Campaigning and Election Outcomes: Evidence From
the 2008 Democratic Primaries
Steven Bednar∗
June 6, 2014
Abstract
This paper presents new evidence from the US presidential primary setting
on the role campaigning plays in determining election outcomes. I develop novel
instruments based on Democratic Party rules for delegate allocation to account
for the endogeneity of campaign activity. Campaign visits are used as a measure
of campaign intensity in order to deal with the problem of measurement error that
often arises when the focus is on campaign spending. I estimate a discrete choice
model of voting where I allow for abstention. On average, a visit by a candidate
increases the vote share of this candidate by about 2.4 percentage points and
decreases the abstaining share by 0.7 percentage points.
∗
Elon University. 2075 Campus Box, 27244. [email protected]. 336-278-5935
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1
Introduction
This paper examines the impact of campaigning on vote shares and turnout in the US
presidential primary setting using the number of visits a candidate makes to a congressional district as a measure of campaign intensity. Much of the research on primary
elections focuses on momentum (Abramowitz 1989) and candidate policy positioning
(Hummel 2013). However, little is known about the effects of campaign activity on
voting outcomes in presidential primaries. Numerous studies document small effects in
general, senate and house elections. It is possible that campaigning will have a larger
effect when individuals have to choose a candidate from within their own party (Haynes
et al. 1997).
Studying campaign visits has the advantage that the exact location of the events
is known, which allows a more precise measure of the group of voters exposed to the
resource than is possible with campaign spending, lessening the effects of measurement
error that normally arise in a study of campaign spending. There is a small literature
on campaign visits (Shaw 1999, Jones 1998, Herr 2002, Chen and Reeves 2011) but none
have considered the primary setting where the same candidates face off multiple times.
I estimate a discrete choice model of voting where I allow for abstention (Hansford
and Gomez 2010, Basinger et al. 2012). I exploit Democratic Party rules for delegate
allocation to derive instrumental variables to deal with the endogeneity of campaign
intensity.
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2
Background on 2008 Democratic Presidential Primaries
Barack Obama and Hilary Clinton were the only two viable candidates from Super
Tuesday until the end of the primary setting. They faced off in 388 different congressional
districts over a four month period, competing for delegates, 75% of which are awarded
proportional to the vote within the district and 25% of which are awarded proportional
to the statewide vote. Each state is allocated delegates based on its number of electoral
college votes and previous Democratic voter turnout in general elections. States must
allocate their delegates to congressional districts based on a function of Democratic
vote in previous presidential elections, gubernatorial elections and Democratic Party
registration. Whether the district has a large or small number of delegates is based on
the past actions of the voters within the district. Whether the district has an odd or
even number of delegates is due to integer constraints and rounding. Table 1 shows
summary statistics broken down by whether the district has an odd or even number of
delegates. t-tests do not reject equal means for odd and even districts for any of the
demographic variables included in my model.
This structure provides an incentive for candidates to campaign in districts with an
odd number of delegates if the election is expected to be close as receiving just over
half of the votes results in winning one more delegate than the opponent. In districts
with an even number of delegates the candidates split the delegates evenly unless one
candidate wins by a sizable margin. Over half of the districts in this election ended up
being decided by a small margin. Additionally, campaigning in districts with a larger
number of delegates help candidates win delegates allocated proportional to the state
wide vote. Temporal variation in primary dates places additional constraints on the
amount of time available to campaign in a district.
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Table 1: Summary Statistics by Odd and Even Number of Delegates
Percent over 65
Percent Black
Percent with a Bachelor Degree
Unemployment Rate
Median Income
Market Size (Million TVs)
Days to Campaign per District
N
Odd
0.122
(0.029)
0.121
(0.148)
0.243
(0.095)
0.041
(0.018)
43,487
(12, 155)
1.894
(2.029)
0.362
(0.796)
185
Even
0.120
(0.026)
0.116
(0.142)
0.248
(0.093)
0.040
(0.014)
44,443
(10, 954)
1.691
(1.671)
0.290
(0.670)
203
p-value
0.546
0.732
0.571
0.490
0.416
0.280
0.168
p-value from test of equal means. Standard deviations are in parenthesis.
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Model
I model the voting decision with a discrete choice differentiated products framework.
With this framework I can estimate the effect of a visit on both two-candidate vote
shares as well as turnout. Individuals can vote or not vote, {v, nv}. Conditional on
voting, the individual must vote for candidate j ∈ {c, o}. Individual i in district d
receives utility from voting for candidate j according to the following linear form:
uijd = γvisitsjd + Xd β + Dijd λ + ξjd + ζiv + εijd .
(1)
visitsjd is the number of visits by candidate j in district d.
Xd are district specific variables that affect the attractiveness of performing the act
of voting. For example, adverse weather on the day of the election could keep individuals from visiting the polls (Gomez et al. 2007). Dijd is the individual’s demographic
characteristics interacted with the candidate dummy. It is possible that certain groups
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of individuals will favor a candidate based on having similar characteristics.
Finally there are the unobservable terms. ξjd is a candidate specific unobservable
which is assumed to be normally distributed. ζiv is the unobserved utility from voting.
εijd is an error term distributed type-I extreme value. This error structure leads to
well known logit vote share equations which can be inverted to form an estimable linear
equation (Berry 1994).
ln(sjd ) − ln(snvd ) = γvisitsjd + Xd β + Djd λ + ξjd ,
(2)
The left hand side of equation (2) is the natural log of the ratio of candidate vote
share of the citizen voting age population to abstention share.
I compute the partial effects of visits on own two-candidate vote share (sj|v ) as well as
abstention (snv ) from the model and use the coefficients from the regression as estimates
for the parameters.
∂sj|v
= γsj|v (1 − sj|v )
∂visitsj
(3)
∂snv
= −γsj snv
∂visitsj
(4)
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4
Data
Data on candidate visits comes from The Washington Post website.1 Citizen voting
age population is taken from the 2008 American Community Survey 1-year estimates.
Other demographics are taken from the 2000 census. Election returns and the number
of delegates per district come from Dave Leip’s Atlas of U.S. Presidential Elections. The
National Climatic Data Center provides data on rain, snow and temperature.
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Results
Table 2 reports the results from the first stage regression predicting visits. Obama’s
campaign focused on districts with a larger and odd number of delegates as evidenced
by the positive coefficients on the delegates and interaction of the odd and delegates
variables. The negative sign on odd is at first troubling. The number of delegates must
be multiplied by the coefficient on the interaction of delegates with odd if there are an
odd number of delegates. When calculating this non-linear function we see that Obama
targeted districts with a larger number of delegates and within those he focused on the
odd ones. Both candidates make more visits when there is more time to do so, but
Clinton’s strategy appears to rely on making as many visits as possible whereas Obama
made fewer visits and targeted them to specific districts. The F-test on the instruments
in the first stage is 15.37.
Table 3 shows the coefficients from estimating equation (2), using OLS and 2SLS for
the visits variable. This coefficient is not directly interpretable, so using equation (3)
and equation (4) I report the partial effects averaged across districts. One visit increases
own two-party vote share by roughly 2.4 percentage points and decreases abstention
1
The website was accessed throughout the primary cycle. http://projects.washingtonpost.com/2008presidential-candidates/tracker/dates/
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Table 2: First Stage Results
Visits
Clinton
0.20
(0.149)
-0.886
(0.571)
0.125
(0.125)
1.450***
(0.264)
Delegates
Odd
Odd * delegates
Days per district
R2
N
Obama
0.121
(0.08)
-0.770**
(0.325)
0.129*
(0.071)
0.668***
(0.087)
0.431
776
* p<0.10, ** p<0.05, *** p<0.01. Standard errors clustered at the state level in parentheses. The
regression also includes controls for market size, population density, whether the state has a caucus
or primary, whether the election is open or closed, the percent black, proportion over age sixty-five,
proportion with bachelor’s degree, median income, the unemployment rate and the amount of snow and
rain compared to the week before.
share by 0.7 percentage points. The 2SLS result is roughly 40 percent larger than the
OLS estimate, which is still large compared to the literature.
Table 3: Results
Visits
R2
N
Effect of Visits on Vote Share Relative to Non-Vote Share
OLS
2SLS
0.075***
0.103***
(0.010)
(0.027)
0.809
0.807
776
776
Own two-candidate share
Abstention share
Partial Effects of Visits
OLS
0.017
(0.0016)
-0.005
(0.0025)
2SLS
0.024
(0.0023)
-0.007
(0.0035)
* p<0.10, ** p<0.05, *** p<0.01. Standard errors clustered at the state level in parentheses. All
additional covariates from the first stage are included.
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Conclusion
Many papers find negligible effects of campaigning which seems to be at odds with candidates raising vast sums of money. This paper finds a relatively larger effects by focusing
on the presidential primary setting and addressing measurement error and endogeneity.
While this result may not be generalizable to other election settings, studying campaigning in the primary setting is interesting in itself because primaries are high stakes events
in that the eventual nominee for the general election is chosen there.
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Acknowledgments
Support from the Leitner Program in International and Comparative Political Economy is gratefully acknowledged. All remaining errors are my own. I thank Ebonya
Washington, Brian Knight, Steve Berry, Achyuta Adhvaryu, Christopher Conlon, Juan
Eberhard, Alan Gerber, Dora Gicheva, Don Green, Justine Hastings, Fabian Lange and
seminar participants at Yale and Brown for helpful comments.
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