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.

Roll-Off, Straight Ticket Voting, and African Americans

So I have now finished playing with my data, and have decided I can put myself out there and place some confidence in the following findings. First, I've got a couple of definitions for reference:

AATOcomp – proportion of turnout in a county which is registered as African American

STVrate – proportion of ballots from a county which used the straight-ticket voting option

Roll-off – proportion of decreased cast ballots in a particular race as compared to the Presidential contest

I've distilled my thoughts into five key observations. These will form the basis of the paper that I'm working on with Professor Aldrich. Please feel free to respond with any thoughts in the comments section. These are just the data findings - what I feel like the numbers tell me - not publishable research. I'm working on developing a political theoretical framework on how they all make sense together. I don't expect to come up with a single unifying theory, but I am going to try to make as much sense of it all as I can.

Overall

1. There is a significant positive correlation between AATOcomp and STVrate. This causes issues in examining the effect of AATOcomp on roll-off due to the necessary relationship between STVrate and roll-off.

On Partisan Races

2. Without controlling for STVrate, higher levels of AATOcomp are associated with higher amounts of roll-off in top ticket races but this effect reverses as we go down the ticket and there is more overall roll-off. The effect is significant but weak at the top of the ticket, but becomes much stronger down the ballot (presumably as a result of a higher proportion of the remaining votes coming from STV). 

3. By subtracting STV vote totals from the overall totals, we can examine roll-off among non-STV ballots.* Using this method, we find that the same positive association between AATOcomp and roll-off exists still in the upper ticket. Continuing to control for STVrate, as we progress down the ticket, we see that there becomes no association at all between AATOcomp and roll-off – with one exception. After the judicial contests, there is the Soil and Water Conservation District Supervisor. In this race there is a very strong positive exponential effect between AATOcomp and roll-off (controlled for STV).

On Judicial Races

4. Surprisingly, there is only a weak positive correlation between judicial roll-off and STVrate. All correlations are positive with bivariate regression slope frequentist p-values < .15 but all are > .01 Controlling for the effects of multiple tests, it is difficult to assert this as a strong effect.

5. In the judicial races, there is no correlation between AATOcomp and roll-off. This makes sense when considered in conjunction with the facts that (1) STVrate has a minimal effect on roll-off in the nonpartisan races (see Observation 4) and that (2) this is a continuation of the trend controlled for STVrate as identified in Observation 3.

*It is important to remember here that we can only study the behavior in the aggregate. We can use this method to understand the voting behavior of the county, but not individuals.