Party Switching and Primary Election Participation

A look at the relationship between party-switching and primary election participation in North Carolina from 2012-2018.

Project Description: This project uses North Carolina’s voter registration and voter history files as well as voter snapshot files from the May 8th 2012 presidential primary and May 6th 2014 primary elections. Combined, these files allowed me to look at party switching behavior among voters in between the May 2012 and May 2014 primary elections, and then pair that data with voter’s participation in subsequent primary elections. This topic was interesting to me because of it’s potential to expose any trends or correlations in the voter behaviors of party-switching and participation in primary elections. Furthermore, the project aims to explore which age groups or party affiliations, if any, are more or less inclined to engage in party-switching. These findings could have implications regarding public interest in primary elections and the candidate selection process, strategic voting practices, or public political volatility.

Some Notes/Limitations: In the process of my analysis, I ran in to several memory & processing issues. I used resources I had available to limit the effect that these issues had on my project, but some decisions were made with this in mind. For example, importing older files (2012 and 2014) seemed to allow for more efficiency than more recent snapshot files (perhaps due to size).

Literature

Past literature seems to focus more on the behavior of party-switching as an indication of partisan politics. There is not much to strongly indicate causes for party-switching. Still, topics discussed in past literature are relevant to an analysis on party-switching and participation.

Smidt (2017): This article by Corwin D. Smidt of Michigan State University looks at how partisans vote (it focuses less on official party-switching and more on whether the vote casted aligns with party-affiliation). Smidt concludes that Americans currently exhibit the highest observed rates of party allegiance when voting across successive presidential elections and attributes this to clear and easy recognition of party-differences. With clearly polarized choices, American’s battle little conflict or concern in picking a side and sticking to it. This could point away from the possibility that those who do switch parties usually do so because of genuine feelings of alignment with the opposing party’s values. https://www.jstor.org/stable/26384737

Bock & Schnabel (2021): This short analysis by Harvard and Cornell University students identifies shifts in party-affiliation from 2016-2020. It discusses trends in self-reported partisan identification, and concludes that despite a downward trend in voter’s engaging in party-switching overall, there is still a significant minority who do realign. https://journals.sagepub.com/doi/epub/10.1177/23780231211057322

Hypothesis

H1: I anticipate finding a positive correlation between party-switching and participation in subsequent primary elections among North Carolina voters.

H2: I expect to find higher rates of party-switching among the younger population.

H3: I expect to find that voters whose original party affiliation is independent/unaffiliated engage in higher-rates of party-switching than those who originally identify as Republican or Democrat.

Project Outline

1. Creating Data Set

Summary: I created my final data set by joining (using an inner join) the May 2012 and May 2014 Voter Snapshot files using the voter registration number as a common identifier. This gave me a data set with voter’s party affiliations in 2012 and 2014. I then used the mutate() function to add a column that would indicate whether there was a party switch within that 2 year period. At this point, I joined my data with the North Carolina Voter History file -this would allow me to see participation in primary elections over the past ten years using the filter() function for primary elections only. I chose to further filter the data for only the following two primary elections (March 2016 and May 2018) for relevancy. I then created a new column for the March 2016 and May 2018 elections with a true/false value to indicate if the voter participated in each election.

Note: Pre-processing each data set included grouping the data by voter registration number and using the distinct() function to get rid of duplicate entries caused through joining the may files and then again with the voter history files.

mayjoin <- inner_join(may2010, may2014, by = "voter_reg_num")

mayjoin <- mayjoin %>% mutate(party_changes = ifelse(party_cd.x == party_cd.y, 1, 2))

Here I loaded in voter history files

finaldata <- inner_join(mayjoin, swhist, by = "voter_reg_num")

finaldata <- filter(finaldata, grepl("primary", election_desc, ignore.case = TRUE))

mar2016 <- "03/15/2016 PRIMARY"
finaldata <- finaldata %>% group_by(voter_reg_num) %>% mutate(mar2016 = any(election_desc == may2014))

may2018 <- "05/08/2018 PRIMARY"
finaldatadata <- finaldata %>% group_by(voter_reg_num) %>% mutate(may2018 = any(election_desc == may2018))

I created subsets for those who switched parties and those who did not. This made it easier when I started to create visuals for the data.

noswitchdata <- subset(finaldata, party_changes == 1) %>% group_by(voter_reg_num) 

switchdata <- subset(finaldata, party_changes == 2) %>% group_by(voter_reg_num)

I used the following code to count voters who participated in each election based on whether they had switched party affiliation or not. I did this to identify potential trends before graphing data. Note: I repeated this for both subsets of data and for both elections (4 total).

Findings:

March 2016 participation among those who did NOT switch parties between 2012 and 2014: 93.5%

## Did not participate: 34116 
##  Did participate: 491390

March 2016 Participation among those who switched parties between 2012 and 2014: 92% 

## Did not participate: 4460 
##  Did participate: 52149

May 2018 participation among those who did NOT switch parties: 56.5% 

## Did not participate: 228773 
##  Did participate: 296733

May 2018 participation among those who switched parties: 49.7% 

## Did not participate: 28928 
##  Did participate: 27681

Summary: In the 2016 presidential primary, there seems to be no statistically significant difference in participation rates among those who switched parties in-between 2012 and 2014 and those who did not (there is a 1% difference). In the 2018 primary, however, 56% of voters who did NOT switch parties in between 2012 and 2014 participated in the 2018 primary compared to the 49% of those who did switch parties. With the numbers alone, it is evident that those who did not engage in party switching participated in both primary elections at higher rates than those who did switch parties. In neither election is there a significant difference in participation rate among party-switchers and non party-switchers, but the 2018 data actually invalidates my original hypothesis. If anything, it is suggestive that strong-partisan voters participate in primary elections at higher rates.

2. Visualizing Data

Note: With no statistically significant differences in data, it was hard to decide appropriate visualization methods. Below, I used simple bar-graphs to show percentages of voters who did and did not participate in each election based on the party-switching behavior.

This code creates a bar graph with the subsetted data for those who did not switch party-affiliation to display their participation in the 2016 primary. I used geom_text() to add percentage values to each x-axis variable, labs() for the title and axis labels, and scale_y_continuous() to change the y-axis from scientific notation to exact numbers for clear visualization.

ggplot(noswitchdata, aes(x = mar2016, fill = mar2016)) 
  + geom_bar(color = "black") 
  + geom_text(stat = "count", aes(label = paste0(round((..count..)/sum(..count..)*100, 1), "%")), position = position_stack(vjust = 0.5), size = 3) 
  + labs(title = "March 2016 Presidential Primary Participation Among non-Party-Switchers", subtitle = "Voters who did not switch party-affiliation in-between the previous two primary elections", x = "Participation", y = "Count", fill = "Key")
  + theme_minimal()
  + scale_y_continuous(labels = scales::number_format())

Note: Here I will provide code for each subset, but not for each election (to avoid including almost the same code 4x)

ggplot(switchdata, aes(x = mar2016, fill = mar2016)) 
  + geom_bar(color = "black") 
  + geom_text(stat = "count", aes(label = paste0(round((..count..)/sum(..count..)*100, 1), "%")), position = position_stack(vjust = 0.5), size = 3) 
  + labs(title = "March 2016 Presidential Primary Participation Among Party-Switchers", subtitle = "Voters who switched party-affiliation in-between the previous two primary elections", x = "Participation", y = "Count", fill = "Key") 
  + theme_minimal() 
  + scale_y_continuous(labels = scales::number_format())

Resulting Graphs

In the graphs below, “true” represents voters who did participate, and “false” represents those who did not.

1. March 2016, No Party-Switch

2. March 2016, Party-Switch

3. May 2018, No Party-Switch

4. May 2018, Party-Switch

3. Conclusion/Where to Next?

Conclusion: The data and graphs above display little to no correlation between party-switching and primary election participation among North Carolina voters in between 2012 and 2018. My original hypothesis that there would be a positive correlation between the party-switching behavior and participation in subsequent primary elections seems to have been proven wrong thus far. In 2016, there was little difference in participation rates among those who swapped parties and those who did not. In 2018, there was an approximate 6.5% difference in participation rates between those who swapped parties and those who did not, but that additional 6.5% was held by the voters who did not engage in party-switching, suggesting higher rates of political engagement overall among stronger partisans. It is worth noting the significant difference in participation rates overall in the 2016 presidential primary election compared to the 2018 primaries. This could potentially be attributed voters higher rates of political engagement during a presidential election year.

Continuation: Up until this point I have only tested my original hypothesis. I am still interested in looking into potential relationships between certain voter demographics, like age or race, and party-switching to identify if any particular demographic is more/less likely to engage in party-switching. Originally, I expected to find a positive correlation between party-switching and primary election participation, and then use trends in voter-demographics to gain further insight in to potential explanations for why people switch-parties. Instead, I may shift my focus away from election participation in general, and zero in on identifying which demographics, if any, engage in party-switching at higher rates. Additionally, I am interested to see if there are any trends in original party ID and party-switching.