Presentations text content in Demographic and Socio-Economic Profiles that Relate to Poli
Demographic and Socio-Economic Profiles that Relate to Political Party Affiliation
Examined in Massachusetts and Wyoming for the 2016 Presidential Election
Group 4:Mason ChengEryn HallAlex RhodeBailey BoulterSlide2
ObjectiveHypothesisStudy CharacteristicsMethodologyResultsAnalysisConclusionDegree of AccuracySlide3
The 2016 Presidential election consist of 4 main competitors 2 Main Democratic candidates: Hillary Clinton & Bernie Sanders 2 Main Republican candidates: Donald Trump & Ted Cruz Series of primaries and caucuses take place between February 1st and June 14 2016. Massachusetts had their primaries on March 1st Winners: Donald Trump & Hillary ClintonWyoming had their caucus on April 9th Winners: Bernie Sanders and Ted CruzSlide4
discover if there is a
relationship or correlation
outcomes (party affiliation)
demographic and socio-economical variables
Looking at two extreme states (one red, one blue)
Variables: population density, civilian labor force, veterans, education, ethnicity/race, sex, foreign-born, health insurance, disability, age, per-capita income, and poverty
To see if social stereotypes of political party affiliation attributes hold true
To develop experience of gathering data from reputable sources, and then modifying it for geo-spatial analysis
There will be a correlation between the 2016 presidential election results and the selected demographic and socio-economic factors
There will also be a significant difference in measured indicator variables between the two extreme statesSlide6
Choice of States: Massachusetts and Wyoming
anted two extreme states (one red, one blue)
Wyoming was uniformly red and Massachusetts was uniformly blue
Geographically different regions different
Choice of Indicators
Wanted to test accuracy of social stereotypes of political parties
Focused on both social
economical variables that were presented by the most recent census(2010)Slide7
Choose 10-12 indicators that may relateto political party affiliationSort and compile data from US CensusConvert Excel spreadsheets into DBF files On ArcMap, join the DBF tables based on a unique county valuesExport joined table as a shapefile Manipulated visual components of each individual shapefile to help compare two the two statesSlide8
Population Density:Wyoming Alone
Population Density (cont.)Slide10
Education (Bachelor’s Degree)Slide16
Per Capita IncomeSlide18
Much of our results were as expected based off of what we knew about voting trends.
Education, Age Distribution, Non-Hispanic Whites, Foreign Born
However, there were some surprises
Health insurance coverage was high in areas with high poverty—Medicaid?
Relatively high Hispanic populations in
Not all of our indicators gave us the expected trends, or even any decipherable trends at all
Correlation does not equal causation
Overall, this study was designed to explore trends in voting and attempt to predict certain outcomes based on these trends.
trends in voting were substantiated by our demographic data, others were inconclusive, and other interesting patterns were revealed in the plotting of this
E.g. Poverty, Gender, Foreign Born votersSlide25
HOW MIGHT CANDIDATES & CAMPAIGN MANAGERS USE THIS INFORMATION?
Realistically, in states as “red” and “blue” as Wyoming and Massachusetts,
unlikely they would be won by the opposing party.
However, during primary season, candidates only run against other
By knowing what demographic groups prevail in certain counties, candidates can strategize their campaigning efforts within a state to areas they are confident they may have a better chance of gaining support in.
Donald Trump and adults without bachelor’ degrees
Hillary Clinton and adults ages 65 and
Degree of accuracy
Other unmeasureable indicatorsi.e. Parental influence, religion, gun control, etc.Possibilities of Error due to:Low population density in WyomingLow voter turnout in Wyoming (about 3% of the total population)Difference in two states (size, etc.) making scales/legends different and difficult to createOlder census data (mostly from 2010) Low number of indicators used No real statistical math was used to determine if an indicator was statistically significant Correlation not causation