Efforts to Defeat Roll-Off

For a project that started out in my Bayesian statistics class and has found its way to my research paper on roll-off, I decided to try running a model to test the effectiveness of the NC Coordinated Campaign's efforts to fight roll-off in light of what happened in the Primary. Democrats won up and down the ballot, so it seems obvious to the casual observer that roll-off wasn't an issue in the General. I used non-informative priors on a regression for the Primary race roll-off (% fewer ballots cast in each contest relative to the Presidential contest) and calculated posterior slopes on some relevant variables. The real variable of interest here was minority registration growth rate since January. I then used the posterior estimates from that regression to put priors on a General election model.

This framework provides a strong analysis of the effectiveness of the Coordinated Campaign. It is also a great example of using Bayesian statistics to do something frequentist methods do not have an effective structure to do - examine a dataset with prior beliefs to investigate changes from the prior. The priors for the model being the posteriors from the Primary provides a more rigorous method of establishing the change in effect than could be established otherwise. In quick summary, it seems as though the Coordinated Campaign was successful, as evidenced by the plot below on the Governor's race.

In terms of interpreting this picture, the red distribution is the posterior estimate for the primary. This means that in the primary, in the average county, for every ten newly registered minority voters, three of those votes had disappeared by the time they got to the Governor's race. Now is the point where the savvy reader might interject, "But, Will, roll-off is always less in the General race, so this is not a huge concern." While savvy, that reader is failing to realize that these are slope estimates. With this model we are explaining the variance that exists geographically in roll-off. There is always variance around any mean - no matter what the mean is. Our interest is in what correlates with this variance. 

This brings us to the blue density. This line is what the data in the General election do to the prior. For those unfamiliar with Bayesian analysis, I'll butcher it and say - Primary(Red Line) Slope Estimate + General Election Data = Blue Estimate of General Election Slope. As is readily visible, this parameter is zero for all intents and purposes. It also has a lot of certainty associated with it. Whereas there was a lot of spread on the Primary estimate, there is none here.

In sum my conclusion is that the Coordinated Campaign did an excellent job stamping out the problem of newly registered minority voters falling off the ballot. Well done, Keith, if you're reading this.