acs_vars <- c(
total_edu_25_plus = "B15003_001",
high_school_diploma = "B15003_017",
GED_or_alt = "B15003_018",
associates = "B15003_021",
bachelors = "B15003_022",
masters = "B15003_023",
professional = "B15003_024",
doctorate = "B15003_025")Education, Ballot Language, and Voter Support in Colorado (2016-2018)
Introduction
This project examines the potential impact of education level on support for a ballot initiative when the wording on the measure changes. In the state of Colorado, Amendment T (2016) failed 50.32% to 49.68%, while a version revised for clarity (Amendment A) passed with 66.21% support in 2018. Analyzing precinct-level election results combined with demographic estimates from the American Community Survey, we explore the question:
How does education influence support for ballot initiatives when the wording of the measure changes?
Amendment T (2016): Shall there be an amendment to the Colorado constitution concerning the removal of the exception to the prohibition of slavery and involuntary servitude when used as punishment for persons duly convicted of a crime?
- Failed 50.32% to 49.68% (2,576,759 total votes)
Amendment A (2018): Shall there be an amendment to the Colorado constitution that prohibits slavery and involuntary servitude as punishment for a crime and thereby prohibits slavery and involuntary servitude in all circumstances?
- Passed 66.21% to 33.79% (2,416,132 total votes)
Support for this amendment increased by ~16 percentage points between 2016 and 2018. Turnout in 2018 was only around six percent lower than in 2016 (midterm election year vs. presidential election year). Notably, Amendment T contained a double negative (ex. “removal of the exception to the prohibition of slavery”), where Amendment A replaced it with straightforward wording (ex. “prohibits slavery”).
Turning to education level as a proxy for reading comprehension, precincts with a lower education level may experience the greatest increase in support once the wording was changed. If differences in education help to explain this shift in support, the implication is that the wording (not the policy content) can determine whether a measure passes or fails.
To assess the relationship between changing support from 2016 to 2018 and education level, precinct boundaries were merged with statewide election results. Tract-level education measures from the American Community Survey were then spatially attached to these merged results, providing a demographic estimate of support and education level by precinct for both years. These datasets were then used to visualize choropleth maps, scatterplots, and develop regression models to estimate the relationship between education and changing support.
Data and Methods
This analysis uses election results downloaded directly from the Colorado Secretary of State, shapefiles for precinct boundaries from the UF Election Lab, and Census data from the American Community Survey.
Precinct-level results were retrieved from the Colorado Secretary of State’s election database for Amendment T (2016) and Amendment A (2018).
Precinct shapefiles were obtained from the UF Election Lab for 2016 and 2018, and are necessary to create maps of each precinct to attach ACS data to.
Five-year education estimates were retrieved from the U.S. Census Bureau ACS using the
{tidycensus}R package for both 2016 and 2018For 2016, the 2012-2016 ACS was used
For 2018, the 2014-2018 ACS was used
- Education variables were later condensed into broader categories (ex. high school or equivalent, associates, bachelors, and masters plus).
Spatial Join
To apply education values from geographically larger Census tracts to smaller precincts, an approximation method is required. This analysis uses a centroid join, where each precinct is assigned the ACS estimate of the tract containing its geometric center. While an area-weighted interpolation is more precise, this approach provides a reasonable estimate that is less computationally intensive. Due to this, education values derived at the precinct level should be interpreted as approximations. Given the 16 percentage point increase in support from 2016 to 2018, if a relationship between education level and ballot language exists, it should be apparent despite the limitations.
Precinct Centroid Join (2016)
# get precinct centroids
precinct_centroids <- merged_clean_2016 |>
st_centroid()
# join each precinct centroid to the tract it falls in
precinct_tract_join <- st_join(
precinct_centroids |> select(Precinct),
acs_2016_wide,
join = st_within
)
# drop geometry to can merge back
precinct_tract_intersect <- st_drop_geometry(precinct_tract_join)
# joining Precinct/Election Data with ACS Data
merged_final_2016 <- merged_clean_2016 |>
left_join(st_drop_geometry(precinct_tract_intersect), by = "Precinct") |>
mutate(ballot_support_rate = Yes / TotalVotes) |>
relocate(ballot_support_rate, .before = 6)- Note: An identical process was repeated for 2018.
Results
The following maps depict the ballot support rate for Amendment T in 2016 (Figure X) and then Amendment A in 2018 (Figure Y), while Figure Z depicts the percentage change in support from 2016 to 2018 for each precinct.
The share of voters who supported the amendment in each precinct.
Precinct Level Support (2016)
Figure A. This map depicts the ballot support rate per precinct for Amendment T, using 2016 precinct boundaries and official precinct election results from the CO Secretary of State. Support widely varies, with the highest support rate occurring in urban areas along the front range such as Denver and Boulder. The highest support rate was in Boulder at ~83%, while the lowest occurred in rural counties with only 13-16% of people voting yes. The amendment failed statewide during this year.
Precinct Level Support (2018)
