(This post was co-written with John Graves, summer intern at the Castleton Polling Institute and student at Mill River Union High School, Clarendon, VT)
With the Vermont state primary behind us, the Castleton Polling Institute went back to the July VPR Poll to explore the relationship between the candidates’ relative favorability and their share of the primary votes. Without developing a “likely voter” model (which in low-turnout elections becomes very difficult), we simply used the favorability ratings from all of the respondents who identified themselves as either Democrat or Republican and as potential primary voters.
Using the principle of transitivity from rational choice theory, we made the following presumptions:
- If Respondent A rated Candidate X more favorably than they rated Candidate X’s primary opponents, then Respondent A would choose Candidate X. Thus the probability of Respondent A’s vote going to Candidate X would be 1, and the probability of Respondent A’s vote going to Candidate Y or Z is 0.
- If Respondent A rated all candidates the same, Respondent A is equally likely to choose any candidate. Thus, the vote probability in a three-way race is Candidate X = .33, Candidate Y = .33, and Candidate Z = .33.
- If Respondent A rated Candidate X and Candidate Y more favorably than they rated Candidate Z, then Respondent A is equally likely choose X or Y but not Z. Thus the probability of Respondent A’s vote going to Candidate X would be .5, to Candidate Y is .5, and the probability of Respondent A’s vote going to Candidate Z is 0.
Even if Respondent A rated all of the candidate’s poorly, if Respondent A was to cast a vote in a rational manner, the vote would go to whomever was rated highest, on a relative scale.
- Respondents are more likely to vote for a candidate with whom with they have at least passing familiarity than for one they don’t recognize.
- We presume, however, that a respondent will choose a candidate unknown to him over one whom the respondent has rated unfavorably.
- Thus, in order of likelihood to get respondents’ votes, here are the scores assigned to each respondent for each of the candidates:
1. Very favorable rating and known to the respondent
2. Somewhat favorable rating and known to the respondent
3. Known to the respondent, but the respondent has no definite opinion either favorable or unfavorable
4. Unknown to the respondent
5. Somewhat unfavorable rating and known to the respondent
6. Very unfavorable rating and known to the respondent
After figuring out which candidate or candidates we thought each subject was going to vote for we tried to control for the most likely voters by looking at party affiliation and how likely each subject self-reported that they would be to vote in the primary. We concluded that the most representative sample of likely voters would be subjects who were affiliated with the given party and who also said they were at least somewhat likely to vote in the primary. This formed a group of 69 Republicans and 138 Democrats from the poll that were predicted to vote in the primary, representing 11.9% and 23.7% respectively of the registered voters from the VPR poll. These numbers are slightly higher than the actual 10.3% and 16.2% turnout in the actual election, but that is to be expected with the polling response bias for citizens interested in politics.
Figure 1 illustrates the percent of the vote each candidate is projected to receive based on the relative favorability ratings; in addition, the chart compares the projected vote against the actual vote received in the respective primary races.
As Figure 1 illustrates, our model did a good job at predicting both parties’ gubernatorial primary elections, with both predictions within the margin of error for the actual results, with the exception of Peter Galbraith’s projected vote total, which was lower than the model projected. In the Republican race our model predicted Scott to win with 64 percent of the vote, very close to the actual 60 percent. The model also predicted that Minter would receive 48 percent of the Democratic vote—very close to the 49 percent she actually received. It is possible—although we lack any empirical evidence—that the model’s over-prediction of Galbraith could be explained by some strategic voting, voters choosing their favorite between the two front runners out of concern that Galbraith could not win.
On the other hand, the model missed predicting the Democratic primary outcome for the Lieutenant Governor’s race, picking Smith instead of Zuckerman as the likely winner. One possible reason for this difference between the model and results could be because of a change in public perception from the time the poll was completed until Election Day. This seems especially possible in this race given the late endorsement from the extremely popular Bernie Sanders who might have changed the minds of some Vermont voters. This difference illustrates the difficulty in predicting election results in advance in low turnout elections, especially when only using favorability rating as a proxy for whom subjects will vote. It is also possible that Progressives—who would not have self-identified as Democrats and who therefore would not be included in the model—crossed over to the Democratic primary to support Zuckerman.
Though our model successfully predicted two out of the three races, it is a respondent-level model, and therefore requires that we have a good estimate for who will vote in the primaries—which of our respondents expressing views will actually show up and cast a ballot. In a higher turnout race, such as the general election, we can estimate that a majority of respondents will follow through and vote. This is not the case with the state primary races, where fewer than 3 in 10 eligible voters cast a ballot.
Consequently, we lack a high-enough level of confidence in this model to predict a future event so we are left to test the model and do as most political scientists do: predict the past.