[1] The methods we used were described here.

[2] Hillary Clinton won the 2016 popular vote by 2.1 points. The Democratic margin for 2018 was 8.6 points, but there were many uncontested races that Democrats won 100–0, skewing comparisons between years. We project election results for uncontested races to make results between years more comparable; the “projected” margin for Democrats was 7.3 points. See here for more detail.

[3] We have worked on other research projects showing that the lifelong preferences of white voters are most strongly formed around the ages of 14–24, and informed by the popularity of the President during those formative years. The youngest white voters were simply not alive or paying much attention during the popular Democratic years of Bill Clinton, and the unpopular years of George W Bush. For more information, see here, here, and here.

[4] This section is built on joint research that we conducted with People’s Action.

[5] The maps show census tracts, using precinct-level election results where available, and data projected from individual-level voter registration data and district-level Congressional results elsewhere.

[6] These groups are not measured perfectly, even on a national voter database that is maintained consistently over time. The problem is that matching, also known as record linkage, is an imperfect process that has false positives and false negatives. In other words, when somebody moves or changes their registration information in other ways, a statistical system has to link the changes to earlier records in the database. This has uncertainty and errors associated with it, so somebody who we think is a “new voter” may in fact have been an existing voter and our systems can’t recognize that they are in fact the same person. See here for more discussion and details of one open-source matching system.

The implications of this matching uncertainty on various statistics of interest are important and beyond the scope of this post. For this section, the important point is that the same systems are used over time, so comparisons over time should not be biased. The unit of analysis is a person/state combination.

[7] After projecting uncontested races.

[8] In this analysis, we intentionally assign as much weight as possible to turnout, because we think it is valuable to show that vote choice still matters even when computed as a lower bound. Arguably true changes in vote choice are captured here as part of the “turnout” number in a variety of ways: (a) people who dropped off from 2012 to 2014 may have also switched towards Republicans had they decided to vote; (b) new 2014 voters may have been motivated to vote this time because they supported Republicans more strongly; © mismatches who actually voted both times but appear to be both dropoff and new voters (as different records) fall under the “turnout” column in this analysis; (d) relatedly, people who moved from one state to another between elections are counted as “new voters” because the Catalist database is keyed on person/state as the unique identifier. Some people also argue that vote choice and turnout are always jointly determined, so in some sense this division is nonsensical; even so, it is a practical question that informs many campaign decisions, so we continue to analyze it here.

[9] Some of the vote switching was to/from third party, but we repeated the analysis in two ways: removing third party voters and allocating their votes. There are no changes to the conclusions in either case.

[10] Congressional races are sized smaller than statewide races to keep things fairly visible.

[11] Statewide elections where we had more than 150 survey responses are shown. All CDs are shown, except those in Pennsylvania which had different district lines in 2016.

[12] The x-axis is bounded at 0% and 100%. Elections that fall outside of these bounds are places where turnout and vote choice went in opposite directions. For example, the Nevada Governor’s race had a small Democratic margin gain overall, but the turnout was slightly worse for Democrats than in 2016; the vote choice component therefore accounts for > 100% of the total gain, and is shown as 100% in the graph.

[13] This analysis includes vote history information for all states except Alabama, Kentucky, and New Hampshire.