I recently came across an article in Vice by Sean McElwee which made a very compelling case for Democrats moving to the left on issues like immigration and racial justice to mobilize millennial voters instead of moving to the center on these issues in an attempt to win over white working class voters. This is a move that aligns with my personal politics, but it seems to run completely contrary to a good deal of conventional punditry on how Democrats should go about winning elections. The author made his case by analyzing several questions from the 2016 Cooperative Congressional Election Survey, which is a very large national survey that covers a number of topics related to policy and political attitudes.
In this post, I'll attempt a deeper dive into some of the CCES data using Bayesian Inference with PyMC3. I'm mainly interested in how much the CCES data can tell us about how opinions vary by congressional district, with a particular focus on college educated millennials. The results backup the main thesis of the vice article pretty strongly, so I won't try to rehash much of the commentary. Instead, this post will be more focused on the computational and modeling issues surrounding this type of analysis.Read More