Randall E. Adkins

The New Hampshire Effect in Presidential Nominations

In order to demonstrate challenges to conventional wisdom (Aldrich 1980a, b; Bartels 1985 1988; Orren and Polsby 1987), this article develops several forecasting models of the presidential primary vote to compare to a baseline model of the aggregate primary vote (APV) that uses pre-primary and New Hampshire primary data. The models indicate that candidates’ Gallup poll position and cash reserves are significant positive predictors of a candidates’ primary vote share, though there are differences between forecasting models of the primary vote in Democratic and Republican nomination campaigns. Parallel models incorporating results of the New Hampshire primary improve the predictive power of the baseline model, indicating that the bellwether primary has a “correcting” effect on the relative standings of some candidates seeking a presidential nomination. This effect is substantially greater for Democrats than for Republicans.

How Important Are Iowa and New Hampshire to Winning Post-Reform Presidential Nominations?

While conventional wisdom holds that the first delegate selection events in Iowa and New Hampshire are important influences on the outcome of the presidential selection process, scholars increasingly question whether victories in these ‘bellwether’ contests are sufficient to propel darkhorse candidates to the nomination. This study utilizes four OLS regression models to predict nomination outcomes from 1980 to 1996 where the incumbent president did not sit for reelection. Earlier research demonstrated the possibility of forecasting presidential nominations by examining the results of (1) public opinion polls; FEC records regarding (2) money raised; and (3) cash reserves; and (4) whether candidates were southern Democrats (Mayer 1996a; Adkins and Dowdle 2000). Utilizing measures representing the outcome of the Iowa caucuses and the New Hampshire primary, this study contrasts the effect of momentum from these early contests on final primary vote totals. Evidence suggests that New Hampshire plays a role in determining the ordinal ranking of candidate finishes, but not necessarily the winner of the party nomination.

The Viability Primary Modeling Candidate Support before the Primaries

Public support before the primaries is the strongest predictor of presidential candidate attrition and of the aggregate primary vote. Yet little is known about the factors that drive candidate preferences before the primaries. This article examines pre-primary candidate support in national Gallup polls for open presidential nomination races from 1976 to 2004. The study finds that candidate background characteristics have marginal effects on mass partisan support during the earliest phase of the nomination campaign and that campaign-related factors significantly affect pre-primary candidate support once the campaign begins. Prior levels of support, network news coverage, and party elite endorsements are significant factors in explaining variation in mass partisan support for candidates throughout the nomination campaign. The decisions of well-known, party “heavyweights” to enter or not enter the race affect the choices available to partisan voters and the overall competitiveness of the nomination campaign.

Is the Exhibition Season Becoming more Important to Forecasting Presidential Nominations

The Democratic and Republican nomination contests for the 2000 election cycle produced seemingly different dynamics with which to test previous models for forecasting presidential nominations (Adkins & Dowdle, 2000; Mayer, 1996; Steger, 2000). First, although the number of Republican contestants was only slightly below the average for the postreform period (6 compared with 7), the battle between Democrats Bill Bradley and Al Gore was the smallest field without an incumbent president running for reelection during the postreform era (average of 7.5). Second, although neither Bradley nor Gore held a clear-cut advantage in preprimary polling or fundraising, George W. Bush held a commanding lead in both. Even though the most recent presidential nomination cycle possessed certain idiosyncrasies, the growing stability of the “rhythm of the race” since the reform movement makes forecasting nominations possible (Adkins & Dowdle, 2000; Barilleaux & Adkins, 1993; Mayer, 1996). Using data available at the culmination of the “exhibition season,” which ends prior to the Iowa caucuses, Adkins and Dowdle (2000) attempted to forecast the outcome of presidential nominations in the postreform era using two models to predict the percentage of the primary vote total that presidential aspirants received in races in which the incumbent president did not sit for reelection. Both models employ

Forecasting Presidential Nominations in 2016: #WePredictedClintonANDTrump

A number of scholars successfully modeled and predicted presidential nomination outcomes from 1996–2008. However, dramatic changes occurred in subsequent years that would seem to make replicating these results challenging at best. Building on those earlier studies, we utilize a series of OLS models that included measures of preprimary resources and early campaign successes or failures to forecast that Hillary Clinton and Donald Trump would win the Democratic and Republican presidential nominations in 2016. This outcome suggests that some fundamental factors governing nomination outcomes have not changed despite the conventional wisdom. Numerous models forecast general election outcomes by employing a variety of economic and political measures to make accurate predictions about whether the party in control of the White House will retain or lose the presidency (for an overview see Campbell 2012 ). In many ways forecasting presidential nominations presents a more challenging task. Important individual-level cues such as partisanship or systemic-level factors such as economic growth or the popularity of the incumbent are helpful in understanding why a voter might choose Bill Clinton over George W. Bush in 1992. Unfortunately, they are not useful in explaining why the same individual picked Paul Tsongas over Bill Clinton or Tom Harkin nine months earlier in the New Hampshire primary (Steger, Dowdle, and Adkins 2012 ). While the McGovern-Fraser reform movement of the early 1970s created a new system of presidential nominations designed to increase the role of voters in picking party nominees, a period of stability in the nomination process of both parties’ emerged by the end of the 1980s (Barilleaux and Adkins 1993 ). As these contests became more routinized, a number of scholars attempted to forecast the results of the presidential primary season by utilizing factors such as polling, fi nancial resources, and elite support (Adkins and Dowdle 2000 , 2001a , 2001b , 2005 ; Mayer 1996 ; Steger 2000 ; see Steger 2008 for a comparison of the forecasts generated by the diff erent models). Momentum from performing well in early primaries was also found to play an important role in determining nomination outcomes (Bartels 1988 ), though there is some controversy about the precise eff ect of particular contests (Adkins and Dowdle 2001a ; Christenson and Smidt 2012 ; Hull 2008 ). At first glance, current events appear to have altered this equilibrium in at least two important ways. First, super PACs, a relatively new type of political committee that arose from the Speechnow v FEC and Citizens United v FEC court decisions in 2010, should alter the impact of traditional sources of campaign fi nance (Dwyre and Braz 2015 ). Second, the Republican elite has arguably fragmented in recent years, which should aff ect elite support on the process (Steger 2015 ). Since traditional forecasting models encountered diffi culty predicting the 2004 Democratic nomination correctly (Steger 2008 ), these new factors should make predicting recent nomination outcomes even more challenging. MODEL SPECIFICATION To forecast presidential nomination outcomes this research employs two OLS regression models that use the “open” presidential nomination contests from 1980–2012 and then applies the estimates to the 2016 Democratic and Republican presidential nomination contests to create forecasts for each. 1 The models examine Democratic and Republican contests from 1980 to 2012 inclusive, with the exception of the 1980, 1996 and 2012 Democratic Andrew J. Dowdle is a professor in the department of political science at the University of Arkansas. He can be reached at adowdle@uark.edu . Randall E. Adkins is a professor in the department of political science and associate dean of the College of Arts & Sciences at the University of Nebraska at Omaha. He can be reached at radkins@unomaha.edu . Karen Sebold is a clinical assistant professor in the department of political science at the University of Arkansas. She can be reached at ksebold@uark.edu . Jarred Cuellar is a graduate student at the University of Arkansas. He can be reached at jcuellar@uark.edu .