Theory and Practice
In Testing Models of the Iowa Caucus
Presented at the Midwest Political Science Association
Section 12: Elections and Voting Behavior
Panel 2: The Presidential Nominating Process
By Christopher Clayton Hull
Georgetown University Department of Government
April 15, 2004
Theory and Practice
In Testing Models of the Iowa Caucus
By Christopher Clayton Hull, Georgetown University
April 15, 2004
I. Abstract
In prior research, empirical models were constructed to test what factors influence candidate
success in the Iowa Caucus. Those models indicated that organizational strength, television
advertising, and perceptions of viability mattered most in determining a candidate’s Caucus vote
share.1 Following that research, ten interviews with Iowa political professionals were conducted
to add qualitative nuance to the models’ findings. This paper returns to the models with the
addition of new data from the 2004 Iowa Caucus to test those nuances, find whether they are
supported by the data, and use the results to predict the 2004 Democratic Caucus results.
The pol-influenced models indicate that the amount of earned media a candidate receives has a
significant impact on Caucus performance; that the Democratic 15% viability threshold leaves an
empirically verifiable mark on Iowa results; and that some significant shift may have occurred in
TV’s importance after publisher Steve Forbes’ 1996 television blitz. Also, a preliminary new
model based on the interviews’ results successfully predicted the rank order of the first four
finishers in the 2004 Democratic Caucus, a hotly contested and serpentine contest.
These results lend credence to the notion that working with both theory and practice – building
upon the literature, then running the results by political professionals – can yield important and
useful insights in predicting and explaining electoral results.
II. Introduction
The first-in-the-nation Iowa Presidential Caucuses are not an open book. Unlike primaries,
caucuses do not “rhyme” with well-studied general elections, and the factors that influence them
are therefore more difficult to spell out. Doing so is important, however, because only
candidates faring well in the Caucuses have gone on to be president for a generation, and
candidates faring poorly in Iowa have often found their campaigns ended shortly thereafter – or
even beforehand. With heavy criticism leveled at the Caucus as unrepresentative
demographically, ideologically and geographically, it becomes especially crucial to grasp the
mechanisms that drive success or failure in the contest.
Accordingly, this paper attempts to better both explain and predict success in the caucus, with
the help of Iowa political professionals who make a living doing both, as well as with an infusion
of new data including survey research from the 2004 Democratic Caucus.
1
C. Clayton Hull, “Explaining Vote Share in the Iowa Caucus,” American Political Science Association conference
paper, forthcoming August 2004.
2
This paper’s research was conducted in three phases. First, before data were available on the
2004 caucus, a set of models were built to estimate what leads to candidate success. Second,
interviews were conducted with political professionals to get insight into those models. Third,
those insights were put to the test, first with the original pre-2004 data, then with an expanded
and improved dataset including 2004 information.
In the original, pre-2004 research, based on a review of important Caucus literature, empirical
models were constructed to blueprint the mechanisms for Caucus success from a “campaign’s
eye view,” rather than from the voter’s point of view as past studies had. After all,
understanding which candidates succeed or fail in general seems best achieved by exploring
empirically the characteristics of campaigns that did and did not do well, rather than by relying
on “voter’s eye view” survey results alone.
Those original models yielded interesting results, including that a strong grassroots organization,
investment in television advertising, and viability in the primary as measured by national polling
strength appeared most important in explaining and predicting Caucus outcomes.
To improve those empirical models, interviews were conducted with ten political professionals
with important experiences in Iowa to draw upon. The pols were asked about the various factors
included in the original models, and probed on subtleties that might have been missing from the
original analysis of the Caucus.
Apart from the factors already in the original models, which interviewees largely accepted as
given, the interviews generated a number of practical questions to explore, including:
1. Whether there are important differences between the two parties in what determines
Caucus success, including in early straw polls, the importance of organization, and
the Caucus structure itself
2. Whether there was a “Paradigm Change” in TV advertising due to Forbes’ blitz in
1996
3. Whether a candidate’s viability in the primary disproportionately enhances or
diminishes other factors such as organization, fundraising, Iowa spending, or time
spent in Iowa, including whether being a “frontrunner” – or trailing one – matters
disproportionately in the Caucus
4. Whether measuring the ideological space a candidate occupies relative to his or her
supporters better measures the role message plays in helping determine vote share
5. What role earned media plays in helping or hurting candidate success
6. Whether a candidate’s home state geography plays a role, as “favorite son”
candidates have a checkered history in Iowa
7. Whether perceived chances of success in Iowa (Iowa polling strength) has an
independent impact separate from national viability, or whether Iowa polls are only
helpful for prediction
8. Whether minority candidates fare disproportionately worse in the Caucus, once
adequately controlled for both viability and ideology
The results of these tests are presented below. The database used is described; the original
results before discussions with the political professionals are presented; a new Base Model is
proposed built on the original models but using new data; using that model, the input from the
3
professionals is put to the test empirically; and a revised, preliminary predictive model is used to
estimate the 2004 Iowa Caucus results.
III. Data
This study’s models are built on an original pooled cross-sectional and time-series database of
candidate level of effort in Iowa, national performance on various fronts, in-party activist voter
surveys, and of course performance in the Iowa Presidential Precinct Caucuses, all from 19762004.2
The information in that database was pulled from a variety of sources. The Federal Election
Commission was the source of the information on national fundraising.3 Much candidate “level
of effort” information (number of days spent in Iowa, Iowa spending overall, television
spending) was drawn mainly from Winebrenner’s landmark study of the Caucus, as well as from
other important secondary sources on the subject.4
Much of the older voter-level data (on ideology, viability perceptions, contact by organization,
rally attendance, etc.) is a composite for each campaign taken from Abramowitz, Stone and
Rapoport’s voluminous time-series and panel survey data of party caucus-goers and activists.5
Finally, the newest of the voter-level data (1996-2004) comes from the author’s survey of
politically active Iowa partisans conducted prior to the 2004 Iowa Caucus itself.
IV. Original Models and Empirical Findings
The original literature-based research tested a number of basic models of Caucus performance,
using the original 1976-2000 database (which had significant drawbacks ameliorated in the
models in this paper).6 The first literature-based (“Simple Variables”) model is shown in Table
1, and its results are included in Appendix 1. Note that the model has a number of deficiencies.
2
Note that 1976 data is so sparse that models run without interpolation always include at least one variable with no
measurement in that year, so that models must be interpolated to draw upon it. Likewise, the pre-interpolation 2000
and 2004 data has enough gaps to eliminate some alternative model constructions. In addition, note that 1992 was a
relatively uncontested caucus, with an incumbent Republican president, with Buchanan focusing his primary
challenge on New Hampshire, and with “favorite son” Sen. Tom Harkin, D-Iowa running on the Democratic side.
As a result, no regression model in this paper has enough information to run in that year either. Thus while the new
database has information from 1976-2004, in effect a pre-interpolation model would largely stretch from 1980-1988,
then 1996-2000, depending on which regression is run. As a result, the Base Model run in the paper employ
interpolated data, with clear references as to how the new information was estimated for each variable. Therefore
the Base Model and all tests performed with it use a data set the stretches from 1976 to 2004.
3
All of the FEC information was drawn from the website, www.fec.gov, and from on-site reviews of e-reports and
actual filings by research assistant Irina Andre Papkov.
4
Hugh Winebrenner, The Iowa Caucuses: The Making of a Media Event (Ames, Iowa State University Press, 2nd
Ed., 1998). See also Peverill Squire, ed., The Iowa Caucuses and the Presidential Nominating Process, 121-148.
Boulder, Colo.: Westview Press; and Mayer, William G. ed., In Pursuit of the White House 2000 – How We Choose
Our Presidential Nominees. New York: Chatham House.
5
Alan I. Abramowitz, John McGlennon, Ronald B. Rapoport, and Walter J. Stone, “Activists in the United States
Presidential Nomination Process, 1980-1996,” Inter-university Consortium for Political and Social Research 6143,
Second ICPSR Version, July 2001.
6
See note 2 above.
4
Mainly, it fails to account for relative effort. After all, candidates are boosted not just by
spending on television or building a large organization, but by spending more and building
bigger than other campaigns that cycle. In addition, three factors which we believe to be
importance based on the literature or basic theory do not appear statistically significant, namely
the perceived ideology of the candidate, the amount of time a candidate spent in Iowa, and
especially the number of candidates in the field.
TABLE 1: Simple Variables Model
•
•
•
•
•
•
•
•
•
Number of Candidates in the Field.
Number of Days Spent in Iowa.7
Log of Iowa Spending in real (2002) dollars.
Log of TV Advertising Spending in real (2002) dollars.8
National Polling Performance, as measured by % support the candidate obtained in a national poll soon
before the Iowa Caucus.
Organizational Strength, as measured by the percentage of votes received by the candidate in the Ames
Straw Poll (for Republicans) or Jefferson-Jackson dinner straw poll (for Democrats) the summer before the
Caucus.
Log of National Fundraising in real (2002) dollars.9
Aggregate Mean Ideology Score for each candidate, in interaction with the Democratic dummy variable, to
control for the “moderation hypothesis,” that caucus-goers strategically look to the more moderate
candidates for viability reasons, in spite of their ideological true feelings.
Dummy Variables for 1980, 1984, 1996, and 2000, to control for one-way fixed effects for time, as well as
to account for measurement errors in 1980 and 2000 data.
For ideology, the Simple Variables Model may have been measuring what is important and is
explored extensively in this paper. For days in Iowa, the estimation of the model showed a
negative impact. However, we may be simply observing an interesting empirical result:
spending more time in Iowa does not necessarily translate into success. That subject, also, was
explored further in interviews.
Based on the Simple Variable Model’s results, a second “Relative Variables” model was
constructed to measure levels of effort compared to other candidates that cycle. It also pared
away variables with less significance and sharpened other measures. The Relative Variable
model is presented in Table 2 below, and its results are likewise contained in Appendix 1.
7
Note that there is a measurement error in this data: its 2000 values are systematically different, measured only
through election day 1998, about a year before Caucus day. Using a 2000 Dummy Variable would help ameliorate
the problem with it. However, the variables on Model 1 do not actually have enough data to support a 2000 case, so
the problem is moot.
8
Note that there is a measurement error in my data for this variable: 2000 ad spending is measured by total dollars,
while the rest is measured by spending on the two of the largest stations in Iowa, KCCI and WHO. To address that
concern, I included a Dummy variable for the 2000 caucus when using raw TV Advertising data (or its log). I did
not feel it was necessary to use that “patch” on the data when using the “% difference from the average” form of the
TV spending variable. The reason was that we might theorize that in that form, total spending and spending on two
major stations in Iowa should be roughly equivalent – and in fact, I found that the 2000 Dummy had virtually no
explanatory power in models using TV spending in the % difference form.
9
This variable, too, has a measurement error: for 2000, expenditures are through July 31, 2000; for 1980, it is
through March 31, 1979, and for the rest, it is through April of the Election Year. A 1980 and 2000 dummy variable
were used each time National Fundraising was included in a model in raw form, in an attempt to mitigate its
measurement error.
5
TABLE 2: Relative Variables Model
•
•
•
•
•
•
•
Number of Candidates in the Field. Same as Simple Variables Model.
Relative TV Advertising Spending in constant dollars, in % difference from average form, isolating the
explanatory power of the mean ad spending by competitors.
National Polling Performance. Same as Simple Variables Model.
Overall Organizational Strength, an index taking into account the scaled performance in an early straw poll
(that is, scaling results to the number of candidates making it to the Caucus as opposed to the number of
candidates in the field at the time of the early Straw Poll), as well as the % of supporters saying they were
contacted by the campaign’s organization, and %s of supporters who personally 1) attended a rally or
meeting, 2) did canvassing, and 3) tried to convince friends to support the candidate, as well as other
controls.
Relative Iowa Spending in real (2002) dollars, again as a % difference from average spending of the
candidates in that cycle of that party.
Aggregate Mean Ideology Score. Same as Simple Variables Model.
Dummy Variables for 1980, 1984, 1996, and 2000. Same as Simple Variables Model.
The results yielded not only a more significant organization variable, but more explanatory
power per variable as measured by the higher adjusted R-squared statistic, and a more significant
model as measured by the higher F-statistic.
When the model was estimated, number of candidates in the field was not significant at the .05
level, but it seems to have some explanatory power. Relative Iowa spending remained
statistically insignificant, an interesting result that was explored further in the interviews. The
Mean Ideology score, this time only employed for the Democrats so that it could be observed
operating in a single ideological direction, is still insignificant, but the crudeness of the measure
demanded further inquiry, especially after interviewees stressed the impact of message on the
Caucus outcome.
A final model was created in order to predict, as opposed to explain, the results of the Caucus.
Though a good model will do both, the goal of the Predictive Model was to include only the
most salient variables, to give prognosticators a tool without requiring extensive (and expensive)
spadework. Also, the Predictive Model (see Table 3 below) was intended to resort to the original
data with no indexing or interpolation, to examine the raw Caucus data and verify that those
results matched those of the first two models. Obviously, its estimation against the base data for
the first two models was not a full-fledged challenge of its powers, but it was at least a partial
verification that indexing had not created false relationships.
TABLE 3: Predictive Model
•
•
•
•
Number of Candidates in the Field. Same as Model 2.
Relative TV Advertising Spending. Same as Model 2.
National Polling Performance. Same as Model 2.
Scaled Early Organizational Strength. Same as Model 2.
The results of the original Predictive Model are included in Appendix 1. It excludes no
statistically significant variable from the Relative Variables Model, but it is still not at all clear
that it is correctly specified. However, if its goal is to predict and not to estimate individual
effects, the crucial question is the power of the overall model, power which it certainly retained.
6
Note that the dummy variables for time are not included, meaning the Predictive Model is a
restricted model, rather than a one-way fixed effects model.10 Also, note that this model
excludes Relative Iowa Spending in real (2002) dollars as a % difference from average spending
of the candidates in that cycle of that party, based on the previous model’s indication that what
matters to predicting a candidate’s success is really organization and television advertising, and
that other spending makes virtually no difference in the performance of a campaign, statistically
speaking at least.
V. The New Base Model
For this paper, the original explanatory models were modified to serve as a base Explanatory
Model from which to explore the issues raised by the interviews. The Base Model incorporates a
great deal of new data, including extensive new survey data from 1996-2004. It also adopts a
different approach to relative factors: instead of relying on the “percentage difference from the
mean score” for each candidate as the Relative Variables Model did for TV and Iowa spending,
the Base Model adds new “percentage of the total” variables. Percentage of the total (spending,
amount of media, etc.) has a more linear relationship with Caucus vote share, which is to be
expected since vote share is itself a percentage of a total.
The Base Model adds back in as controls National Fundraising and Days in Iowa from the
Simple Variables Model, though it contains the former in the “percentage of the total” form.11
The estimation of the new Base Explanatory Model including its results is presented in Table 3.12
With the new data from 1996-2004 included, and with the modifications to make the explanatory
variables more compatible with the dependent variable, the model’s overall power is similar, but
different factors rise to the fore. While national in-party prominence remains among the single
most important determinants of Caucus success, the importance of a candidate spending time in
Iowa is more stark with the new data. Television spending’s importance drops dramatically, a
result that remains regardless of which mathematical form one puts the variable in. With all the
10
Since in the original pre-2004 data, two variables with significant measurement errors in those years – Days in
Iowa and National Fundraising – are not included in the model, and two others with less significant concerns, TV
spending and Iowa spending, are included in % difference form, the 1980 and 2000 dummy variables were not
necessary from a data management point of view.
11
For the test regression run without interpolation, percentage of the total by its nature is skewed by gaps in the data.
Accordingly, for Iowa Spending, which is simply not reported by many candidates to the FEC, it was necessary to
scale the “percent of total” variable. This was done by assuming other candidates would spend money at the
average rate of those for whom data was available. Mathematically, the total of the observed Iowa Spending data
was estimated by:
EST_TOTAL = X + (X - (X * P / T)
Where
X was the total amount of the observed cases
P was the total number of cases for which there was data in that cycle for that party
T was the total number of cases in that cycle for that party.
12
Note that interpolation is used in order to estimate the model fully, as in the original models. The model was
estimated using unfilled data with two important results. First, none of the factors proved statistically significant
individually, since the N was only 27. Second, Television Spending could not be included in the model because so
little data were available for the cases available for the other variables (with it included, the N of the model dropped
to 13). Fuller results of the Base Model test regression without interpolation can be found in Appendix 1.
7
controls built back into the model except national fundraising,13 there is a high level of
multicollinearity, which may cloud the role of some variables.
TABLE 3: Base Explanatory Model
(Constant)
DAYS IN IOWA the candidate spent in the two years
prior to the election14
NATIONAL POLLING DATA
IOWA ORGANIZATIONAL STRENGTH Index15
IDEOLOGICAL PROXIMITY, the % of partisans
reporting 0 distance from candidate on a 7-point scale 16
% OF FIELD that candidate represents
% OF TOTAL IOWA SPENDING among same-party
candidates that cycle, summing Pre-Election Year 3rd
and 4th quarter17
% TV SPENDING TOTAL the candidate’s TV
spending represented among candidates of the same
party that cycle18
1976, 1980, 1984, 1996, 2000 and 2004 Dummy
Variables to control for fixed effects in time
Unstandardized
Beta
-0.163
pvalue
0.036
0.001
0.265
0.518
0.017
0.021
0.039
0.288
0.195
0.16
0.384
0.067
0.697
-0.039
0.725
None
sig.
Various
a Dependent Variable: CAUCUS VOTE SHARE, %
R2: .803
Adjusted R2: .745
F-Statistic: 13.797
Sig.: <.001
Using this Base Model, we can go back and explore the input the political professionals
provided.
Do the questions they raise bear out empirically?
13
National Fundraising Data is used for all the interpolations and is highly multicollinear with the rest of the factors.
However, a Base Model estimated with National Fundraising demonstrating similar results is included in Appendix
1.
14
Filled using predictions from a model of %field, %fundraising, %Iowa spending, national polling, pre-caucus
straw poll results, ideological proximity, time in 4 year increments, caucus skipper dummy variable, and years
candidate’s party had spent out of power
15
Filled using predictions from a model using pre-caucus straw poll % filled, % of supporters surveyed reporting
attending a rally, and % of supporters reporting they were contacted by the candidate's organization
16
Filled using the rest of the Base Model.
17
Filled using national fundraising, % of the field the candidate represents, and national polling data
18
Filled using %field, %$raised, national polling data, real receipts, Iowa spending (real, filled as above), % total
Days in Iowa (filled as above), as well as dummy variables for Steve Forbes’ 1996 and 2000 campaigns and whether
the candidate is a Democrat.
8
VI. Testing the Professionals
This section will explore eight qualitative questions raised in the interviews with Iowa political
professionals. In each case, the real-world description painted by the pols is laid out briefly,
following by an examination of that description using the Base Model and the new 1976-2004
data.
Qualitative Question 1: Are there important differences between the two parties in what
determines Caucus success?
The interviews highlighted differences between the Republican and Democratic Caucus
processes that might lead to important empirical findings about what leads to success for
candidates from each of the two parties. Three of those differences are discussed and explored
below: whether early straw polls are different for GOP and Democratic candidates, whether the
Caucus itself is different with respect to organizational demands, and whether the Democratic
viability threshold hurts lower-tier candidates disproportionately.
Potential Party Difference: Are Republican and Democratic Early Straw Polls Different?
First, the question was raised whether early straw polls are as accurate a gauge of Democratic
performance as Republican performance. For example, the Republican Ames Straw Poll, in
Quayle 2000 Iowa Campaign Manager Keith Fortmann’s view, is different than the Iowa Caucus
in important ways, being heavily predicated on the logistics of putting together the details of
getting a significant number of people to a single location in the state.19 By contrast, the
Democratic Jefferson-Jackson Day dinner’s straw poll is not as organizationally intense. As a
result, it may be that the Democratic pre-caucus straw poll contest does not proxy organizational
strength as well as its Republican equivalent.
To test that premise, we can first simply split Early Straw Poll Results (the organizational
strength variable in the Simple Variables Model) into a Republican and a Democratic variable,
using interaction terms with party dummy variables. If the result is that Republican Early Straw
Poll Performance has a great deal of explanatory power and Democratic Straw Poll Performance
does not controlling for the other factors in the model, it will indicate that the differences
between the logistics-heavy Ames Straw Poll and the more casual Jefferson-Jackson Day Dinner
straw poll affect the combined variable’s performance in a systematically biased way.
In fact, this is not what we find. Building Republican Early Straw Poll Performance and
Democratic Early Straw Poll Performance into the original Relative Variables Model instead of
Organizational Strength alone, both remain statistically significant at p=.013 and p=.005,
respectively. As far as the data goes, we cannot get an additional hint as to the outcome of the
caucus from Republican as opposed to Democratic results, though the Ames Straw Poll looms
far larger on the GOP horizon.
That said, it was clearly of concern to operationalize organizational strength simply as precaucus straw poll performance, as it was in the original Simple Variables Model. As a result, the
19
Interview with Keith Fortmann, April 19, 2003.
9
Relative Variables Model attempted to account for alternative measures of organizational
strength by creating an index combining early straw poll performance with the:
• Percentage of a candidate’s supporters saying they were contacted by the campaign’s
organization, and the
• Percentage of supporters who personally attended a rally or meeting20
The results of the second, Relative Variables model indicated both that using a broader measure
of organization does in fact help explain more of the variation, as well as that more theoretically
precise variables yield a benefit to the model. Thus the Base Model includes a revised version of
the Organizational Strength index rather than just pre-caucus straw poll results.
Does the Base Model’s operationalization of organization help further address the concerns
Fortmann raises? To answer, let us again split the organization variable down the middle, into a
Republican Organizational Index and a Democratic Organizational Index. Again, if we find that
the Republican variable is significant and the Democratic one is not, we will still have a measure
that appears not to capture the explanatory power of Democratic organization.
Plugging these two new variables into the Base Model in place of the single organizational
index, however, once more yields two statistically significant variables. The Republican
Organizational Index is significant at p=.047, and the Democratic Organizational Index is
significant at p=.040. Again, the Democratic variable appears to map slightly better onto
Caucus performance, though the Republican one obviously describes significant variation in
Caucus outcomes as a result of organizational strength.
We find, therefore, that there is no statistically significant challenges arising from the GOP Ames
Straw Poll’s systematic differences from the Democratic Jefferson-Jackson Day Dinner straw
poll.
Potential Party Difference: Are Republican and Democratic Caucuses Themselves
Organizationally Different, Too?
That brings us to a second part of the question: Whether the Democratic Caucus itself actually
requires heavier organization than the Republican Caucus. Prominent Democratic campaign
operative Jeff Link insists that because of the difference in structure and procedure complexity,
caucus organizational strength is more important to Democratic candidates than to Republicans
in Iowa: “On the Republican side it’s a head count;” he argues, “on the Democratic side it
requires training.” 21
On the Democratic side, Link explains, the mathematics center on the viability threshold of 15%.
That is, in order to get any delegates, a candidate must receive at least 15% of the attendees at a
given precinct caucus, but above that level the candidate begins being awarded delegates as
20
Both measures came originally from the Abramowitz, Rapoport and Stone primary survey data of partisan
activists, and was supplemented with more Iowa-specific data through 2004 in this paper. See Alan I. Abramowitz,
John McGlennon, Ronald B. Rapoport, and Walter J. Stone, “Activists in the United States Presidential Nomination
Process, 1980-1996,” Inter-university Consortium for Political and Social Research 6143, Second ICPSR Version,
July 2001.
21
Interview with Jeff Link, June 10, 2003.
10
proportionally as possible to support. Thus in a (two-way) Gore vs. Bradley Caucus, in any
precinct where Bradley passes 15%, he gets at least one delegate. If there are five delegates in
that precinct, that is not so disproportionate – if Bradley got only 15% of the vote he would get
20% of the delegates (one). If, however, there are only two delegates in a precinct, which Link
says is the average, then Bradley’s same 15% would net him 50% of the delegates (again, one).22
That complex electoral math creates the enormous incentive Eric Bakker, the top aide to the
Iowa Senate Democratic Leader, describes for Democratic candidates to calculate their exact
support even in areas where they are overwhelmingly popular or unpopular. It is more complex
a process than that, too. “Republicans just count numbers” in the Caucus, says Bakker.
Candidates “have to be viable” in Democratic caucuses. Also, precincts are weighted by
Democratic performance, Bakker points out, so a precinct’s votes can be curtailed based on
“who’s done a poor job of organizing in the past.” Also, Bakker stresses that the Democratic
Caucus’ structure makes it important to organize even in places with very low Democratic
penetration, such as Sioux County in northwest Iowa, a heavily Dutch Republican Mecca.
Democrats have an incentive to build organization all across the state. That makes certain that
the contest on that side of the aisle is not a “popularity contest.” 23
Thus on the Democratic side, organization is of premier importance in the Caucus. “That’s who
wins: the one that’s organized across the state,” says Bakker.24
And the only way to calculate such support precinct-by-precinct, echoes Link, is to have a
muscular statewide organization pinpointing the campaign’s “hard count” of supporters, precinct
by precinct. Unless you have names on a list, Link believes, you cannot perform this allimportant targeting.25
Does this difference between the two caucuses create a differential in the importance between
Republican and Democratic organizations? We can address this question as well using the new
Democratic and Republican Organization Index variables. If we find that the Democratic index
has a steeper slope, that is, if its beta value is larger, it will indicate that systematically, Link is
correct and additional organizational effort on the part of Democratic candidates yields
disproportionately more benefits than such efforts on the Republican side. More rigorously, if
we control for the overall impact of Organizational Strength, then test to see whether the
Democratic Organizational index remains significant, we will have some justification for saying
that organization matters disproportionately on the Democratic side.
As hinted at above, we do in fact find a small difference in the importance of Republican and
Democratic organization. The model’s estimate of the beta value for the Democratic
Organizational Index is .547, and for the Republican Organizational Index, .505. More
convincingly, the standardized betas, to be certain we are not merely observing differing
measures, are .442 and .390, respectively. This is not a statistically significant difference,
however. With a standard error of .258 and .248, respectively, the two betas fall well within the
margins of error for each other.
22
Interview with Jeff Link, June 10, 2003.
Interview with Eric Bakker, May 13, 2003.
24
Ibid.
25
Ibid.
23
11
That indicates to us that there may be a difference in importance between Republican and
Democratic organizations, and in the theorized direction, but we are not yet able to say with
confidence that the difference exists.
Examining the Democratic Organization interaction term when controlling for Organization
more generally, we again find that no statistically significant difference exists. Building both
into the Base Model, the result is that Organizational Strength remains significant at p=.047,
while the Democratic interact is not, at p=.716.
Thus all we can conclude is that a grassroots organization matters to both parties’ presidential
candidates in winning the Caucus.
Potential Party Difference: Does the Democratic Viability Threshold Itself Make a Significant
Difference?
Regardless of the difference in organization’s importance, does the 15% Democratic viability
threshold matter in Caucus performance? We would expect that a candidate going into the
Caucus with under 15% support on average, for instance, would face a possibly crippling
challenge, as in precinct after precinct he was eliminated from consideration entirely. On the flip
side, Democratic candidates with over 15% average support would benefit from the elimination
of the rabble beneath them, and fare disproportionately better. Since neither is the case for
Republican candidates, we should test to determine if Caucus math significantly impedes lowertier Democrats.
One way to do so is to include the Des Moines Register’s Iowa Poll results in the Model. First,
we can create a dummy variable to measure the average impact of being a candidate expected to
draw an average of less than 15% in a Caucus. Second, we can test an interaction term between
that dummy variable and the polling itself, to see if there is a significant slope differential
between those doing well and those doing poorly with respect to polling data as a predictive
variable. We must ultimately include the Iowa polling results overall as well as a control for
both. (Note that the role of Iowa Poll data as an explanatory and predictive variable is explored
in more detail below.)
The results of adding a factor for those polling under 15% without controlling for Iowa Polling
data unsurprisingly add explanatory value to the Base Model. The R2 for the Base Model edges
up to .814, the adjusted R2 increases to .754, and the F statistic rises to 13.449 (with the Model
still significant at <.001). However, the dummy variable itself is not significant at p=.117.
Moreover, controlling for Iowa Polling, which we must to get an accurate picture of whether
those polling less than 15% on Caucus do disproportionately worse, the Under Democratic
Viability Threshold dummy variable’s explanatory power is virtually nil (p=.970). An
interaction term between Iowa Polling and the under 15% threshold dummy does about the same
(p=.968), indicating that the slope for those under 15% is also not significantly different. And a
factor testing whether organization matters more for those under 15% - an interaction between
the threshold dummy and the Organizational Index - only claws its way to p=.503.
A second way to approach the viability threshold issue is to use the Base Model to estimate the
Caucus performance of the candidates, and rely on that data to predict who might fall below that
12
Democratic benchmark. That offers more promise for two reasons. First, it would rely on the
rest of the model to control for external factors, as it is drawn from the Base Model directly, not
external polling data. Second, it is likely to be a closer approximation than the Iowa Poll as to
who actually performs well.
The result is only slightly more promising. The dummy variable for those Democrats predicted
by the base model to fall under 15% of performance statewide does not rise to significance, but
provides new information to the model and achieves a p value of .247, insignificant statistically
but better than the Iowa Polling equivalent. The interact between Democrats under 15% and
Organization Strength index fares marginally better, at p=.231.
At this stage, we can conclude only that at least given this Base Model, there is no statistically
significant differential in performance for Democrats for those faring worse than 15%. That
said, as we will see in the discussion of earned media, it may be wise to continue to test for the
difference in importance of Organization for Democrats staring a poor finish in the face.
Qualitative Question 2: Was there was a “Paradigm Change” in TV advertising due to Forbes’
blitz in 1996?
The interviews raised a question posed by the literature as well: Did Steve Forbes’
unprecedented 1996 TV blitz cause a permanent shift in the importance of candidate television
advertising in the Caucus? Robert Haus, the head of the Iowa 1996 Gramm organization and a
senior advisor to Forbes in 2000, believes so. “The paradigm has shifted since 1996, and
dramatically in 2000,” he says.26 Beginning in 1988, argues Republican strategist Ed Rollins, a
new emphasis on paid media began to alter the nature of the campaign throughout the country,
and if “the use of paid radio and television increased in Iowa as it had nationally,” Winebrenner
(98) says, “the retail nature of the Iowa campaign might be lost and with it the rationale for
beginning the presidential campaign in a small state.”27
In 1996, Forbes’ infusion of advertising seemed to alter the fundamentally organizational
dynamic of the Iowa campaign. Republicans spent about ten times as much on television
advertising in 1996 as they did in 1988, and “pundits wondered whether it was possible to ‘buy’
the caucuses.”28 Of course, it turned out that Forbes finished a dismal fourth, notwithstanding
his powerful poll ratings on the eve of the contest. “Rather,” Winebrenner argues, “the campaign
was a hybrid of ‘retail’ and ‘wholesale’ politics.”29
The interviews reflected others who like Haus saw an “inflection point” in the importance of
organization and television advertising in 1996. Television counts in Iowa, says Tim Hyde,
Executive Director of the Republican Party of Iowa in 1980 and an advisor to Pete DuPont in
1988, though it “didn’t used to. Time was it turned people off because they thought it was
spending way too much money.” 30
26
Interview with Bob Haus, April 15, 2003.
Hugh Winebrenner, The Iowa Caucuses: The Making of a Media Event (Ames, Iowa State University Press, 2nd
Ed., 1998), p. 98. (Or 177-78 – check.)
28
Ibid., p. 227.
29
Ibid., p. 243.
30
Interview with Tim Hyde, April 16, 2003.
27
13
Haus, the head of the Iowa 1996 Gramm organization, who was also a senior advisor to Forbes
in 2000, argues that in 1996 and 2000, the way that Iowa campaigns are run has changed
fundamentally. “The paradigm has shifted since 1996, and dramatically in 2000,” says Haus. It
was Forbes that changed the way the campaigns were run, in his view. Instead of carefully
building a state-wide organization over the course of a year or more, Forbes launched into the
race in 1996 with a heavy 1000 gross rating points of television advertising per week “tearing
Dole’s head off.” 31
Forbes’ self-financing affected the mix of the ’96 contest, he says – “‘polluted’ would be too
strong a word.” From his vantage point as Gramm’s campaign manager, he remembers watching
the cost climb dramatically when Forbes entered the race in October of 1995. The Gramm
campaign plans were already in place for mail, phone calls, and personal visits based on an
understanding of how much television would cost. Suddenly they had to shift resources to
television advertising at the last minute as the costs soared in response to Forbes’ flooding the
airwaves. That shift was painful and inefficient for the other campaigns to undertake. 32
Forbes’ 1996 bombastic entry into the race “took the shroud of secrecy” off the Caucus, and
“opened it up.” Iowa became less “land-locked” politically, Haus says, less insular and
inaccessible. The “Forbes factor” made the overall Caucus feel more “primaryesque,” Haus said.
Yet, following Winebrenner, Haus acknowledges that in 2000 the Forbes campaign was very
much a “redo, with ground organization in all 2034 precincts” he says. 33 After the Forbes ’96
advertising blitz failed to produce a successful result, the “$64,000 question” was how to meld
advertising and communication at the grassroots level together. In 2000, Haus acknowledged, it
was George W. Bush who did that best, projecting a successful public image in the media, with
“motorized ground forces behind him.”
Forbes 2000 senior aide John Stineman agrees that learning from falling short in 1996, in 2000
Forbes made a strategic decision to feed the organization. Throughout the campaign Forbes
relied on bus tours across the state, going from town to town holding events at which each
attendee got a picture with the candidate. “We had huge events,” Stineman recalls, “going up to
Sioux City and having 600 people show up.” At Forbes’ birthday barbeque in Shell Rock in
Hardin County, for instance, there were 2600 people – which exceeded the population of the
town.34
According to Stineman, the 2000 Forbes Des Moines headquarters was about coordinating the
organization, get out the vote (GOTV), events, and earned media. Both Bush and Forbes relied
more heavily than in past campaigns on the “professional county chair” model, hiring full-time
organizers at the county level instead of recruiting volunteers for those positions. They both “did
that kind of spending,” said Stineman. The focus was on turning people out to events, driving
that turnout with post cards, paid calls, and local field organizations. 35 Thus Forbes appears to
31
Interview with Bob Haus, April 15, 2003.
Ibid.
33
Interview with Bob Haus, April 15, 2003.
34
Interview with John Stineman, April 10, 2003.
35
Ibid.
32
14
have done as Winebrenner speculates, and adjusted his 2000 strategy to the reality of Iowa’s
Caucus process.
Nonetheless, it seems crucial to investigate the potential differential in television’s effectiveness.
The literature-based model controlled for the fixed effects of time, but it should also:
• Separate before and after the 1996 cycle to see if slopes of organizational strength or
television spending change significantly within the model, and
• Estimate the average difference television made in 1996, 2000, and 2004 relative to
other years.
The original research included testing a number of additional factors to measure the potential
impact of Forbes’ 1996 TV blitz. Those tests included adding to the Relative Variables model:
•
An interactive term between the 1996 Dummy and Relative TV Advertising Spending in
constant dollars. Though the variable was statistically significant at the .10 level, it was
negative – meaning that controlling for the other variables in the model (including TV
spending itself), on average additional TV spending in 1996 had a negative effect on a
candidate’s vote performance. (Upon re-estimating it with the new Base Model,
however, the factor, though still negative, was not statistically significant (p=.447).)
•
An interactive term between a combined 1996 and 2000 Dummy and Relative TV
Advertising Spending in constant dollars. That factor had much less relevance than the
1996/TV Interact.
•
An interactive term between a 1988, 1996 and 2000 Dummy and Relative TV
Advertising Spending in constant dollars, which was included to measure Ed Rollins’
assertion that as TV increased in use across the country, Iowa would follow. The factor
was not statistically significant, implying that TV, while important, was not more
important in that period than it was in early Caucuses.
•
Beyond those listed above, an interactive term between each year’s Dummy and TV
spending, to test whether there are any fixed effects for time and organization, that is,
years in which TV made an extra difference. None of the terms was statistically
significant, though again some had apparent explanatory power.
•
An interactive term between each year’s Dummy and Scaled Organizational Strength
measured by success in an early state-wide straw poll, to test whether there are any fixed
effects for time and organization, that is, years in which organization made an extra
difference. None of the terms was statistically significant, though some had apparent
explanatory power. 36
These tests found no indication that Steve Forbes’ television spending in 1996 somehow changed
the importance of television spending in the caucuses. In fact, Forbes’ own television in 1996
36
C. Clayton Hull, “Explaining Vote Share in the Iowa Caucus,” American Political Science Association conference
paper, forthcoming August 2004.
15
seems to have had a negative impact on him. And 2000, in which Forbes himself competed,
seems to be in line with earlier years in the impact television spending had on the process.
However, using the new Base Model and adding in the year 2004 to this analysis produces a
fascinating effect: both the interactive factor and especially relative TV spending itself get far
closer to being statistically significant. That is, the addition of a pre-1996 relative TV spending
interactive term appears to improve the predictive power of relative TV spending. More
important, the signs of their correlation coefficient are opposite over multiple operationalizations
of the variable, with 1996 and later relative spending actually negatively influencing Caucus
performance.
The indication here, though an indication only, is that starting in 1996 those who spent larger
shares of the total TV expenditure fared worse on average, controlling for other factors in the
model.
Though neither of these variables is statistically significant, we have a theoretical reason to leave
a control for a shift in 1996 in place in future constructions of an Iowa Caucus model – as well as
testimony from our political professionals. Perhaps that shift, which they perceived as negative
for the Caucuses more generally, in fact is damaging the candidates who are driving it.37
Qualitative Question 3: Does a candidate’s viability disproportionately enhance or diminish
other factors?
The Iowa political operatives indicated that viability (or lack thereof) tended to boost (or cripple)
a candidate’s other efforts in Iowa. Interviewees alternately referred to the power of a lack of
viability to undercut organization, fundraising, investment in Iowa, and time on task in the state;
to the impact of “frontrunner” status; and to the degree to which a campaign is focused on issues,
rather than electability.
Potential Viability Impacts: Does Viability Affect the Impact of Organization, Fundraising, or
Spending Time and Money in Iowa?
What impact does national prominence have on other factors? For instance, in 2000 the Bush
team had a great deal of money, Forbes senior organizational aide Stineman says, but more
importantly “he had the whole ‘inevitability thing’ going.”38 By contrast, Alexander 2000
National Campaign Manager Brian Kennedy believes at the time of the Ames Straw Poll,
Alexander probably had more quality county chairs than any other candidate, yet received only
7% of the total cast in Ames.39 On the effect of time and money in Iowa, Hyde maintains that
despite DuPont spending “every other dime” (and 92 days) in the Hawkeye State, the campaign
languished because DuPont “wasn’t credible nationally.”40 Finally McCain, without “boots on
the ground” in the Hawkeye State, was doomed to a relatively light level of support, his
prominence notwithstanding, contends Kennedy (who advised his campaign after Alexander’s
37
One question this analysis leaves open is whether Forbes marked an increase use of television, regardless of its
effectiveness. That question requires more extensive raw data than the model currently draws upon, but would be an
interesting subject for further investigation.
38
Interview with John Stineman, April 10, 2003.
39
Interview with Brian Kennedy, April 8, 2003.
40
Interview with Tim Hyde, April 16, 2003.
16
exit).41 These examples all point to interactions between viability and other factors like
fundraising, organization, and days in Iowa.
Prior research found that national polling data constituted an effective proxy for viability. The
models were run adding in the raw percentage of supporters saying the candidate is certain to
win, as well as the relative (percentage difference from the mean) percentage of supporters
saying the candidate is certain to win. Controlling for the effects of national polling data, neither
factor was important, whereas national polling data remained significant, implying both that
national polling data is an effective proxy for direct measures of viability, as well as that it
contains additional explanatory power beyond what they provide.
Therefore, we should be able to employ national polling data in interaction with organization,
fundraising, Iowa spending, and time spent in Iowa, to measure viability’s disproportionate
effects on those factors. The results from the original Relative Variables model are presented
below.
•
Viability and organization did not appear to matter more in interaction in predicting
Caucus performance. An interaction term between national polling data and the
organization index of each campaign appeared to have little explanatory power at all,
with a p-value of .752.
•
Viability and national fundraising also did not appear to have a disproportionate effect
when combined. The interaction term was not significant, with a p-value of .316, and
was also negative, the opposite of what we would theorize.
•
Viability and Iowa spending likewise did not appear interlinked. An interaction term
between the Log of Iowa Spending and National Polling Data had virtually no
explanatory power, with a p-value of .996, and an interaction term between the
Percentage Difference of a candidate’s Iowa Spending from his competitors and National
Polling Data was also not significant, with a p-value of .581.
•
By contrast, Viability and a candidate’s Relative Days in Iowa do seem to have some
connection. The interaction term between National Polling data and the percentage
difference of a candidate’s Days in Iowa from the average of his competitors was
significant at p=.029, in the theorized positive direction, at the same time National
Polling Data itself remained statistically significant at p=.023. What is more, the
explanatory power of the overall model increased with the addition of the factor, with the
adjusted R-square rising from .793 to .835 and the F-statistic rising from 15.318 to
25.629.42 However, testing similar hypotheses with the new Base Model does not yield
the same result, or even a similar one, a result explored extensively below.
41
Interview with Brian Kennedy, April 8, 2003.
Note that the interaction term between Total Days in Iowa (as opposed to the percentage difference from
competitors) and National Polling Data yields a strange result: It was not statistically significant, with a p-value of
.210, yet controlling for this interaction term, National Polling Data itself is no longer remotely significant, with a pvalue of .729.
42
17
These results indicate that viability gives no marginal additional nudge to organization, national
fundraising or Iowa spending over and above their primary impact on Caucus performance –
which is not to say that any of those factors is weak in and of itself.
Viability did seem to have some disproportionate impact on the amount of time a candidate
spends in Iowa. Thus Hyde’s experience that DuPont’s 92 days in the state seemed to have little
impact makes sense considering the candidate was languishing at 10% in the polls behind Vice
President Bush’s crushing 47%. It appears that the amount of national clout a candidate wields
may increase the marginal impact of his or her visits to the Hawkeye State.
Also, note that the significance of a National Polling-Days in Iowa factor also implies that
national clout disproportionately hurts candidates who do not come to Iowa, which might explain
Kennedy’s observation about McCain. Think of the math: McCain, having spent fewer days in
Iowa than his competitors, would have a large but negative percentage difference from average
number of Days in Iowa. Thus his strength in national polls would give him a large but also
negative interaction term between Viability and Relative Days in Iowa.
That said, the new Base Model does not replicate those results. The main reason, we might
speculate, would be that the new model recasts most of its variables in a percent of the total form
that more closely aligns itself with the dependent variable. That may have remedied a flaw in the
last model, considering that there is a functional form concern with the relationship between
Caucus Performance and this Viability-Days in Iowa interaction term.
.7
.6
.5
1
.4
62
Caucus Votes, Percent
.3
.2
.1
0.0
-.1
Rsq = 0.0583
-.8
-.6
-.4
-.2
0.0
.2
.4
.6
Ntl Poll & %dif Days in IA Interaction Term
Figure 1: Caucus Votes and Viability-Days in Iowa Interaction term comparison
18
Comparing them visually, it becomes clear that a few outliers such as Bush 2000 (Case 1 in
chart) and Kennedy 1980 (Case 62) dramatically effect the slope of the relationship, which
appears to be virtually nonexistent in most other cases. This is hardly a linear relationship,
regardless, and it seems baseless to include it as a control.
Potential Viability Impact: Does Frontrunner Status Matter?
In addition to simply noting some interactive effect between other factors and perceived national
viability, interviewees from dark horse campaigns tended to bemoan the role of frontrunner
status specifically in dampening their ability to function.
For instance, Hyde contrasts DuPont in 1988, who “couldn’t break in to Iowa,” with Carter in
1976, because in 1988 DuPont trailed prohibitive frontrunner George Bush, while in 1976 on the
Democratic side as there was no frontrunner. 43 Likewise, the candidate’s record on fundraising
also matters in Iowa assessments, Kennedy says, as frontrunner status is measured not just in
polling percentage points, but in cold hard cash as well. Whether the candidate is perceived as
the frontrunner weighs heavily in Iowans’ decisions of whom to support, in Kennedy’s
experience. 44 Finally, the primary difference between 2000 and 2004 on the Democratic side,
said Bakker, was the most obvious: The number of candidates, a symptom of the fact that in
2000 the incumbent Vice President was in the race, whereas for 2004 there was no clear
frontrunner leading to a profusion of talent. 45
We can estimate the impact of “frontrunner” status, by testing the average performance
difference for a candidate who leads by, say, 10% or more in national polling, and also holds the
national fundraising lead, as well as for those trailing such a candidate. By that definition, those
who were frontrunners at the time of the Caucus were George W. Bush and Al Gore in 2000,
Bob Dole in 1996, George Bush in 1988, Mondale in 1984, and Carter in 1980. Howard Dean in
2004 also qualifies.
From the data available before the 2004 cycle, it did not appear that frontrunner status matters
disproportionately. The original analysis found that the frontrunner dummy variable is not
statistically significant with a p-value of .892. Likewise, the dummy variable for trailing a
frontrunner was also not significant, with a p-value of .952. These results are replicated by the
new Base Model, in which the frontrunner status variable has a p value of .947, and the
frontrunner trailing factor has a p value of .281, and is positive, indicating that on average,
controlling for other factors, it is an advantage in the Caucus to be an underdog. That result
would be interesting, but we cannot reject the null hypothesis that it was arrived at by random
error.
Thus controlling for national fundraising and polling themselves, there appears to be no marginal
boost in the Caucus to leading in both at the same time.
43
Interview with Tim Hyde, April 16, 2003.
Ibid.
45
Interview with Eric Bakker, May 13, 2003.
44
19
Qualitative Question 4: Does measuring the ideological space a candidate occupies relative to
voters, including Moderation, better measure the role message plays in Iowa?
The Iowa Caucus literature includes an ongoing debate on what importance to place on ideology.
Some (Mayer, 2000; Winebrenner 1998) have found that ideological skew toward the extreme to
have an important impact on Caucus outcomes.46 Others (Stone, Rapoport and Abramowitz,
1989; Stone, 1982; Stone and Abramowitz, 1983; Abramowitz and Stone, 1984) have found that
Iowa’s activists put personal preferences aside in their “desire to support a winner” which gives
to rise to a “moderation hypothesis,” that Iowa’s ideologically unrepresentative sample of voters
choose a relatively representative (or at least viable) candidate.47
Interviewed political professionals focused more on message than ideology. From a campaign’s
view, message is the summation of candidates’ core personal beliefs on important issues of the
day, communicated through all the tactics at their disposal. Voters’ perceived notions of
ideology, we can theorize, come largely (though not exclusively) from candidates’ messages –
and all other factors shaping perceptions of ideology are not within the candidate’s control, by
definition.
Those who discussed it were united in the belief that a candidate’s message mattered a great deal
in determining who performed well in Iowa. To Dole 1988 and 1996 veteran and Iowa
grassroots guru Tom Synhorst, for instance the “different approach with their message” is a
crucial determinant of the success of a presidential campaign in the state. 48
That said, the original, literature-based model failed to find a significant role for ideological
assessments by voters, which again one might argue are generated by the candidate’s message.
The Simple Variables Model saw some explanatory power in the Ideology factor, but it was not
statistically significant at the .05 level, and in the Relative Variables Model it seemed to have no
explanatory power whatever. In addition to the Aggregate Mean Ideology Score used in both
models, prior research tested the percentage of all partisans (Republicans or Democrats) in
Abramowitz, et. al.'s early primary sample who listed a candidate of their party as 0 steps away
on a 7 point ideology scale, as well as the percentage who listed the candidate as 0 or 1 step
away. Neither was statistically significant in the original models.
46
William G. Mayer, “Caucuses: How They Work, What Difference they Make”, in Mayer, William G., ed., In
Pursuit of the White House 2000 – How We Choose Our Presidential Nominees (New York: Chatham House,
2000), pp. 132, 143. Mayer’s 1996 edition of this study is cited in Winebrenner, p. 22. Hugh Winebrenner, The
Iowa Caucuses: The Making of a Media Event (Ames, Iowa State University Press, 2nd Ed., 1998). * Cited
in.Peverill, Squire, “Iowa and the Nomination Process,” in The Iowa Caucuses and the Presidential Nominating
Process (Boulder, Colo.: Westview Press, 1989), edited by Peverill Squire, p. 11. (*)
47
See Walter J. Stone, Ronald B. Rapoport, and Alan I. Abramowitz, 1989, “How Representative are the Iowa
Caucuses?” in The Iowa Caucuses and the Presidential Nominating Process (Boulder, Colo.: Westview Press,
1989), edited by Peverill Squire, p. 45.; Walter J. Stone, Ronald B. Rapoport and Alan I. Abramowitz, “Candidate
Support in Presidential Nomination Campaigns: The Case of Iowa in 1984,” The Journal of Politics, vol. 54, no. 4,
November 1992, p. 1074; Alan I. Abramowitz and Walter J. Stone, Nomination Politics: Party Activists and
Presidential Choice (New York: Praeger, 1984); Walter J. Stone and Alan I. Abramowitz, “Winning May Not Be
Everything, But It’s More than We Thought,” American Political Science Review, 77:945-956; Walter J. Stone,
“Party, Ideology, and the Lure of Victory: Iowa Activists and in the 1980 Prenomination Campaign,” Western
Political Quarterly, 35 527-38. Cited in Walter J. Stone, Ronald B. Rapoport, and Alan I. Abramowitz, 1989, “How
Representative are the Iowa Caucuses?” in The Iowa Caucuses and the Presidential Nominating Process (Boulder,
Colo.: Westview Press, 1989), edited by Peverill Squire, p. 41.
48
Interview with Tom Synhorst, April 11, 2003.
20
In addition, the pre-2004 research investigated an interaction term between “Raw” Ideology and
Viability, to see if more moderate candidates were receiving relatively more votes than their
views would merit because more ideologically extreme voters believed they could win, as the
Moderation Hypothesis would suggest. There was some explanatory effect to that interaction
term, but it was not statistically significant. Thus original models of Iowa Caucus success
suggested further research into how a candidate’s perception of ideology and viability among
caucus goers interact, but initial investigations did not yield a statistically significant result.
With those possibilities preliminarily explored, perhaps the ideological space a candidate
occupies relative to his or her supporters, from voters’ view, can better capture the
acknowledged effect that message and/or ideology has in the Caucus. Also, perhaps those
factors interact with viability, as the “moderation hypothesis” would suggest.
In order to measure those factors, we can return to voter-level data, which contains both
candidate and voter ideology assessments. Using the difference between those assessments, we
can calculate the percentage of a candidate’s party’s Caucus attendees who regard themselves as
“matching” the candidate’s ideology (on a seven-point scale). This “Ideological Proximity”
factor is the one the Base Model employs. Again, this factor was not statistically significant in
the original pre-2004 Models, but it seems the most theoretically sound of the ideological
controls.
0.70
Caucus Votes, Percent
0.60
0.50
0.40
0.30
0.20
0.10
R Sq Linear = 0.319
0.00
0.100
0.200
0.300
0.400
0.500
Ideological Proximity - % of partisans with 0 distance
(updated)
Figure 3: Relationship between candidates’ Caucus Performance and perceived Ideological
Proximity to surveyed partisan activists.
21
To test the moderation hypothesis, what we would need instead is to calculate the degree to
which Viability mitigates ideological distance away from the party’s extreme. That is, we need
to know whether there is a steeper drop-off in candidate performance as a candidate moves away
from voters toward the party’s extreme than towards the center, and if the higher the Viability
score gets, the greater the difference in those two slopes becomes.
Actually operationalizing this concept could be done by:
•
First, determining for each candidate the average perceived Ideological Distance the
candidate has from every Caucus-goer and activist of his party polled. If this variable,
call it “Average Ideological Distance,” were significant, and its slope coefficient
negative, it would indicate that candidates whose message is at variance from Caucusgoers are at risk. The resulting variable is charted below against Caucus Performance in
Figure 2, and initially we find without controlling for other factors that it seems to have a
significant, negative slope. However, when controlling for the main ideological factor in
the Base Model (Percentage of Partisans Surveyed with Zero Ideological Distance from
the Candidate) as well as the number of candidates in the field, this new Average
Ideological Distance variable does not appear statistically significant (p=.718), while the
other two remain highly significant (p<.01).
0.70
Caucus Votes, Percent
0.60
0.50
0.40
0.30
0.20
0.10
R Sq Linear = 0.251
0.00
1.00
1.50
2.00
Average Ideological Distance from partisans of candidate's
own party (Sum:Abs(partisan ideo-cand ideo)/N)
Figure 2: Relationship between candidates’ Caucus Performance and Average Ideological
Distance from surveyed partisan activists.
22
•
Second, creating a Moderate dummy variable representing whether the candidate is
perceived on average as closer to the spectrum’s center or to the party’s extreme than the
Caucus-goers. If the “Moderate” variable were positive and significant when controlling
for Ideological Distance with Caucus Performance as the dependent variable, it would
indicate how much less on average candidates in Iowa are punished for being centrist
than being extreme relative to Caucus-goers. In fact, even when controlling only for
Average Ideological Distance and the number of candidates in the field (technically,
Percentage of the Field that the Candidate Represents), the Moderate dummy variable
though positive is not significant (with a p value of .368), while both other factors remain
significant at p<.001. This indicates that a candidate’s being perceived as closer to the
political center than the average respondent is not necessarily less damaging than being
more extreme, without taking into account Viability.
•
Third, multiplying this Moderate dummy variable by the Viability variable (as measured
by national polling strength) creates a “Moderate Viability” interactive term, which, if its
correlation coefficient is significant and positive when controlling for Average
Ideological Proximity and Viability, would indicate for every percentage point better a
more Moderate candidate fares in the polls relative to his competitors, how much more of
a bump he gets in Caucus performance than an Extreme candidate. In fact, Moderate
Viability controlling for these factors is not significant (p=.973), while all three other
factors remain significant at p<.05.49
•
Fourth, multiplying the Moderate dummy by Average Ideological Distance to create an
Average Ideological Moderation interactive term. If the correlation coefficient of
Average Ideological Moderation were positive and significant controlling for Ideological
Distance, it would indicate the degree to which moving toward the center mitigates the
harm of Average Ideological Distance generally, per unit of distance. In fact, however,
controlling only for Average Ideological Distance and the number of candidates in the
field,50 Average Ideological Moderation while positive is not significant, with a p value
of .293, while both other factors remain significant at p<.001.
•
Fifth, dividing Average Ideological Distance by Viability (National Polling Data) and
taking its natural logarithm creates a “Viability Mitigating Ideology” interactive term.
Regardless of whether a candidate is more centrist or extreme than voters, the greater the
distance from voters the larger this factor will be – and the greater the viability, the
smaller it will be. The log value would give it a linear relationship to the dependent
variable, rather than an obviously asymptotic one. If Viability Mitigating Ideology is
significant and negative controlling for Ideological Distance and Viability, it would
indicate that Viability mitigates the harm of Ideological Distance, regardless of whether
the candidate is moderate or extreme relative to the average respondent. In fact, while
the Viability Mitigating Ideology appears to have some explanatory effect when mapped
directly onto Caucus Performance (see Figure 3 below), when controlling for Viability,
49
In that model, Average Ideological Distance retains a p value of .011, National Polling Data retains a p value of
.003, and Percentage of Field that the Candidate Represents retains a p value of .017.
50
Again, actually, Percentage of the Field that the Candidate Represents, to keep a linear relationship with the
dependent variable
23
Ideological Distance and candidates in the field that effect vanishes entirely. In such a
model, Viability Mitigating Ideology has a p value of only .837.
0.70
Caucus Votes, Percent
0.60
0.50
0.40
0.30
0.20
0.10
R Sq Linear = 0.431
0.00
0.00
1.00
2.00
3.00
4.00
5.00
6.00
Log of Viability Mitigating Ideological Distance Factor National Polling Data and Ideological Distance Interact
Figure 3: Relationship between candidates’ Caucus Performance and the log of interaction term
between National Polling Data (Viability proxy) and Average Ideological Distance from
surveyed partisan activists.
•
Finally, multiplying this Viability Mitigating Ideology term by the Moderate dummy to
create a “Moderation Effect” factor. Moderation Effect would be an attempt to measure
the degree to which Viability mitigates the harm of Ideological Distance for more centrist
candidates only. However, this term too does not rise to a level of significance allowing
us to reject the hypothesis that its explanatory power is arrived at by chance. Controlling
for Average Ideological Distance (p=.009), Viability (p<.001), and Percentage of the
Field that the Candidate Represents (p=.0013), the Moderation Effect variable has a p
value of .545.
This analysis seems to indicate that, given our limited data set:
•
The greater the average perceived Ideological Distance a candidate has from activists of
his party, the greater the damage to his performance in the Caucuses, though not
controlling for his perceived Ideological Proximity (the percentage of his party’s activists
surveyed listing him as the same ideology as themselves);
24
•
A candidate’s being perceived as closer to the political center than respondents is not
significantly less damaging than being more extreme on average;
•
A Moderate candidate faring better in the polls relative to his competitors, and thus being
more a more viable candidate to win the nomination, does not yield a significant bump in
Caucus performance versus an Extreme candidate;
•
A candidate’s moving toward the center does not appear to mitigate the harm of Average
Ideological Distance generally, per unit of distance, relative to moving to the extreme;
•
Success in national polling relative to other primary contestants – Viability – does not
appear to mitigate the harm of Ideological Distance, regardless of whether the candidate
is moderate or extreme relative to the average respondent; and
•
Viability also does not appear to mitigate the harm of Ideological Distance for more
centrist candidates only.
This is by no means to assert that the Moderation Hypothesis is not correct, as some of the same
voter-level surveys as this candidate-oriented model is partially based upon have over many
years demonstrated it. To more fully test for its effect, we could use voter-level data on
assessments of both viability and electability rather than relatively more powerful national
polling data to tease out more subtle interactions. However, for the purposes of our model, we
should retain the Ideological Proximity factor in the Base Model as our measure of the success of
message, and rely on some Moderation variable (such as Average Ideological Moderation) as a
control suggested by the literature, as well as the political professionals, if obliquely.
Qualitative Question 5: Does earned media play an independent role in candidate success?
Several interviewees raised press coverage as a factor in determining Iowa Caucus success.
Informed by his experience with the Biden 1988 race in which the candidate was forced out by
allegations of plagiarism, Link believes that in terms of effect on winning the Caucus, earned
media and paid media rank “neck and neck,” and he would put both ahead of organization.51
Though quality data was lacking on press coverage in the pre-2004 database, some voter-view
data was available on the topic. That data allows us to undertake an investigation of the
correlation between seeing press on a candidate and turning out to support him, by including the
percentage of Caucus-night supporters who had read articles about a candidate.52
In the pre-interview phase of work, there was insufficient data to reliably report findings on the
impact of earned media, since we had data for only 11 cases out of the 75 candidates in our
51
Interview with Jeff Link, June 10, 2003.
Going forward, we should also examine the correlation between seeing press on a candidate and turning out
against him, by including the percentage of non-supporters who had read articles about a candidate, as well. That
factor might constitute a rough approximation of the role of negative press coverage in determining Iowa Caucus
vote share.
52
25
model, and the data is in fact on the percentage of supporters who both read an article or, for
some cases, read a print ad about the candidate.
Suspect though it may seem with such sparse information, we can create a somewhat reliable
index to predict earned media for more of the candidates using factors we believe might
influence Iowa press coverage, namely the number of days and amount of money the candidate
spent in the state and the national polling data for the candidate. Such a model – using
information on the Log of Iowa Spending, Days in Iowa, and National Polling Strength –
predicted 64.9% of the variation in earned media. However, even doing so, we found that this
makeshift Print Media Coverage factor remains statistically insignificant at p=.657.
The new database adds more voter-view press data, as well as a measure of the number of print
articles on each candidate from 1996 through 2004 in four of the major papers read in the state.
Though that data does not stretch into the distant past, we can build an extremely reliable model
to predict who will and will not generate such coverage in Iowa, using as predictors:
• The Number of Days the candidate spent in Iowa,
• The candidate’s National Polling Data
• The Number of Candidates in the Iowa field on Caucus day
• The percentage of total spending in Iowa among same-party candidates that cycle the
candidate’s Iowa spending represents
• The strength of the candidate’s organization, using the Base Model’s Organization Index,
and
• Whether the candidate is a Democrat (which, ceteris paribus, will yield more coverage if
Republican complaints about media bias are correct)
This model predicts 96% of the variation in print coverage for candidates from 1996-2004, with
all but two of the factors statistically significant within the model at .05 or less. It is interesting
to note that the Democratic dummy variable is very strongly significant (p<0.0003) and positive.
Its slope coefficient indicates that Democratic presidential candidates, on average, receive 223
more articles than Republicans over the course of a two-year election cycle, +/- 46.
Using this model, we can estimate print coverage for past candidates with some reliability,
though we could under no circumstances be certain in-Iowa press coverage has not changed over
so many years (in fact it seems likely that it has).
Using the standard Base Model, the factor that this procedure generates still remains statistically
insignificant in the base model, at p=.601. However, since the press variable was estimated in
large part using Days in Iowa, controlling that factor in a different way (% of total days, rather
than the raw number), and controlling for Democrats under the viability threshold who might
have received more coverage on average but fared more poorly in the Caucus, we find something
altogether different.
With those slightly modified controls in place, both the press coverage variable and the
Democratic viability threshold variable become statistically significant, in the expected direction
(positive and negative, respectively).
26
The twin messages this finding sends are clear:
• Controlling for poorly-performing Democrats who might get more press coverage but do
worse in the Caucus than equivalent Republicans, more press coverage on average is
associated with higher totals in the Caucus, and
• Controlling for press coverage, Democrats who stand to garner under 15% in the Caucus
do disproportionately worse than those over 15%, implying that those who get substantial
amounts of press mask the viability threshold’s real damage to their Caucus results.
In terms of the scale of impact press has on Caucus performance, we can say approximately that
for every 100 articles a candidate generates in the four papers measured, on average we could
expect a 2% increase in that candidate’s Caucus performance, controlling for other factors in the
model.
Link’s strongly-worded assertion that earned media coverage is among the most important
determinants of Caucus outcomes is borne out by this analysis. In addition, Link and Bakker’s
insight about the Democratic viability threshold turns out to be important not just in practice, but
in an empirically verifiable way.
In future Caucus models, it will be crucial to include controls for both media coverage and
Democratic stragglers.
Qualitative Question 6: Does a candidate’s home state geography play a role in Caucus
success?
Both the literature and interviewers have touched on the rule of thumb that neighbor-state
candidates do better than those from further away. Winebrenner (1998) notes that “The tendency
for Iowans to support Midwesterners in the caucuses continued in 1996, even though Dole’s
support was somewhat modest.”53 Likewise, Hyde relates that DuPont’s campaign liked his
chances in the 1988 campaign in part because of the candidate’s Illinois residence.54
Adding a dummy variable for geography is relatively straightforward. The initial result was that
though there appears to be some explanatory effect, and the sign is positive as we would
theorize, the factor is not significant, with a p-value of .141. Plugging the variable into the new
Base Model yields even less of a factor (p=.463).
The matter likely bears further investigation, but a (surprising) initial assessment is that
geography does not play a significant role in determining Caucus outcomes, on average. It is
worth considering a control for the factor, however, considering the widespread belief in its
importance.
53
Hugh Winebrenner, The Iowa Caucuses: The Making of a Media Event (Ames, Iowa State University Press, 2nd
Ed., 1998), p. 247.
54
Interview with Tim Hyde, April 16, 2003.
27
Qualitative Question 7: Do perceived chances of success in Iowa (“Iowability”) have an
independent impact separate from national viability – or is Iowa Polling only good for
prediction?
One question raised in passing by an interviewed Iowa pol was whether polling strength within
Iowa leads to an increased perception of viability (or perhaps ‘Iowability’) on the part of a
campaign.55 The capacity of an organization to “make something happen” in Iowa is based on
the “overall campaign environment,” observes Alexander 2000 Campaign Manager Kennedy.
Part of that environment, according to Kennedy, is that polling strength, both within Iowa and
nationally, leads to a perception of viability on the part of a campaign. National polls especially,
Kennedy said, can be driving results in Iowa. In the summer and fall of 1999, the Bush Iowa
team could point to national polls showing that he was beating Gore and leading other
Republican candidates to demoralize and demobilize other campaigns. 56
The impact of perceived Iowa success can be estimated by checking the role of Iowa polling data
in predicting Caucus success. Note that adding Iowa poll strength may mask some of the
predictive power of the other variables, since as a relatively endogenous factor it will be highly
multicollinear with them. Theoretically, Iowa polling is more a measure of the success of the
other factors in the model than a factor itself. Thus unless it has extraordinary explanatory
power of its own and does not merely mask that of other variables, it can and should be excluded
from any explanatory model. (By contrast, including Iowa poll strength in a predictive model
makes good sense theoretically.)
Additional power Iowa polling has, for certain. When originally put into the Relative Variables
model, the Adjusted R-square of the Iowa Polling model shot up to .906, with an unadjusted Rsquare of .937. The F-statistic likewise spiked to 29.748. Iowa polling was highly significant, at
.002. But as theorized, adding Iowa polling to the model, the predictive power of virtually every
other factor was drowned out. Republican organization and the two Viability interacts (with
Relative Days in Iowa and Relative Ideology) retain some explanatory power, but was not
significant with p values of .096, .088 and .093, respectively. Controlling for Iowa Polling Data,
National Polling Data was not at all significant, with a p-value of .745.
The only factors that were statistically significant with Iowa Polling data present were
Democratic organization, relative TV spending, and relative Iowa spending, with p-values of
.020, .043, and .029. Note that Iowa spending was a control in the original models that under no
other circumstances had been statistically significant. What is more, controlling for Iowa Polls,
relative Iowa spending’s slope coefficient was negative. That suggested that given polling
strength in Iowa and the other factors in the model, additional spending actually hurts a
candidate, on average, or at least is associated with losing efforts trying to spend their way into
the history books. Of course, it may also be an anomalous result, or just a red flag about the role
Iowa Polling Data plays in the model, but is certainly of interest.
Using the new data set and the Base Model, Iowa polling has a similar result, spiking the R2 to
.881, the Adjusted R2 to .861, and the model’s F-statistic to 45.204. The Organizational Strength
index remains significant (p=.018), as does Days in Iowa (p=.006), but all other variables’
55
56
Interview with Brian Kennedy, April 8, 2003.
Ibid.
28
explanatory power is indeed swamped. The % of total Iowa Spending factor is negative again,
though not statistically significant, as is the % of total Television Spending factor. Interesting
results, but highly suspect, as Iowa spending and TV ads are intended to bolster Iowa polling.
Thus regardless of its beneficial effects on the model’s power, there are important theoretical
reasons to exclude Iowa Polling Data from an explanatory model, as cited above. It certainly
appears to be masking the effects of some variables through multicollinearity. And it does not
necessarily meet the standard of inclusion in a causative model, being potentially a byproduct of
the causes in the model. Its inclusion likely hinges upon whether the model in question is
explanatory or predictive in nature.
Qualitative Question 8: Do minority candidates fare disproportionately worse in the Caucus,
once adequately controlled for both viability and ideology?
A final, consistent charge within the political community is that Caucus voters are
demographically unrepresentative of either the parties or the nation as a whole. That raises the
question whether those largely white voters are in fact selectively disadvantaging minority
candidates. None of the interviews specifically raised this specter, but in keeping with exploring
questions raised in political practice, this last one should be explored.
Admittedly, it is a challenging question to answer with respect to the caucus, since only two
minority candidates, Jackson (1984 and 1988) and Keyes (1996 and 2000), have contested them.
What is more, the two candidates’ relative liberalism and conservatism respectively, as well as
perceptions that they could not win, would have a great deal of explanatory power over their
performance. And of course, many viability measures might be partially a proxy for racial
proclivities on the part of either national polling subjects or Caucus-goers, so teasing out true
viability assessments might be a challenge.
That said, with a somewhat more successful interaction term between Ideology and Viability
built into the model, a dummy variable for the two minority candidates can be estimated. Its
results would be analogous to testing a demographic Moderation Hypothesis: Does the fact that
Iowa Caucus-goers are demographically unrepresentative translate into a proportionally worse
performance for minority candidates, or does the unrepresentative demography of Iowa not in
fact hamper minority candidates, ceteris paribus?
The results were originally inconclusive. Controlling for the other factors in the pre-2004
Relative Variables model, a minority dummy variable was not statistically significant, with a pvalue of .207. Including it in the model did have an impact – the Republican organization
variable, for instance, became significant. It is also worthy of note that the dummy’s slope
coefficient is actually positive. With new data in the Base Model, the results are not even that
promising. There appears to be no impact on the model, and the factor itself has a p value of
.885.
However, though these findings hint that race plays no negative role in the Caucus, more
spadework would be called for to pronounce it so more assertively. Accordingly, the factor
should be explored in future constructions of Caucus models, if only to demonstrate that race is
not playing a role in outcomes.
29
VII. Prediction of the 2004 Caucus
Based on the preliminary results of this analysis – before the completion of the new data and
Base Model, but after the first testing of the political professionals’ input – the working model
was put to the test. Before the 2004 Caucus, enough new data was available to generate a
prediction using the working model.
The prediction is included in Table 4 below, and the estimation of the model is provided in
Appendix 1. In the 2004 Predictive Model, the R2 and Adjusted R2 are substantially higher than
in the original models, at .928 and .881 respectively, indicating that we are explaining about 90%
of the variation in Caucus performance. Note also that the F-statistic has climbed to 19.918 in
spite of the addition of more factors. Fewer factors are significant, but the 2004 Predictive
Model included Iowa Polling Data, aimed as it was at prediction not explanation, which largely
explains the difference.
2004 Iowa Democratic Caucus
Vote %, Scaled Prediction
30.00%
25.00%
20.00%
26.71%
21.16%
19.16%
15.00%
Scaled Prediction
15.00%
10.00%
6.12% 5.14%
4.43%
5.00%
2.28%
Sh
ar
pt
on
Ku
ci
ni
ch
C
la
rk
dt
Li
eb
er
m
an
ep
ha
r
G
D
ea
n
ar
ds
Ed
w
Ke
rr
y
0.00%
TABLE 4: Results of 2004 Predictive Model
Note that the Model correctly predicts the order of the first four finishers, though it is a few
percentage points off from their exact finishes (Kerry, 37.6%, Edwards, 31.9%, Dean 18.0%,
Gephardt, 10.6%). It overestimates Lieberman, Clark and Sharpton, none of which seriously
contested Iowa and who therefore received no delegates at all. This indicates that a Caucus
model should include a “Caucus Skipper” model, to account for the negative effect merely
announcing one is going to bypass the state has on performance. The 2004 Predictive Model
also slightly overestimates the result for Kucinich, who in fact received only 1.3 of the delegate
total. Taken together, these low-tier candidate errors also cry out for a Democratic Viability
Threshold control variable. But these are lessons for the 2008 contests.
In the model’s defense, we should also make plain that the 2004 Caucus moved with
extraordinary speed at the end and was considered a tossup whose outcome the media took pains
not to attempt to predict. The frontrunner, Gov. Dean, was upset in a dramatic fashion that was
to send his campaign into a terminal decline. Also, Gephardt’s much-vaunted organization was
swamped by the sudden sky-high popularity of Kerry and Edwards, as well as the passion of the
Deaniacs. Thus a prediction of this clarity may be regarded to some extent as a testament to the
model’s effectiveness at cutting through various competing factors to generate accurate results.
30
VIII. Conclusion
With apologies to Iowa natives, working with political practitioners yielded a fertile crop of
further questions to explore, and a harvest of answers to those questions. We can state with
reasonable confidence that differences between the two parties’ early straw polls does not make
the Democratic one a poor proxy for organization, and that differences in Caucus procedures do
not necessarily mean that organization matters more on the Democratic side. We also have
tentative indications that Forbes’ advertising blitz in 1996 had some impact on the balance of
advertising and organization that has been effective in past Caucuses.
More importantly, we have newly verified that, properly controlled, a candidate’s Iowa press
coverage has a significant impact on Caucus performance. We have shown that the Democrats’
viability requirements at the precinct level disproportionately damages lagging candidates’
delegate percentages, if only when its effects are included in a properly specified model. And it
is stark that Iowa polling strength remains a good predictor of Caucus outcomes, especially when
combined with data on other factors, though it is less clear whether it is perceived chances of
success in Iowa that have an independent causative impact.
Likewise, it appears that though Viability matters in the Caucus, being the frontrunner in national
polling and fundraising does not give a candidate an additional boost. Further, we have hints that
Viability may interact significantly with a candidate’s Days in Iowa in important ways, though
precisely how should be further explored.
Also, it does not seem that a candidate’s home state geography plays an obvious role in
determining Caucus results. And, contrary to implied charges in the press and political
community, minority candidates do not appear to fare disproportionately worse in the Caucus,
once adequately controlled for both viability and ideology.
Finally, the model influenced by the input of political professionals, in addition its ability to
explain the role of additional factors, seems to have performed better than pundits and pols at
predicting the results of the 2004 Democratic Caucus.
Howard Dean went down to defeat in that contest, and John Kerry’s meteoric rise thereafter pays
tribute to the Iowa Caucus’ major role in selecting our president. Understanding which
candidates fare better or worse in the state is therefore equally important. Based on these
analyses performed of Iowa Caucus specialists’ views, hopefully we have a sharpened view of
what wins in the Hawkeye State.
31
Bibliography
Abramowitz, Alan I. and Walter J. Stone. 1984. Nomination Politics: Party Activists and
Presidential Choice. New York: Praeger,.
Abramowitz, Alan I., John McGlennon, Ronald B. Rapoport, and Walter J. Stone. 2001.
“Activists in the United States Presidential Nomination Process, 1980-1996.” Inter-university
Consortium for Political and Social Research 6143, Second ICPSR Version.
Adams, Williams C. 1987. ‘As New Hampshire Goes.’ In Gary R. Orren and Nelson W. Polsby,
eds., Media and Momentum. Chatham, NJ: Chatham House.
Adkins, Randall E. and Andrew J. Dowdle, “How Important Are Iowa and New Hampshire to
Winning Post-Reform Presidential Nominations?” Political Research Quarterly, vol. 54, no. 2
(June 2001) 431-444.
Bartels, Larry M. 1989. After Iowa: Momentum in Presidential Primaries. In Peverill Squire,
ed., The Iowa Caucuses and the Presidential Nominating Process, 121-148. Boulder, Colo.:
Westview Press.
Brady, Henry E. 1989. “Is Iowa News?” in The Iowa Caucuses and the Presidential Nominating
Process, edited by Peverill Squire, 89-119. Boulder, Colo.: Westview Press.
Hull, C. Clayton. “Explaining Vote Share in the Iowa Caucus,” American Political Science
Association conference paper, forthcoming August 2004.
Lengle, James I. 1981. Representation and Presidential Primaries: The Democratic Party in
the Post-Reform Era. Westport, Conn.: Greenwood Press.
Mayer, William G. 1996. Forecasting Presidential Nominations. In Mayer, William G., ed., In
Pursuit of the White House – How We Choose Our Presidential Nominees. Chatham, New
Jersey: Chatham House.
__________. 2000 ed. In Pursuit of the White House 2000 – How We Choose Our Presidential
Nominees. New York: Chatham House.
__________. 2000. Caucuses: How They Work, What Difference they Make. In Mayer,
William G. ed., In Pursuit of the White House 2000 – How We Choose Our Presidential
Nominees. New York: Chatham House.
Polsby, Nelson W. 1989. The Iowa Caucuses in a Front-Loaded System: A Few Historical
Lessons, in The Iowa Caucuses and the Presidential Nominating Process, edited by Peverill
Squire, 149-161. Boulder, Colo.: Westview Press.
Squire, Peverill. 1989. Iowa and the Nomination Process. In Peverill Squire, ed., The Iowa
Caucuses and the Presidential Nominating Process, 1-18. Boulder, Colo.: Westview Press.
32
Stone, Walter J. Party, Ideology, and the Lure of Victory: Iowa Activists and in the 1980
Prenomination Campaign. Western Political Quarterly, 35:527-38.
_________. Winning May Not Be Everything, But It’s More than We Thought. American
Political Science Review, 77:945-956
Stone, Walter J., Ronald B. Rapoport and Alan I. Abramowitz. 1989. How Representative are
the Iowa Caucuses? In Peverill Squire, ed., The Iowa Caucuses and the Presidential Nominating
Process, 19-49. Boulder, Colo.: Westview Press.
__________. 1992. Candidate Support in Presidential Nomination Campaigns: The Case of
Iowa in 1984. Journal of Politics 54: 1074-1097.
Stone, Walter J., and Ronald B. Rapoport. 1994. Candidate Perception among Nomination
Activists: A New Look at the Moderation Hypothesis. Journal of Politics 56:1034-1052.
Winebrenner, Hugh. 1998. The Iowa Caucuses: The Making of a Media Event. 2nd Ed. Ames:
Iowa State University Press.
Wolfinger, Raymond E. 1989. Who Is Vulnerable to the Iowa Caucuses? In Peverill Squire,
ed., The Iowa Caucuses and the Presidential Nominating Process, 163-168. Boulder, Colo.:
Westview Press.
33
Appendix 1: Model Estimation Results
1. ORIGINAL Simple Variables Model
Simple Variables Model
Beta
p-value
Factor
National Polling Performance, as measured by % support the candidate obtained in a
national poll soon before the Iowa Caucus.
0.34
Organizational Strength, as measured by the percentage of votes received by the
candidate in the Ames Straw Poll (for Republicans) or Jefferson-Jackson dinner straw poll
(for Democrats) the summer before the Caucus.
0.421
0.099
Log of TV Advertising Spending in real (2002) dollars.
Aggregate Mean Ideology Score for each candidate, in interaction with the Democratic
dummy variable, to control for the “moderation hypothesis,” that caucus-goers
strategically look to the more moderate candidates for viability reasons, in spite of their
ideological true feelings.
Number of Days Spent in Iowa.
Log of Iowa Spending in real (2002) dollars.
0.008
0.013
0.016
0.026
0.093
0.0009
0.162
0.025
0.171
0.031
0.192
Number of Candidates in the Field.
-0.0018
0.929
Dummy Variables for 1980, 1984, 1996, and 2000, to control for one-way fixed effects
for time, as well as to help account for measurement errors in 1980 and 2000 data.
Various None significant
R2: .858
Adjusted R2: .790
F-Statistic: 12.586
Sig.: <.001
Log of National Fundraising in real (2002) dollars.
2. ORIGINAL Relative Variables Model
Relative Variables Model Results
Beta
p-value
Factor
Relative TV Advertising Spending in constant dollars, in %
difference from average form, isolating the explanatory power of the
mean ad spending by competitors.
0.078
0.002
Overall Organizational Strength, an index taking into account the
scaled performance in an early straw poll (that is, scaling results to
the number of candidates making it to the Caucus as opposed to the
number of candidates in the field at the time of the early Straw Poll),
as well as the % of supporters saying they were contacted by the
campaign’s organization, and %s of supporters who personally 1)
attended a rally or meeting, 2) did canvassing, and 3) tried to
convince friends to support the candidate, as well as other controls.
0.51
0.002
National Polling Performance. Same as Model 1.
0.203
0.033
Number of Candidates in the Field. Same as Model 1.
0.0251
0.098
Relative Iowa Spending in real (2002) dollars, again as a %
difference from average spending of the candidates in that cycle of
that party.
0.039
0.543
Aggregate Mean Ideology Score for each candidate, in interaction
with the Democratic dummy variable, to control for the “moderation
hypothesis,” that caucus-goers strategically look to the more
moderate candidates for viability reasons, in spite of their ideological
true feelings.
0.0024
0.831
Dummy Variables for 1980, 1984, 1996, and 2000, to control for
one-way fixed effects for time, as well as to help account for
None
measurement errors in 1980 and 2000 data.
Various
Significant
R2: .846
34
Adjusted R2: .796
F-Statistic: 17.002
Sig.: <.001
3. ORIGINAL Predictive Model
Predictive Model Results
Factor
Number of Candidates in the Field. Same as Model 2
Relative TV Advertising Spending. Same as Model 2.
National Polling Performance. Same as Model 2.
Scaled Early Organizational Strength. Same as Model 2.
R2: .836
Adjusted R2: .819
F-Statistic: 47.291
Sig.: <.001
Beta
p-value
0.014
0.069
0.21
0.534
0.105
<.001
0.017
<.001
4. UNFILLED Base Model
(Forces exclusion of TV spending, because it has too few cases)
(Model automatically excludes % of candidates in the field)
Unfilled Base Model
(Constant)
Scaled Precaucus Straw Poll Percent, unfilled
National Polling Data
Scaled unfilled % of total Iowa Spending, Pre-Election Year,
Combined third and fourth quarter (to account for unfilled data
gaps)
% OF FUNDRAISING, Cand's % of active same-party
candidates' total money raised, second half of pre-election year,
unfilled
% of total Days in Iowa active same-party candidates' total DIA,
unfilled
IDEOLOGICAL PROXIMITY - % of partisans with 0 distance
(updated)
Year Dummy Variables
Unstandardized
Beta
-0.117
0.272
0.165
Sig.
0.449
0.45
0.406
0.026
0.925
0.463
0.322
-0.063
0.78
0.402
0.395
None
significant
Various
a Dependent Variable: CAUCUS VOTE SHARE, Percent
R2=.850
Adj R2 = .740
F = 7.724
Model sig. < .001
35
4. Filled Base Model, WITH National Fundraising included
(highly multicollinear with other factors)
Filled Base Explanatory Model, with National Fundraising
(Constant)
Days in Iowa for candidate
Unstandardized
Beta
-0.166
Sig.
0.073
0.001
0.264
0.501
0.296
0.199
0.023
0.024
0.148
0.207
0.393
0.069
0.696
-0.045
0.753
0.014
Various
0.946
None sig
National Polling Data
ORGANIZATION INDEX, using precaucus straw poll % filled, rally f, and contact f
IDEOLOGICAL PROXIMITY, filled - % of partisans with 0 distance (updated)
% OF FIELD that candidate represents
% OF TOTAL IOWA SPENDING among same-party cands that cycle, Pre-Election
Year 3rd and 4th quarter, f using fundraising, %field, natl poll
% TV SPENDING TOTAL, predicted using %field, %$raised, natl poll, reciepts real,
IAspnd (real, f), %totDIA (f), Forbes, Dem
% OF FUNDRAISING, Cand's % of active same-party candidates' total money raised,
second half of pre-election year, unfilled
1976, 1980, 1984, 1996, 2000 & 2004 Dummy Variable
a Dependent Variable: CAUCUS VOTE SHARE, Percent
R2: .803
Adj. R: .739
F Statistic: 12.522
Sig. < .001
5. 2004 Predictive Model
2004 Predictive Model
(Constant)
IA POLLING %57
% OF TOTAL DAYS IN IOWA58
% TOTAL TV Spending59
% OF TOTAL IOWA SPENDING60
PRECAUCUS STRAW POLL %61
NATIONAL POLLING DATA
% TOTAL PRESS COVERAGE in 4 top Iowa region papers among
active same-party candidates62
% TOTAL RECEIPTS, Candidate's % of active same-party candidates'
total money raised
FAVORITE SONS home state next to Iowa dummy
% OF FIELD that candidate represents
FORBES-TV SPENDING Interactive term
AFRICAN-AMERICAN dummy variable
DEMOCRATIC PRE-CAUCUS STRAW POLL % Interactive term
1976, 1980, 1984, 1996, and 2000 Dummy variables to control for fixed
57
Unstandardized
Beta
0.036
1.178
-0.301
0.248
-0.311
0.192
-0.212
p-Value
0.223
4.38E-05
0.022
0.084
0.124
0.197
0.208
-0.108
0.525
0.102
-0.016
0.082
-0.044
0.003
0.079
Various
0.565
0.584
0.633
0.837
0.926
0.324
None
Filled using an index of % of the field, % of $ raised, % tot. IA spend filled, natl poll, org str scaling factor,
receipts (real), IA spnd (real), precauc (filled) % tot. DIA (filled), home state next to IA.
58
Filled using % of field, % of national fundraising, and % in national polls to create index for 1980 Republicans
59
Filled using %field, %$raised, natl poll, org str scale, reciepts real, IAspnd (real, f), %totDIA (f), Forbes, Dem.
60
Real, filled using Forbes-DIA & Forbes-Natl funds interacts, avg. days in IA of all cands, % of field, Demdum,
natl polls, yrs out of power, & receipts (real).
61
Filled using index of %field, %tot $ raised, receipts (r), %tot ia spnd (f), ia spnd (r, f), natl poll, & org str scale.
62
Filled using %totDIA, natlpoll, ia poll f, precaucf, #cand, receiptsr, forbes, dem, tvrf, %tottvf, iaspdrf, %iaspdtotf.
36
effects for time
a Dependent Variable: Caucus Votes, Percent
R2: .928
Adjusted R2: .881
F Statistic: 19.918
Sig. < .001
sig.
37