Primary Politics: Race, Gender, and Age in the 2008 Democratic Primary

Abstract

Despite Barack Obama’s momentum in the early phase of the Democratic nomination, the process of selecting a nominee took longer than usual. Obama’s momentum, it seems, got stuck, and the 2008 Democratic presidential nomination was an unusually drawn out affair. Even when it appeared Barack Obama would win the nomination, many Clinton supporters said they would support John McCain in the general election. Why were some Democrats unwilling to join the Obama bandwagon once he emerged as a viable front‐runner – and ultimately the Democratic nominee? In this paper we bring a unique set of panel data from the 2008 Cooperative Campaign Analysis Project (CCAP) to bear on questions about primary vote choice, examining the evolution of preferences over the unusually long and intense 2008 Democratic presidential nomination campaign. Attitudes about race predict vote choice in partisan contests; here we show that (conditional on the presence of a black candidate) these attitudes help explain the dynamics of candidate support over the prolonged intra‐party contest for the 2008 Democratic presidential nomination.

Acknowledgements

We thank Seth Hill, Michael Tesler, and Delia Bailey for help with comparisons to data from other election studies. We benefited greatly from conversations about race and politics with David Sears, Paul Sniderman, Michael Tesler, Frank Gilliam, Ryan Enos, and participants at the UCLA meeting on Race and the Presidential Campaign in September of 2008. Larry Bartels, Andrew Gelman, Michael Tesler, the JEPOP editors and reviewers, and other authors in this issue provided helpful feedback on earlier drafts. We thank Dick Niemi and Harold Stanley for the invitation to participate in this collection and in the Geurin‐Pettus Conference at SMU‐in‐Taos on the 2008 US Presidential Election, and for being our mentors at Rochester. Portions of this paper were presented in seminars at Princeton University, Yale University, Stanford University, UCLA, and Oxford University. Jackman was a Visiting Professor, United States Studies Centre, University of Sydney, in 2009 where much of the writing and research for this article was conducted.

Notes

1. The pledged delegate count remained very close right until the last contest, making Clinton’s late victories important. Further uncertainty arose from the controversy over the status of Florida and Michigan delegates; these states held their primaries earlier than allowed by party rules and both primaries were won by Clinton. On the other hand, the superdelegate count appeared to favor Obama and at an increasing rate throughout the process. These unknowns left enough uncertainty about what the delegate count actually was to keep Clinton alive throughout.

2. A sampling of the literature includes Aldrich (1980); Wattier (1983); Bartels (1988); Geer (1989); Abramowitz (1989); Norrander (1986); Brady and Johnston (1987); Abramson et al. (1992); Johnston et al. (1992); Mutz (1995, 1997); Vavreck et al. (2002); Stone et al. (1992); Morton and Williams (2001); Polsby et al. (2007); Fowler et al. (2003); Mayer (2000).

3. Symbolic racism taps components of racial prejudice in domains such as the values and norms of racial groups (e.g. the stereotype that a particular racial group violates norms of hard work or self reliance) or support for public policies designed to redress racial inequality (e.g. affirmative action).

4. The symbolic racism measures ask respondents to agree or disagree with the following: (1) Generations of slavery and discrimination have created conditions that make it difficult for African Americans to work their way out of the lower class. (2) Many other minority groups have overcome prejudice and worked their way up. African Americans should do the same without any special favors. (3) Over the past few years, African Americans have gotten less than they deserve. (4) It’s really a matter of some people not trying hard enough; if African Americans would only try harder they could be just as well off as whites. Respondents could answer: agree strongly, agree somewhat, neither agree nor disagree, disagree somewhat, disagree strongly.

5. The Common Content portion of CCAP is the first 10 minutes of every respondent’s survey. The total length of the survey is 20 minutes. After the common part of the survey respondents are routed to any one of the many team studies, which make up the second half of the survey. For details on the mechanics of how this works, see Vavreck and Rivers (2008).

6. From here on, when we say “primary” we mean “primary or caucus”.

7. The remaining candidates include Chris Dodd, Joe Biden, Mike Gravel, Dennis Kucinich, and Bill Richardson.

8. Note that the last category on the x‐axis is for those who refuse to report their income.

9. Of course, we are alert to the possibility that evaluations of Bill Clinton – measured in March – are endogenous to voting intentions, particularly since Bill Clinton was playing such an active and vocal role in his wife’s campaign, including some widely‐reported criticism of Obama’s experience and electability. On balance, we think our elicitation of evaluations of Bill Clinton – asking respondents to rank a set of recent US presidents – puts some cognitive and affective distance between evaluations of Clinton and preferences over Democratic candidates.

10. We ignore the discrete, ordinal nature of the five point responses. The matrix of polychoric correlations for the five indicators (computed using pairwise deletion of a small amount of missing data) has an eigen‐structure that suggests a one dimensional factor analysis model is sufficient for these data; using responses from whites in the September wave of CCAP, the eigenvalues of the correlation matrix are 2.7, 0.6, 0.3 and 0.3.

11. Local logistic regression is a version of loess tailored for the case of a binary dependent variable y; it is a semi‐parametric (or largely “model free”) estimate of the proportion of cases with y = 1 in a local neighborhood of a target point x₀ , formed by running a weighted logistic regression of y on x with weights that reach a maximum at the target point x₀ , but then taper away to zero (see Wood, 2003).

12. We fit the multinomial logistic model using the multinom function in the R package nnet (Venables & Ripley, 2002).

13. That is, 1.5 × −0.64 = −0.96 which is about 120% the magnitude of the 0.82 logit coefficient on the black indicator variable in the Obama/Clinton pairing.

14. The form of our vote intention and vote report questions is worth explaining. Respondents were administered items tailored to their state of residence. This included asking respondents in primary states about “primaries” and respondents in caucus states about “caucuses”. But importantly, each respondent was fielded a vote intention or report depending on whether his or her state primary was yet to occur or had already taken place. In this way, the CCAP primary vote questions are closely tied to the political reality experienced by each respondent; we did not rely on a vague or unrealistic item asking respondents to give a hypothetical vote intention as “if their state primary were held today”. Quite the contrary. If a respondent lived in New Hampshire, he or she got the vote intention question in December, but a vote report question in all the subsequent waves. Someone in Pennsylvania got a vote intention question all the way through the March wave.

15. This includes respondents who say they are “not sure” about which candidate to support in the initial wave of interviews.

16. A total of 8,425 respondents (an unweighted count) indicate that they intend to vote in the Democratic contests and provide some indication as to their preferred candidate in December (including “Not sure”). After accounting for those who dropped out, voted for someone other than Obama or Clinton, or were missing on covariates, we are left with 4,718 cases for analysis (again, this is an unweighted count). We lose another three respondents supporting other Democratic candidates who state they were “Not sure” as to their political ideology, due to over‐fitting when trying to include these respondents in the logistic regression analysis (these three respondents all report voting for Obama).

17. We restrict the g and h functions to lie in the class of thin‐plate regression splines (e.g. Wood, 2003: 157–160) and estimate the resulting functions so as to minimize a penalized goodness‐of‐fit criteria (with the penalty term protecting against the over‐fitting the data). The resulting model – a semi‐parametric logistic regression model, or a generalized additive model – is implemented using the R package mgcv (Wood, 2008). In the case of a binary dependent variable, the fitting criterion is the Unbiased Risk Estimator, equivalent to Mallows’ (1973) C_p model selection criterion (see Wood, 2006, 2008).

18. For the Obama and “Not sure” initial states, we reject the null hypotheses that the non‐parametric functions g_jt (r_i ) mapping racial resentment r_i to transition probabilities are constant over the three time periods, and we show the three functions for each of these initial states. For each of the other three initial states, we fail to reject H₀ : g_jt = g_j , t = 1, 2, 3, and so a single non‐parametric function g_j (r_i ) is fit, with the three separate functions separated by intercept shifts (the wave‐specific offset terms α_jt ).