A student’s view of Vermont’s Opiate Addiction

The opiate addiction issue is no longer someone else’s issue. This past July Vermont Public Radio came out with a survey, conducted by the Castleton Polling Institute, that had a broad spectrum of questions related to issues in Vermont. Opiate addiction was, of course, on that list.

Eighty-nine percent of respondents in the poll said that opiate addiction is a “major problem” in Vermont. No one responded that opiate addiction was not a problem for our state. Opiate addiction in such a small state came as a shock to many, and was even featured in Rolling Stone magazine alongside an image of a Vermonter doing heroin on a maple syrup bottle.

Robertson Graphic
Figure 1. Percent of Vermonter’s who see Opiate Addiction as a major problem

This past 2016 State of the State address focused largely on the opiate epidemic. Governor Shumlin spoke about daily drug related violence, and uncared for children due to drug addicted parents. He then discussed his plans to further battle the Vermont opiate addiction.

When respondents were asked if they or someone they know have been personally affected by opiate addiction, the state was almost evenly split, with those responding “yes” (53 percent) in a slim majority.

Of those who said yes, to knowing someone affected by opiate addiction, 94 percent said that they “personally” know someone who has struggled with opiate addiction. This shows that this issue reaches much farther than just the respondents.

Despite the high numbers of those who know someone struggling with addiction there is a light that is shining over the sad news. Groups throughout the state are working throughout communities to decrease the use of opiates, as well as other drugs. In Rutland County there is an organization called Project Vision. Project Vision is a leading example of organizations that are getting the community actively involved to fight drug addiction and build the community. It also has local law enforcement actively involved, which creates a positive relationship between them and the communities they serve. Although opiate addiction is an issue it is definitely being fought by different groups of people throughout Vermont.

A students view on refugee resettlement

Support for Refugees
Figure 1. Support for resettling refugees in your community, by Party.

In a recent Vermont Public Radio poll conducted by the Castleton Polling Institute, 54 percent of Republicans (a slim majority) said that they would oppose an effort to resettle refugees in their community, whereas nearly 80 percent of Democrats voiced support for the resettling of refugees in their community.

Consistent with the partisan split in the level of support, only 20 percent of Democrats, contrasted with 60 percent of Republicans, felt as though refugee resettlement would have a negative impact on Vermont. Independents were split in their opinions, with just over 33 percent believing resettlement to have a positive impact and just under 36 percent believing it to have a negative impact.

Of the 637 complete interviews only 8 respondents cited religion as the likely source of a negative impact. For those who see resettlement as a negative, the reasoning most frequently given is the cost of domestic aid and the over taxing of local resources. Those who see refugee resettlement in a positive light are generally more united on the topic; the most frequent opinion shared was that it would make Vermont a more culturally diverse area, and a better place to live.

President Obama has promised asylum in the United States to 10,000 refugees, so far Vermont has been promised 100. Although only a fraction of the whole, a small homogeneous state like Vermont is easily affected. Vermont is.23 of a percent of the total population, yet they are accepting one percent of refugees. For the country as a whole, 10,000 is a small splash in the ocean, but Vermont is only a small pond and 100 people can make a big splash. No one knows what will happen in the coming months, but as Rutland County opens its arms to refugees the impact will become clearer.

Favorability and Vote Choice

(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.

Additional presumptions:

  • 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.


Figure 1. Projected vote (with error bars) based on relative candidate favorabiilty ratings, compared with actual vote totals


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.

Campaigns Matter, Even When Most Voters Are Not Engaged

The VPR Poll in July 2016 asked Vermonters about the candidates. Respondents were asked if they have heard of each candidate for governor or lieutenant governor; for each candidate that a respondent has heard of, the respondents were asked if their opinion of that candidate was favorable or unfavorable.

The data from these two questions allowed us to assess how well a candidate is known and whether those who know the candidate have a favorable or unfavorable opinion (or no opinion at all). This is what a campaign is all about: to introduce or reintroduce one’s candidate to the voters and to create a favorable image for that candidate among those voters. The successful campaigns approach the election with a large percentage of the public holding favorable views of their candidates. As the Vermont state primary approaches, the candidate with the greatest level of name recognition is current Lieutenant Governor and gubernatorial candidate Phil Scott. Of the 86 percent of Vermont adults who recognize Scott, 58 percent hold a favorable view of him, while only 13 percent hold an unfavorable view—giving Scott a net favorability score of 45. (Net favorability is percentage of respondents with an unfavorable opinion of the candidate subtracted from the percentage of respondents with a favorable opinion; those with no opinion are not included in the calculation.) The only gubernatorial candidate with a higher net favorability score—higher by a mere and insignificant 1 point—was Sue Minter; however, only 63 percent of Vermont adults have heard of Minter.

The following graph shows the relative awareness and net favorability for all of the candidates for governor and lieutenant governor.

Figure 1. Candidates’ Name Recognition and Net Favorability Ratings, July 2015

Of course, the job of a campaign is to improve the level of public awareness and public approval for one’s candidate. In September 2015, the Castleton Poll asked Vermonters about a number of candidate who were potentially running for governor. The following table shows the changes from fall of 2015 to July 2016.

Table 1. Changes in Name Recognition and Favorability from September 2015 to July 2016

The campaign of Bruce Lisman made the most traction in getting the candidate’s name recognized by potential voters, going from having only 21 percent knowing who he is in September to 61 percent this July. Unfortunately, being known as a candidate takes a hit on one’s favorability ratings, as LIsman’s net favorability dropped from 13 to 3.  This is what hit Phil Scott, who had the biggest drop in net favorability from September 2015 to July 2016. Of course, Scott had such high favorables it was inevitable that, as a candidate, those numbers would come down.

Randy Brock, a former gubernatorial candidate, has lost ground running for lieutenant governor in both awareness and favorability.

Sue Minter has made the greatest gains in favorability, picking up a net 20 points and increasing her awareness by 25 percentage points. While she is, in July 2016, a little less known than her primary opponent Matt Dunne, her net favorability is comparably higher. This sets up a potentially close race for the Democratic nomination. The victor will likely be the one who mobilizes supporters best with the better get-out-the-vote effort.