Which State Will Push Clinton or Trump Over the Top?
Prognosticators scheming out electoral vote scenarios for November eventually arrive at one question: Which state will push Hillary Clinton or Donald Trump past the 270 votes needed to win the White House?
At first glance, this particular question might seem unimportant. There are a number of other more pressing ones in elections analysis: Who will win the presidency? Which party will control the Senate? And which party is set up for success in 2018 and 2020? But figuring out which state is the tipping point -- the one that, when ordered by win margin for the victor, pushes the triumphant candidate across the threshold to secure the presidency -- helps answer other questions. Among them are how much “padding” Clinton might have in the Electoral College, which state will be most important if the race tightens and which voters may end up becoming most valuable to both campaigns.
Other analysts, including Dave Wasserman, Josh Katz and the FiveThirtyEight team, have examined this issue and/or made models that do a great job of addressing it. But my approach differs slightly from theirs.
I retooled the visuals on the RCP Demographic Calculator. Users can adjust the turnout and partisan vote share for various demographic groups, and the calculator estimates state-by-state results and finds the tipping-point state. The starting values are 2012 election results applied to 2016 demographic estimates. The win margin for each candidate is displayed in the bar graph, and the tipping-point state is bolded.
This approach doesn’t use polls and it has a number of assumptions and limitations built in (see the discussion below), but it does let readers get a feel for which state might tip the balance in different scenarios. So play with it for a bit, see what you come up with and scroll past it for a brief discussion of the results:
This interactive -- like all others -- comes with some caveats (skip the next four paragraphs if you’re not interested in the details). First, it uses uniform swing -- that is, if a user increases Clinton’s vote share with whites by 1 percent, the interactive tries to distribute that gain proportionally across the states. This is a transparent, principled and reasonably safe way to get state-by-state estimates, but it may cause some swing states (where advertising and ground efforts are concentrated) to shift too slowly.
Second, race isn’t the only important demographic. Income, education, gender and other voter characteristics will likely play a role, but it’s impossible (and unwieldy) to fit every relevant factor into one interactive. So it might be reasonable to mentally increase Clinton’s vote share in states like Virginia, where there are many white college-educated voters, and Trump’s in states like Iowa, where there are many non-college educated whites.
Third, the interactive doesn’t take third party candidates into account.
Fourth, all of this data is demographic -- the interactive doesn’t use any polling. That means that it fails to pick up home-state advantages and some of the subtleties of each candidate’s appeal (e.g. Trump’s difficulties with Mormons in Utah). That’s because this isn’t a predictive model; instead, it is designed to give estimates based solely on the movement of a few demographic groups.
A few notable trends emerged while running various scenarios:
Pennsylvania was the tipping point in many of them, both when racial polarization was at a more normal level (Clinton performs worse than Obama with African-Americans but better with whites) and when it was more extreme (Trump outperforms Mitt Romney with whites but nose-dives with Hispanics). This bolsters Wasserman’s case from earlier this year that Pennsylvania might be the tipping point.
Florida, Wisconsin, Iowa, New Hampshire and Pennsylvania were the tipping point state (or close to it) in many of the more racially polarized scenarios. Many of these states make sense -- if Trump were to win by running up the score with white voters, some Rust Belt and Midwestern states might fall into his column and push him over 270. New Hampshire, also a highly white state with a number of persuadable voters, could swing to either side in such a scenario. Florida was also a tipping point for some inputs, which agrees with results from the predictive models at FiveThirtyEight and The Upshot.
These results also underscore how small leads in the popular vote can lead to lopsided wins in the Electoral College. To see this, design a scenario where either candidate has a two- or three-point lead and check the margins for states under the tipping point. In most cases, the winning candidate will take a number of swing states by relatively small margins, giving him or her big returns in the Electoral College for a comparatively small popular vote advantage.
These are just a few initial observations on a small sliver of the range of possible inputs, so if you find anything interesting, please screen-shot it and either email me or tweet it at me. The beauty of interactives is that a thousand readers can find more interesting results than the author can by himself.