class: center, middle, inverse, title-slide # What if everyone voted? ## And what the answer tells us about voter suppression ### <strong>G. Elliott Morris</strong> <br />Data journalist <br /><em>The Economist</em> ### September 30, 2019 --- class: center, inverse # # # What is a "data journalist"? # # --- # What is a "data journalist"? A "data journalist" is just like a "regular" journalist who relies on their own skills in empiricism to tell a story. ## Process: -- ### 1. Find a story -- ### 2. Find a data-driven angle in said story -- ### 3. Analyze data with statistics programs (Excel, STATA, Python, R) -- ### 4. Convey information (with words and graphics) --- class: center, inverse # # # What if everyone voted? # # --- # Guiding questions -- ## 1. How many Democrats and Republicans are there? Given data constraints, we're really asking: How many Clinton and Trump voters are there? -- ### 2. How are they distributed geographically? The answer lets us assign Electoral College votes. --- # Data -- ## 1. Cooperative Congressional Election Study (CCES): A survey of 64,000 Americans Includes demographic data and 2016 vote choice for 40,000+ validated voters -- ## 2. American Community Survey (ACS): A Census Bureau survey of 175,000 Americans Includes the same demographic data as the CCES 32,640 “cells” --- # Method -- ## 1. Train a predictive model on CCES data - Multi-level logistic regression - Predict vote choice with: age, gender, race, education, region and interactions between them -- ## 2. Use the model to predict voting habits for every eligible American Via “post-stratification” on the ACS --- # ACS Post-stratification -- ### 1. Each "type" of person gets their own "cell": - One cell for white men ages 18-30 without college degrees who live in the Northeast - Another for white men ages 18-30 without college degrees who live in the South - Another for non-white men ages 18-30 without college degrees who live in the Northeast - etc. -- ### 2. We know how many voters in that "cell" live in each state -- ### 3. So we can say that x and y% of each "cell" vote for Clinton or Trump, then add up - For example, a Latino female age 18-30 with a college degree in Texas is 85% likely to vote for a Democrat for president (White man 65+ is 80% Republican) --- # Results <img src="figures/states_demography.png" width="3968" /> --- # Results <img src="figures/votes_bystate.png" width="4056" /> --- # Results: If everyone voted <img src="figures/everyone_votes.png" width="80%" /> --- class: center, inverse # # # What does this tell us about voter suppression? # # --- # Voter suppression -- ### - We can modify the percentage of each group that turns out to vote, then re-predict the election - What if only all whites vote? - All non-whites? - Whites without degrees? Etc. -- ### - Democrats do better when non-whites turnout; Republicans have a vested interest in keeping turnout rates low - Especially in southern states with large minority populations - Their efforts to move voting locations off-campus—TX almost removed the FAC as a precinct after 2018—also have political consequences --- # Suppression of white votes <img src="figures/nonwhites_vote.png" width="80%" /> --- # Suppression of non-white votes <img src="figures/whites_vote.png" width="80%" /> --- # Considerations ## What this doesn’t tell us: - That Clinton/Trump/Abrams/etc would have won if certain x, y or z restrictions had been put in place - Downstream effects (AKA party positions and coalition changes) ## The balancing act: - There are a ton of white, non-college educated voters in the Midwest that tilt national scales if we increase turnout - Especially because increases in turnout are not uniform - And because of their geographic distribution, small relative increases in white turnout can tip the Electoral College to Republicans (see: 2016) - But on the other hand, some organizations are explicitly targeting non-whites and young voters for turnout purposes --- class: center # Thank you! ## G. Elliott Morris ### Data journalist, _The Economist_ **Email: [elliott@thecrosstab.com](mailto:elliott@thecrosstab.com)** **Twitter: [@gelliottmorris](http://www.twitter.com/gelliottmorris)** <br> --- _These slides were made with the `xaringan` package for R from Yihui Xie. They are available online at https://www.thecrosstab.com/slides/2019-09-30-utaustin/_