Post on 13-Apr-2017
Abstract:
New technology has political consequences. This work seeks to better understand the newest
technological breakthrough, social media and the internet, as it relates to an established scholarly
literature; campaign advertising. This work seeks to uncover if a new form of campaign advertising via
social media and internet search engines, can be explained by previous theoretical and empirical work,
and if such advertising has unique properties. This paper seeks to test if Senate campaign spending on
social media can be explained via new variables, such as age and gender, that may help to better
understand the relationship between candidate preferences and rational advertising fund allocation.
Introduction
As most scholars of American politics are aware, in 1957 our discipline gave birth to a new
theoretical perspective known as rational choice.1 Outlined in his groundbreaking work, Anthony Downs
applied the logic of economics to politics. Now, over fifty years later, his work has grown to become a
contender for the dominate theoretical perspective in American politics, and it has even penetrated
several other subfields in the discipline of political science (Downs 1985).
Two decades later in 1974, another groundbreaking scholar David Mayhew, adopts the
Downsian framework to understand the incentive structure of House and Senate candidates. Mayhew
develops the concept of a proximate goal for all elected officials, and in doing so, he exposes that
rational elected officials have three methods available to them to pursue the proximate goal of
reelection; advertising, credit claiming, and position taking (Mayhew 1974).
In 2008, it was speculated by both scholars and journalists alike that the United States had
experienced an exogenous shock to its electoral system, precipitated by the use of social media and the
internet in electoral campaigns (see Dutta and Fraser 2008; Carr 2008; Elixer 2008; and Smith 2009 for
journalistic accounts, see Kushin and Yamamoto 2010 for a scholarly account). Since the 2008 election,
both journalists and scholars have been involved with attempting to puzzle out exactly if, how big, and
1 This data was selected as a convenient starting point, the exact date of the birth of rational choice can be disputed.
2
to what effect this exogenous electoral shock may have on future political campaigns in the United
States.
This paper seeks to help to unravel this causal link by employing the scholarly insights of rational
choice theory to explore one category of Mayhew’s typology; advertising.2 To accomplish both goals
(use of rational choice theory and exploration of social media’s impact on advertising), this work
examines the digital advertising of Senate campaigns in the three election cycles immediately following
the 2008 presidential election (2010, 2012, and 2014).3 This paper draws upon the preexisting
literatures methodological debates and theoretical insights to attempt to explain this new type of
advertising, which may have reorganized the means used by rational elected office seekers to gain
public office (see Jacobson 1981; Jacobson 1985; and Jacobson 1990 for an example of the preexisting
literature; Downs 1985).
This question regarding possible new means by which elected officials attain their proximate
goal, is broken down and systematically tested, in each section of this paper. With each section
examining a different piece of this question. Stated more formally, the research question of this work is;
“Can digital advertising be explained in the same manner as aggregate campaign spending? And does
the characteristics of the candidate influence the amount spent on digital advertising?”4
Following this introduction, will be a section on the theory of rational choice, and how its
authors have defined the incentive structure of elected officials, paying close attention to how the
concept of advertising, as defined by Mayhew, is understood. In this section hypotheses are derived
from the theory. After the theoretical section, will be a brief literature review of the advances in the
2 This will be defined more carefully in the next section. 3 For the purposes of this work, digital advertising is operationalized as the percentage of total campaign monetary resources spent on both social media advertising and search engine priority placement. 4 The terms advertising and spending are used very differently in this work. They will be defined in detail in a later section.
3
understanding of the campaign spending literature. This literature review will also cover the rather large
methodological debate that has spawned out of the disagreement on how to model statistically the
relationship between aggregate campaign spending and votes.5 Moving from the literature to the
empirics, the next section will cover the data and methods employed. This section discusses the source
of the data to be tested and the statistical model that will be employed to test the hypotheses. Next, is
the results section, the findings will be presented. Lastly, there will be a concluding section that
discusses the implications of the results and points to avenues of future research for other scholars.
Literature (And Methods) Review
One year after Mayhew publishes his foundational text on the rational elected official, Congress
passes the Federal Election Campaign Act. This act created the Federal Election Commission (FEC) which
is tasked with overseeing federal elections. For this paper, we are interested in the fact that one of the
stated goals of the FEC is to disseminate the campaign finance information that they collect. This
dissemination effort made the data available to scholars for testing research questions regarding
campaign spending (fec.gov).
Three years later (1978), Gary Jacobson publishes the first in-depth quantitative work, using
OLS, on the data that had just been released by the FEC. In this work, he finds that for every $10,000
spent by a challenger, they should expect to see a one percentage point increase in their total vote
share. The second main finding of this early work was altogether unexpected. The vote share of
incumbents, according to Jacobson’s findings, decreases when they spend more (Jacobson 1978). This
finding is directly opposed to Mayhew’s original work. Why would a rational incumbent spend money to
receive less votes (Mayhew 1974)?
5 While this work is heavily related to this literature, this work does not examine the direct causal relationship between advertising and votes.
4
Several years later, Jacobson, the highest profile and most influential scholar in this literature,
sought to retest his initial findings with newly available data from the FEC. However, he received the
same perplexing result, incumbents receive less votes the more they spend. In a third attempt by
Jacobson to explain this odd statistical result, he firmly places the blame on the statistics not the theory.
He identifies two main problems that he believes are the cause of this counterintuitive result. First is
diminishing returns, and second is the simultaneity problem (Jacobson 1985).
Diminishing returns, is that after a certain point, a covariates effect begins to decrease at high
levels. Ten thousand dollars no longer results in a consistent one percent gain in vote share once a
certain amount of the vote share is allocated. This statistical problem is very important, as it violates the
linearity assumption of the OLS model that Jacobson had been using up to this point. Jacobson notes
some very unsatisfying fixes to this problem, such as, logging the dependent variable and using
quadratic terms. However, while these attempted fixes to the problem of the violated linearity
assumption, they also impose a new problem. The ceiling for the diminishing returns is placed to low
and actually hurts the models performance (Jacobson 1985).
The simultaneity problem is another method’s problem outlined by Jacobson. The simultaneity
problem is that the statistical models used are consistently able to find counterintuitive results for
incumbents. To explain the result of negative votes for more spending for incumbents, Jacobson posits a
new theoretical explanation, the expected vote. The expected vote, based on the perceptions of the
candidates by voters and donors, is what produces the contrary results. In theory, the expected vote
influences the behavior of donors and voters in a race. The behavior of donors and voters creates a
perceived outcome of the election. If the challenger is perceived as strong, he will receive more
donations, while the incumbent receives less and vice versa. This effects the ability to model the
votes/spending relationship. Additionally, Jacobson argues that incumbents also saturate the voters
5
prior to the campaign, thus preventing the model from picking out the effect of incumbent spending
during the campaign (Jacobson 1985).
To deal with the problem of simultaneity, the literature began using Two Stage Least Squares
(2SLS) models to deal with simultaneity when modeling the votes/aggregate campaign spending
relationship. However, this method was harshly criticized for improper instrumental variable selection.
Some scholars were so harshly criticized as to become examples of how not to conduct 2SLS (Jacobson
1985; Jacobson 1990; and Green and Krasno 1988). With the near embarrassment of Green and Krasno
for their 1988 article by Jacobson (1990), other authors began to make other small changes to how
these types of models were estimated, or simply employ OLS. These innovations often included new
control and dummy variables.
Examples of these new control variables includes a control for the amount of money spent by
the challenger. This was added to help to control for disparities between incumbent and challenger
spending. This variable assists by sifting out some of the impact of the simultaneity problem. An
example of a new dummy variable added to the literature is the control for an open seat election, which
usually sees higher spending than a race with an incumbent. These two variables were suggested by
Alan Abramowitz (1989), and are now staples in the literature.
Two teams of scholars made rather large improvements statistically in the campaign spending
literature. These are Robert Erikson and Thomas Palfrey, and Kenneth Benoit and Michael Marsh.
Erikson and Palfrey attack the problem of simultaneity by separating races into both marginal and non-
marginal races. What they find is that marginal races are not effected by simultaneity, and they even
posit that OLS models can estimate the votes/spending relationship for these races. However, a caveat
exists. If the incumbent to challenger vote share is not within a specific range of 50/50 to 55/45%, then
simultaneity is found and the race cannot be estimated with OLS (Erikson and Palfrey 2000). Benoit and
6
Marsh expand on this idea, by noting that incumbents have incumbency prerequisites, such as, staff
time, reimbursable travel, postage, etc.6 They note that the effectiveness of these tool vary across both
marginal and non-marginal races, and could even be the cause of the simultaneity problem itself (Benoit
and March 2008)
Taking a page from Jacobson’s later work, some scholar’s sought to innovate on the theory side
of the equation instead of the statistical side. Taking a bit of a different interpretation on rational choice
theory, Stephen Ansolabhere argues that our current line of questioning is mistaken. Instead of asking
why rational incumbents spend when they receive no benefit, scholars should instead investigate
spending as a consumer good. By reframing spending in this way, it can be seen that regardless of the
outcome, voters, donors, and possibly candidates still receive an increase in their utility for engaging in
their respective activities. By framing the question this way, he argues that the votes/spending
relationship is not the most fascinating one (Ansolabhere, Figueiredo, and Snyder 2003).7
One non-American scholar took both lines of improvement (theoretical and statistical) and
assisted in both efforts. Theoretically, Woojin Moon found that there are two types of incumbents, safe
incumbents and weak incumbents. Each faces a different challenge when seeking to spend money to
gain votes. Safe incumbents must swing extreme voters, which is very expensive. For weak incumbents,
they have to swing moderate voters, which is less expensive. While this idea of two types of incumbents
is not new (See Zaller 1992), this its first application to the campaign spending literature. Following this
theoretical addition, Moon finds that Erikson’s marginal election vote share is essentially correct, except
he expands it from 55/45 to 60/40% (Moon 2006).
6 This includes franking and 499’s. 7 This work agrees with this conception of the current literature and seeks to be an empirical example of this new theoretical formulation of the argument.
7
The last group of scholars takes the campaign spending literature and tries to reconcile its
divergent results (or lack thereof) with the political media literature. The campaign spending literature
cannot seem to find consistent results for a votes/spending model, while political media scholars find a
strong correlation between advertising (purchased with funds) and votes. Some of the most recent work
in the campaign spending literature comes from these hybrid works (Goldstein and Freedman 2000;
Stratmann 2009). However, the political media literature comes with its own methodological problems.
Scholars in this literature are fiercely divided over causality (Edwards 2011; Franz et al. 2008; Kaid et al.
2007; Kaid, Fernandes, Painter 2011; Krasner and Green 2008; and Pfau et al. 2001).
This paper seeks to take points from both literatures. By rejecting the expected vote argument
of Jacobson, instead a relationship similar to Ansolabhere, Figueiredo, and Snyder’s is posited (2003;
Jacobson 1985). The causal relationship currently used by the campaign spending literature will be
altered in this work. However, the relationship as noted by scholars of political media, is also
inappropriate. The next section turns to these problems and others of a theoretical nature.
Theory
As noted in the introduction, this work adopts a rational choice framework as its theoretical
foundation. Rational choice of the Downsian variety, is often famous (or notorious) for several key
assumptions about the political world. The most important being the rational actor assumption, where
all individuals are assumed to be utility maximizers. A rational actor seeks to increase his utility, which
can be anything not simply financial in nature, by making the best choices available. But, this is not a
perfect relationship, as the information needed to make the correct choice is costly to attain, and
uncertainty abounds (Downs 1985).
This theory provides the perfect grounding for this paper as these very assumptions would
indicate that a rational elected official or challenger would adjust his or her strategy to the most
8
effective one. Assuming that digital spending has in some way altered the status quo, then it might be
expected that rational candidates would adopt a certain percentage of social media spending to
supplement other methods. It is from the rational choice perspective that the hypotheses in this paper
are derived. Additionally, the rational choice framework also explicitly rejects the idea of modeling the
ends any rational actor seeks to obtain. Only, according to Downs, the means used to achieve them can
be evaluated [this point becomes highly important later] (Downs 1985).
In his extension of Downs’s work, Mayhew developed a very influential typology for the types of
activities that an elected official or challenger can engage in to achieve their proximate goal of
(re)election. This work examines one of the three, which Mayhew termed advertising. According to
Mayhew, advertising is defined as, “...any effort to disseminate one’s name among constituents in such
a fashion as to create a favorable image…” (Mayhew 1974, 49). It is important to note here that
Mayhew’s original definition called for advertising to be mostly free of issue content, and instead to
emphasize candidate characteristics such as experience, knowledge, concern, etc. This work relaxes this
definition of advertising and includes those that have some issue content. By relaxing the definition in
this way, it allows for a larger section of advertising to be examined in the data.
The decision to use the term advertising, an older term used in the literature, has been selected
here for a reason. In the more modern literature, the term campaign spending is used (Benoit and
Marsh 2008; Woojin 2006; Stratmann 2005). Typically, this term is associated with work that seeks to
model how aggregate spending impacts the votes or vote share a candidate receives. As this work does
not model votes, but seeks to explain how a certain type of spending might be explained, the use of
different terminology helps to clearly set this work apart from the literature on campaign spending. The
use of this term also helps to note that given the theory selected here, to model spending’s impact
(means) on votes (ends) is theoretically inappropriate. The relationship tested here is more direct, and
models the impact of actor preferences and characteristics on spending choices (means), and is not
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mediated by the expected vote (Downs 1985, Jacobson 1985). Figure 1 (below) graphically represent the
differences in the two versions of the causal chains.
Figure 1: Causal Chain Comparison
While most work in the old causal framework, noted in Figure 1, focuses on aggregate campaign
spending, some exceptions do apply. Most notably is the work of van Heerde et al, who separates
campaign spending into separate categories to rank their effectiveness. This work borrows heavily from
van Heerde’s division of spending, but adds an additional one, digital spending, to the proposed
typology. The rise of the use of social media (313 million active Twitter users, 500 million Instagram
users, and one billion Facebook users) has been rapid and wide spread. This paper examines the impact
of this usage on elected official’s rational calculations on the allocation scarce resources effectively,
given uncertainty and costly information (Downs 1985; Van Heerde, Johnson, and Bowler 2006; Twitter
– About; Instagram – About; Facebook – About).
From the literature discussed in the previous section, specific testable hypotheses can be
derived and tested statistically. The first, relates to the established items in the literature like the
additional variables proposed by Abramowitz (1989) and others. The question here then, is can the
established variables be used to explain this relatively new form of advertising, and if so which ones may
apply or not apply. From this, the first hypothesis can be formalized as:
Campaign Spending Causal
Chain
Expected Vote
SpendingVote Share
Revised Causal Chain
PreferencesSpending Choices
10
Hypothesis #1: Do the variables used to explain aggregate campaign spending (open seat, quality challenger, etc.) also explain the percentage of digital advertising by a campaign?
The second hypothesis is derived indirectly from Kushin and Yamamoto’s (2010) work on the
impact of social media on voters in the younger age brackets. This seems to imply that age is a factor in
the use of social media. If this is applied to the advertising context, it seems appropriate to assume that
the age of a candidate may affect the percentage of digital spending. This point can even be justified
theoretically, in that rational choice theory would assume that the information costs to effectively use
social media (and thus digital spending) may be higher for campaigns with a candidate that is older. This
leads to the second hypothesis:
Hypothesis #2: As the age of a candidate increases, so do the information costs with adopting new advertising technology, thereby decreasing the percentage of digital advertising by the campaign.
The third and final hypothesis, is also based on a characteristic of the candidate, gender. As
Anzia and Berry (2011) have found, female candidates for elected office tend to be better candidates,
due to self-selection processes. If this finding is applied to digital spending, it could easily be assumed
that a female Senator might be better equipped than her male counterparts to overcome the barriers of
uncertainty and information costs. This leads to the third hypothesis:
Hypothesis #3: Female Senatorial candidates more easily overcome uncertainty and information costs due to higher skill levels, and thus are more likely to adopt higher percentages of digital advertising as a means to gain elected office.
Data and Methods
For this paper, the data that has been chosen to be analyzed is from the Federal Election
Commission (FEC). These data provide a list of all types of expenses that must legally be reported by
campaigns running for US senate. The years selected for this paper are the 2010, 2012 and the 2014
election cycles (the 2016 data is incomplete). The FEC campaign disbursement data has been used
extensively in the literature. Examples include Jacobson’s path breaking work in 1985 and many others
(Jacobson 1985; Thomas 1989; Abramowitz 1989; Benoit and Marsh 2008; Coleman and Manna 2000;
11
Gerber 1998; Goldstein and Freedman 2000; Kenny and McBurnett 1992; Levitt 1994; Moon 2006;
Stratmann 2009). Specifically, this paper examines official campaign disbursements. Thus, Political
Action Committee (PAC) and political party advertising are excluded from this analysis, due to
Ansolabhere, Figueiredo, and Snyder’s (2003) findings.
The 2010 through 2014 data on US Senate expenditures covers a full rotation of the US Senate.
This allows us to test the three derived hypotheses against observations from three pooled cross-
sections. This data provides another benefit in that is covers both types of elections, mid-term elections
(2010 and 2014) and presidential (2012), which provides increased generalizability to the results. Given
that this data is immediately preceding the 2008 presidential election, viewed as the watershed moment
(Dutta and Fraser 2008; Carr 2008; Elixer 2008; Smith 2009; Kushin and Yamamoto 2010), this data set
provides the perfect place to test the hypotheses regarding digital spending.
Table 1: Summary Statistics
Variable N Mean St. Dev. Se(Mean) Max Min
Percentage of Digital Spending 133 3.53 7.57 .66 80.54 .0009 Democrat 133 .511 .50 .04 1 0 Challenger 133 .699 .46 .04 1 0 Open Seat 133 .338 .47 .04 1 0 Population 133 6.62 7.88 .68 38.8 .584 State 133 17.36 9.97 .86 36 1 Age 128 59.97 10.67 .94 83 38 Gender 133 .789 .41 .035 1 0 Competitive Primary 133 .075 .26 .023 1 0 Competitive General 133 .286 .45 .039 1 0 2010 133 .406 .49 .043 1 0
Data from fec.gov
As is shown in Table 1, the coding schemes used for the dependent variable is the percentage of
advertising funds spent on digital advertising. The coding of the various independent variables is also
listed. Most of these coding schemes are relatively straight forward, with the exception of the
population variable. Population is coded in the millions per state. So, state X has 45 million people
(coded as 45) while state Y has half a million people (coded as 0.5). Also worth noting is the coding
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scheme used for the general election competitive variable, which is operationalized as a 1 if the race
was within a 10% margin of victory for the incumbent and zero for races outside this margin.
As noted in the literature review section, most scholars in the campaign spending literature use
OLS estimation with either quadratic terms, logged variables, or adopt 2SLS. Both versions of OLS are
inappropriate as the ceiling for the diminishing returns is to low, and are thus do not yield good
estimates. Also, given the structure of the dependent variables used in this literature, percentage of the
vote, vote share, or percentage of advertising, the use of OLS will result in biased coefficients. Given that
the dependent variable is censored (the predicted values cannot exceed 100% or be below 0%), OLS is
also inappropriate. While it is possible to remove the censored observations from the data and then use
OLS, this results in the sample data becoming a non-random subset and is not appropriate (Breen
1996).8
With a censored dependent variable, the most appropriate estimation technique for the
campaign spending literature, and this project specifically, is maximum likelihood tobit regression
analysis. This is because the tobit model assumes two groups in the data, N1 where a campaign spent
some, and N0 where a campaign spent none9. This gives the model a leg up over OLS as the tobit model
assumes that the N0 group is not random. This allows the tobit model to produce unbiased estimates for
observations where the campaign spent zero dollars on digital advertising (Breen 1996).
Under the N1 and N0 groups, the tobit model assumes there is a latent variable (the true
percentage spent of digital advertising) which is equal to an observed variable, given certain constraints.
The equation for the tobit model is displayed in Equation 1 below (Breen 1996).10
8 An OLS model was estimated without the censored observations and the results were not significantly different from the regular OLS model. This does not however, justify disregarding the possibility of biased coefficients. 9 In the data the NO group was a large segment, further justifying tobit regression. 10 The latent variable formulation of the tobit model equation is included in the appendix.
13
Equation 1: Tobit Model
𝒚𝒊 = (
𝒚𝒊∗ 𝒊𝒇 𝒚𝑳 < 𝒚𝒊
∗ < 𝒚𝑼
𝒚𝑳 𝒊𝒇 𝒚𝒊∗ ≤ 𝒚𝑳
𝒚𝑼 𝒊𝒇 𝒚𝒊∗ ≥ 𝒚𝑼
)
Tobit makes two very important assumptions that are often used to criticize its use. These are 1)
that it assumes independent and normally distributed errors with a mean of zero, and 2) a constant
variance.11 Some claim that this makes the model ‘fragile’ as these error assumptions are not often met.
However, to use OLS with a censored dependent variable is far worse than to not even attempt to
model the data correctly (Breen 1996).
As to why 2SLS estimation is not used in this paper, it must be noted that 2SLS is not a maximum
likelihood technique, but a derivative of the OLS model. Given that maximum likelihood estimators
perform better than two step estimators, such as 2SLS or the Heckman Two Step Estimator, the most
appropriate model to test the hypotheses is maximum likelihood tobit regression analysis (Breen 1996).
The tobit model used here is a left and right censored model that sets the left censoring value at
zero and the right censoring value at 100, due to the nature of the dependent variable.12 Additionally,
the tobit model used here also employs clustering and robust standard errors. The model is clustered by
state to deal with the relationship that exists between the candidates running in the same state wide
election. Robust standard errors are employed here to deal with the correlation between the
independent variables.13
Results
11 Tests indicate that the variance is flat and that the error is not normally distributed. 12 See Equation 1 above. 13 The correlations between the independent variables do not exceed 2.7, and the majority are highly uncorrelated.
14
Looking back at Table 1, it is important to note that the dependent variable ranges from nearly
zero to eighty percent, with a mean of about 3.5 percent. This helps to put the results from the model
into perspective. Thus, it can generally be understood that digital spending is, at least for the mean
Senator, not a large segment of the total campaign budget.
Digging deeper, Table 2 (below) shows that the first hypothesis, regarding the general
effectiveness of various control variables, such as party ID, quality challenger, both competitive
variables, and challenger status are not statistically significant, nor are they substantively significant.
Thus, these tried and true variables from the literature do not assist in explaining digital spending. Thus,
it appears that the first hypothesis can be safely put to rest as not holding true (an exception applies
here and will be discussed shortly).14
Table 2: Model Results
Variable Tobit Model Percent Digital b/se
Population 0.014
(0.05)
Age 0.03 (0.03)
Democrat -0.606 (1.19)
Challenger 0.189 (1.26)
Open Seat 2.052* (1.04)
Gender 0.261 (0.98)
Quality Challenger -1.237 (1.37)
Competitive Primary 8.043 (6.99)
Competitive General -0.085 (0.98)
2010 -0.966 (1.65)
2012 -0.859 (0.96)
2014 . .
Constant 1.238 (2.48)
Sigma . Constant 6.978*
(2.67)
14 The model was estimated separately for the two major political parties, and the results remained consistent.
15
AIC 886.61 BIC 924 N 128
The second hypothesis, regarding the impact of a candidates age on the cost of information and
the relative level of uncertainty surrounding digital spending can be seen as not true. So, age has little to
no impact on a Senator’s ability to employ digital spending, and if they do, it is likely to be a small
percentage. This has implications that are linked to the incumbency prerequisites, and may even
propose an interesting question, about the impact of staff members, who may make up for the
candidates lack of knowledge on new technology (Downs 1985).15
The third hypothesis, which argued that gender may have an impact on digital spending through
the self-selection process (females being more skilled than their male counter parts), does not bear
statistical fruit in the results. Gender is not a significant predictor of the amount of digital spending
selected by a campaign. This seems to indicate that at least when it comes to the selection of where to
advertise, both male and female candidates seems to have similar profiles (Anzi and Berry 2011).16
While the three hypotheses generated in the theory section of this paper do not bear any fruit,
there is one interesting result that can be gleamed from the data. According to Table 2, the open seat
variable is significant at the .05 level. Based on the coefficient value, it can be determined that a one
percent increase in digital spending results in a 2.05 increase in the predicted value of digital spending.
This statistical result is further proof that the work of Abramowitz is correct, the open seat control
variable is vitally important in the campaign spending literature and in other related work such as this
one. This results can be explained via the argument that open seat elections see a larger amount of
15 The model was estimated separately for Senators past the age of mandatory retirement (65) and those who were below this age. The results remained consistent with the results shown. 16 The model was estimated separately for each gender, and the results remained consistent with the ones shown.
16
spending on advertising than races that have an incumbent candidate (Abramowitz 1989; Erikson and
Palfrey 1998; Levitt 1994).
Conclusion
In the previous sections, it was found that the three hypotheses put forth were not
substantiated in the data. With the exception of the open seat variable, which confirms the work of
Abramowitz (1989), the literature derived variables do not perform as expected. Often used variables
such as quality candidate, among others, were not significant. Additionally, the variables that were
theorized to impact digital spending (age and gender) did not perform as theorized.
Thus, despite the rampant speculation by both journalists and scholars that social media and
other forms of digital spending have a high impact is untrue, based on the results of this paper. It
appears that such a claim is at best highly limited. The election of 2008 does not seem to alter the
means used by rational political candidates by much, signified by the fact that the mean percentage of
digital spending is 3.5% of total spending. While it does not appear to have a large impact in the election
cycles immediately preceding 2008, there is an argument to be made that a possible learning curve may
exist for political campaigns. Thus, these null results should be interpreted with skepticism and tested
again when more data is available.
However, the merits of work such as this, and others (van Heerde, Johnson, and Bowler 2006)
do seem to indicate that disaggregating advertising into its components is a fruitful line of scholarship.
Given the results from this paper, future work could investigate the other more important forms of
spending such as, media or canvassing. Given the small mean for digital spending, the question remains,
what other forms of spending make up larger percentages, and how do they vary given candidate
preferences, characteristics, information costs, and uncertainty.
17
This work does however contribute a valuable theoretical point. The typical causal chain used in
the campaign spending literature is inaccurate and needs to be revised. This paper argues for a similar
approach to the work of Ansolabhere, Figueiredo, and Snyder (2003), in that advertising needs to be
viewed as a strategic choice of rational political actors on where to allocate funds. While it may be
tempting to model votes similar to others in the literature (Jacobson 78; Jacobson 1981; Jacobson 1990;
Green and Krasno 1988, and others), but this theoretical construction of the causal relationship is
inaccurate from the rational choice perspective, as the ends of a rational actor are beyond the pale
(Downs 1985). The causal chain employed in this work more accurately reflects both the spirit of rational
choice, and also the observed relationship in reality.
18
References
Abramowitz, Alan I. 1989. "Campaign Spending in US Senate Elections." Legislative Studies Quarterly
14:4 487-507 JSTOR.
"About Us." About Us • Instagram. Accessed October 8, 2016. https://www.instagram.com/about/us/.
“About the FEC.” Accessed October 8, 2016. fec.gov/about.shtml
Ansolabehere, Stephen, John M. de Figueiredo, and James M. Snyder. 2003. "Why Is There So Little
Money in U.S. Politics?" The Journal of Economic Perspectives 17:1 105-130 JSTOR.
Anzia, Sarah F., and Christopher R. Berry. "The Jackie (and Jill) Robinson effect: why do congresswomen
outperform congressmen?." American Journal of Political Science 55, no. 3 (2011): 478-493.
Dutta, Soumitra, and Matthew Fraser. "Barack Obama and the Facebook Election." US News. November
19, 2008. Accessed October 18, 2016.
Benoit, Kenneth, and Michael Marsh. 2008. “The Campaign Value of Incumbency: A New Solution to the
Puzzle of Less Effective Incumbent Spending.” American Journal of Political Science. 52:4. 874-
890. JSTOR.
Breen, Richard. ”Regression Models. Quantitative Applications in the Social Sciences.” SAGE
Publications, Inc., 1996.
Coleman, John J., and Paul F. Manna. 2000. “Congressional Campaign Spending and the Quality of
Democracy.” The Journal of Politics. 62:3. 757-789. JSTOR.
Downs, Anthony. An Economic Theory of Democracy. Addison-Wesly. Boston, MA. 1985.
Erikson, Robert S., and Thomas R. Palfrey. "Equilibria in campaign spending games: Theory and data."
American Political Science Review 94, no. 03 (2000): 595-609.
19
-----------, and Thomas R. Palfrey. "Campaign spending and incumbency: An alternative simultaneous
equations approach." The Journal of Politics 60, no. 02 (1998): 355-373.
Mayhew, David R. Congress: The Electoral Connection. 2nd ed. New Haven: Yale University Press, 1974.
Federal Election, Commission. 2015. Candidate Disbursements. Accessed October 1, 2015.
http://www.fec.gov/data/CandidateDisbursement.do.
"Facebook: About." Accessed September 8, 2016.
https://www.facebook.com/pg/facebook/about/?ref=page_internal.
Franz, Michael, Paul Freedman, Ken Goldstein, and Travis Ridout. 2008. "Understanding the Effect of
Political Advertising on Voter Turnout: A Response to Krasno and Green." The Journal of Politics
70:1 262-268.
Gerber, Alan. 1998. “Estimating the Effect of Campaign Spending on Senate Election Outcomes Using
Instrumental Variables.” The American Political Science Review. 92:2. 401-411. JSTOR.
Grier, Kevin. Campaign Spending and Senate Elections, 1978-1984. Public Choice. Vol. 63. No. 3. (1989).
Pp. 201-219. JSTOR. Accessed Feb, 12 2016.
Goldstein, Ken, and Paul Freedman. 2000. "New Evidence for New Arguments: Money and Advertising in
the 1996 Senate Elections." The Journal of Politics 62:4 1087-1108 JSTOR.
Hollander, Barry A. "The Role of Media Use in the Recall Versus Recognition of Political Knowledge."
Journal of Broadcasting & Electronic Media 58, no. 1 (2014): 97-113.
Carr, David. "How Obama Tapped Into Social Networks’ Power." The New York Times. November 09,
2008. Accessed October 11, 2016.
20
Jacobson, Gary. Incumbents’ Advantages in the 1978 U.S. Congressional Elections. Legislative Studies
Quarterly. Vol. 6 No. 2 (May 1981). pp. 183-200. JSOTR. Accessed Feb. 12, 2016.
----------. 1978. "The Effects of Campaign Spending in Congressional Election." The American Political
Science Review 72:2 469-491 JSTOR.
-----------. The Effects of Campaign Spending in House Elections: New Evidence for Old Arguments.
American Journal of Political Science. Vol. 34. No. 2. (May 1990) pp. 334-362. JSTOR. Accessed
Feb, 12 2016.
-----------. 1985. "Money and Votes Reconsidered: Congressional Elections 1972-1982." Public Choice
47:1 7-62 JSTOR.
Kaid, Lynda, Juliana Fernandes, and David Painter. 2011. "Effects of Political Advertising in the 2008
Presidential Campaign." American Behavioral Scientist 55:4 437-456.
Kaid, Lynda, Monica Postelnicue, Kristen Landerville, Hyun Yun, and Abby LaGrange. 2007. "The Effects
of Political Advertising on Young Voters ." American Behavioral Scientist 50:9 1137-1151.
Kazee, Thomas A. "The Impact of Electoral Reform: "Open Elections" and the Louisiana Party System."
Publius 13, no. 1 (1983): 131-39. http://www.jstor.org/stable/3330075.
Kenny, Christopher, and Michael McBurnett. 1992. “A Dynamic Model of the Effect of Campaign
Spending on Congressional Vote Choice.” American Journal of Political Science. 36:4. 923-937.
JSTOR.
Krasno, Jonathan, and Donald Green. 2008. "Do Televised Presidential Ads Increase Voter Turnout?
Evidence from a Natural Experiment." The Journal of Politics 70:1 245-261.
21
Kushin, Matthew James, and Masahiro Yamamoto. "Did social media really matter? College students'
use of online media and political decision making in the 2008 election." Mass Communication
and Society 13, no. 5 (2010): 608-630.
Kuzenski, John C. "The Four. Yes, Four. Types of State Primaries." PS: Political Science and Politics 30, no.
2 (1997): 207-08.
Levitt, Steven D., 1994. “Using Repeat Challengers to Estimate the Effect of Campaign Spending on
Election Outcomes in the U.S. House.” Journal of Political Economy. 102:4. 777-798. JSTOR.
Lott, John R. Jr. 2000. “A Simple Explanation for Why Campaign Expenditures Are Increasing: The
Government Is Getting Bigger.” The Journal of Law and Economics. 43:2. 359-394. JSTOR.
Moon, Woojin. 2006. "The Paradox of Less Effective Incumbent Spending: Theory and Tests." British
Journal of Political Science 36:4 705-721 JSTOR.
"Obama Wins the 2008 Presidential Election - in Social Media - Elixir Interactive." Elixir Interactive.
October 04, 2008. Accessed September 18, 2016. http://elixirinteractive.org/obama-wins-2008-
presidential-election-social-media/.
Pfau, Michael, David Park, Lance Holbert, and Jaeho Cho. 2001. "The Effects of Party- and PAC-
Sponsored Issue Advertising and the Potential of Inoculation to Combat Its Impact on the
Democratic Process." American Behavioral Scientist 44:12 2379-2397.
Shen, Fuyuan, Frank Dardis, and Heidi Edwards. 2011. "Advertising Exposure and Message Type:
Exploring the Perceived Effects of Soft-Money Television Political Ads." Journal of Political
Marketing 10:3 215-229.
Shepard, Lawrence. "Does campaign spending really matter?." Public Opinion Quarterly 41, no. 2 (1977):
196-205.
22
Smith, Aaron. "The Internet's Role in Campaign 2008." Pew Research Center: Internet, Science & Tech.
April 15, 2009. Accessed October 18, 2016. http://www.pewinternet.org/2009/04/15/the-
internets-role-in-campaign-2008/.
Stewart III, Charles, and Mark Reynolds. "Television markets and US Senate elections." Legislative
Studies Quarterly (1990): 495-523.
Stratmann, Thomas. "Some talk: Money in politics. A (partial) review of the literature." In Policy
Challenges and Political Responses, pp. 135-156. Springer US, 2005.
----------, Thomas. 2009. "How prices matter in politics: the return to campaign advertising." Public
Choice 140:3 357-377 JSTOR.
Thomas, Scott J., 1989. “Do Incumbent Campaign Expenditures Matter?.” The Journal of Politics. 51:4.
965-976. JSTOR.
"Company | About - Twitter About." Accessed September 8, 2016. https://about.twitter.com/company.
Van Heerde, Jennifer, Martin Johnson, and Shaun Bowler. "Barriers to participation, voter sophistication
and candidate spending choices in US Senate elections." British Journal of Political Science 36,
no. 04 (2006): 745-758.
Zaller, John. 1992. The Nature and Origin of Mass Opinion. New York: Cambridge University Press.
23
Methods Appendix
Latent Variable notation of tobit regression model.
𝒚𝒊∗ = 𝑿𝒊
′𝜷 + 𝒖𝒊
𝒖𝒊 ~ 𝑵 (𝟎, 𝝈𝟐)
𝒚𝒊 = 𝒚𝒊∗ 𝒊𝒇 𝒄𝟏 ≤ 𝒚𝒊
∗ ≤ 𝒄𝟐
𝒚𝒊 = 𝒄𝟏 𝒊𝒇 𝒄𝟏 > 𝒚𝒊∗
𝒚𝒊 = 𝒄𝟐 𝒊𝒇 𝒄𝟐 < 𝒚𝒊∗
The probability the latent variable exceeds the threshold (the upper and lower limits)
𝒑𝒓(𝒚𝒊∗ > (𝒎) = 𝒑𝒓(𝒙𝒊
′𝜷 + 𝒖𝒊 > (𝒎)
= 𝒑𝒓(𝒖𝒊 > 𝒄𝒎 − 𝒙𝒊′𝜷)
= 𝟏 − 𝚽 (𝒄𝒎 − 𝒙𝒊
′𝜷
𝝈)
Thus;
𝒚∗ > 𝒄𝟏 = 𝟏 − 𝚽(𝒄𝟏)
𝒚∗ < 𝒄𝟐 = 𝚽
Test of the mean error at zero assumption for tobit regression model
Mean St. Err. 95% Confidence Interval
Error 3.33 .21 2.91 3.75
This test shows that the tobit model does violate the assumption that the mean error is at zero.
Model performance test using the standard deviation of the dependent variable and the sigma value.
. 322 = .102 → 10%
This indicates that the tobit regression model’s predicted values share approximately 10% of their
variance with the dependent variable (digital spending).