Demographic and Socio-Economic Profiles that Relate to Political Party Affiliation Examined in Massachusetts and Wyoming for the 2016 Presidential Election Group 4: Mason Cheng Eryn Hall Alex Rhode Bailey Boulter

Outline Objective Hypothesis Study Characteristics Methodology Results Analysis Conclusion Degree of Accuracy

Background 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 Clinton Wyoming had their caucus on April 9th Winners: Bernie Sanders and Ted Cruz

Objectives To discover if there is a relationship or correlation between election outcomes (party affiliation) and 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 s, etc.

Hypothesis 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 states

Study Characteristics Choice of States: Massachusetts and Wyoming Wanted two extreme states (one red, one blue) Wyoming was uniformly red and Massachusetts was uniformly blue Geographically different regions different people Choice of Indicators Wanted to test accuracy of social stereotypes of political parties Focused on both social and economical variables that were presented by the most recent census(2010)

Methodology Choose 10-12 indicators that may relate to political party affiliation Sort and compile data from US Census Convert Excel spreadsheets into DBF files On ArcMap, join the DBF tables based on a unique county values Export joined table as a shapefile Manipulated visual components of each individual shapefile to help compare two the two states

Results Population Density: Wyoming Alone

Population Density (cont.)

Age Distribution

Female (percent)

Race/Ethnicity Hispanics

Race/Ethnicity Blacks

Race/Ethnicity Whites

Education (Bachelor’s Degree)

Labor Force

Per Capita Income

Poverty

Disabilities

Health Coverage

Veterans

Foreign Born

Analysis Much of our results were as expected based off of what we knew about voting trends. Ex. 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 Wyoming Not all of our indicators gave us the expected trends, or even any decipherable trends at all Correlation does not equal causation

Conclusion Overall, this study was designed to explore trends in voting and attempt to predict certain outcomes based on these trends. Some trends in voting were substantiated by our demographic data, others were inconclusive, and other interesting patterns were revealed in the plotting of this data. E.g. Poverty, Gender, Foreign Born voters

Conclusion HOW MIGHT CANDIDATES & CAMPAIGN MANAGERS USE THIS INFORMATION? Realistically, in states as “red” and “blue” as Wyoming and Massachusetts, it’s unlikely they would be won by the opposing party. However, during primary season, candidates only run against other members of their party. 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 over

Degree of accuracy Other unmeasureable indicators i.e. Parental influence, religion, gun control, etc. Possibilities of Error due to: Low population density in Wyoming Low voter turnout in Wyoming (about 3% of the total population) Difference in two states (size, etc.) making scales/legends different and difficult to create Older 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