coefficients (standard errors)
* p<.05, ** p<.01
-43.12
(33.25)
47.47
(29.86)
-92.14
(48.55)
90.48
(109.66)
31,767
.380
-.02
37.61**
(6.38)
24.41**
(4.72)
76.80**
(8.53)
-20.65
(9.40)
31,765
.329
-.04
4
5
6
7
Figure 5. Percent of Total Republican and Democratic Contributions
by Population Density Deciles of Zip Codes, 2000 and 2004
50.0
% of Total
40.0
30.0
20.0
10.0
0.0
1
2
3
4
5
6
7
8
9
Deciles of Population Density
Republican % 00
Democratic % 00
% Total Pop.
Republican % 04
Democratic % 04
10
8
Figure 6. Percent of Total Republican and Democratic Contributions
by Median Income Deciles of Zip Codes, 2000 and 2004
60.0
50.0
% of Total
40.0
30.0
20.0
10.0
0.0
1
2
3
4
5
6
7
8
9
Deciles of Median Income
Republican % 00
Democratic % 00
% Total Pop.
Republican % 04
Democratic % 04
10
Appendix
Contribution = α + β1 Population + β2 HighIncome + β3 MedianIncome + β4 Elderly + β5 Education
+ β6 Farm + β7 Manufacturing + β8 Executive + β9 SelfEmployed + β10White + β11 SenateCompetition
+ β12 SenateIncumb + β13 HouseCompetition + β14 HouseIncumb + β15CountyVote + β16 CountyCompetition
+ ρWContribution + error
Variable definitions from model:
Population: voting age population of zip code
High Income: percent earning $150,000 or more in zip code
Median Income: median household income of zip code
Elderly: percent over age 65 in zip code
Education: percent with a 4 year college degree or more in zip code
Farm: percent of working age population employed in farming in zip code
Manufacturing: percent of working age population employed in manufacturing in zip
code
Executive: percent of working age population employed as executives or professionals
in zip code.
Self Employed: percent of working age population that is self-employed in zip code
White: percent of population Caucasian in zip code
Senate Competition: 1,0 dummy evaluation of competitiveness of Senate seat from
Congressional Quarterly Weekly Report
Senate Incumbent: 1,0 dummy indicating open seat contest, or Senate incumbent
running for re-election
House Competition: 1,0 dummy evaluation of House seat competitiveness from
Congressional Quarterly Weekly Report
County Vote: average Democratic or average Republican vote for the past two
presidential elections from the county in which the zip code is located.
County Competition: 100-|(average Democratic vote-average Republican vote)| for
the past two presidential elections from the county in which the zip code is located.
Weighted Contribution: Weighted average of the contribution amount for the eight
nearest observations within a sixty mile radius.