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. N umerous 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, 2005Mayer 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...
In fundraising, potential candidates who do not collect sizable amounts of "early money" may be effectively eliminated even before the start of the Iowa Caucus. This winnowing raises concern about the impact money has on narrowing the field of candidates from whom voters can choose. To better grasp patterns of successful fundraising, we explore where candidates obtain funds during the preprimary and primary periods. We use individual contributions data from the Federal Election Commission during the preprimary and primary periods of the 2008 Republican presidential nomination contest. Findings suggest that although California, New York, and Texas provide disproportionate amounts of early financing, the ability of presidential aspirants to broaden their support is indicative of campaign success.
No abstract
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.