A little over two weeks ago, when the Kavanaugh effect/dems are blowing it panic became the obsession of the political punditry, I came across a comment by Scamperdo on an election related diary here at DKos. In pushing back on the Dems in disarray theme, Scamperdo quoted a tweet by Dr. Rachel Bitecofer. I was intrigued.
Over the next few days I read over the work of Dr. Bitecofer, Assistant Director of the Wason Center for Public Policy and Lecturer in Government at Christopher Newport University in Newport News, VA. In a Wason Center blog post titled, Signs, Signs, Everywhere Are Signs: Why Democrats Will Win Big in the 2018 Midterms, Dr. Bitecofer lays out her theory that in an era of political polarization and partisan identity, the makeup of the electorate in midterm elections is determined by negative partisanship and not independents switching sides. (Bold type mine.)
The way we understand the electorate needs to be reexamined for the polarized era. The traditional view sees the electorate as an ocean that flows from left to right depending on the movement of Independent voters from Republican to Democratic party candidates, which is largely predicated on major factors such as how the economy is performing and whether there are any large, salient issues moving voters toward one party or the other. Take the aforementioned midterm effect, for example. The midterm effect is the longstanding tradition of the president’s party losing seats in the subsequent congressional elections two years later, midway through the president’s term. The midterm effect is really a referendum effect and it supposedly measures the amount of “buyer’s remorse” the electorate, particularly Independents, have after the preceding cycle’s presidential election. This may well have been the case in earlier decades, when partisans were more ideologically heterogeneous, Independents fewer in number, and “Reagan Democrats” still roamed the Earth. But my preliminary analysis of voter files indicates that the modern midterm effect may be misunderstood. The data suggests that the rise and fall of the incumbent party’s fortunes may not be driven by the movement of Independent voters from one party to the other, but instead, by the entrance (and exit) of partisan voters who are activated or deactivated by negative partisanship.
While Independents can be an important factor in determining an election outcome, the win is built upon partisan mobilization. Dr. Bitecofer points out that Barack Obama lost Independents in his 2012 successful re-election bid. But, since Democrats have a population advantage over Republicans, as long as Obama didn’t lose Independents by a wide enough margin, he had the numbers to win with his mobilized base.
Dr. Bitecofer illustrates her theory by using the example of the Republican midterm wave elections in 2010 and 2014. Complacent Democrats weren’t energized while Republicans, fearful and angry over democratic policies and newly appointed judges, showed up in force. Fear and anger are stronger motivators than satisfaction and contentment.
Dr. Bitecofer then goes over how she tested her theory by applying it beforehand to the 2017 Virginia state general election.
This updated theory of electoral behavior led to my successful prediction of the Blue Wave in the 2017 elections in Virginia (at the 20 minute and 32 minute marks). All told, we ran 5 surveys on the gubernatorial race between Democrat Ralph Northam and Republican Ed Gillespie over the course of the general election and they were remarkably stable, predicting that Northam would win the election handily. This worried my colleague, who had spent the past decade making a close study of the Virginia electorate because the elections in 2013 and 2014 had turned out to be far more competitive than expected. Indeed, this was a reason the national punditry herded around a close and competitive election the final week heading into Election Day. But by applying my theory of negative partisanship’s electoral effects in the polarized era, I suspected that Ralph Northam’s victory was cemented on November 9th, 2016 when Donald Trump won the presidency. Trump’s victory created a different Virginia electorate from the electorates of 2010, 2013, and 2014. Because Democrats lost the 2016 presidential election, especially considering the way they lost it and to whom, I expected a turnout surge among the Democratic portion of the electorate and this is exactly what happened. Despite predictions of a close race by other pundits, Northam ended up winning by 9%. And he did it by a surge in Democratic Party participation, not by winning over Virginia’s right-leaning Independents. In 2013, 37% of the electorate were Democrats and in 2017 that percent increased to 41%, which is enough to turn a average 2-3 point advantage for statewide Democrats into a 9 point rout that also allowed Democrats to flip 15 House of Delegate seats when even the most ambitious predictions, including my own, predicted a gain of just 7 or 8 seats due to gerrymandering.
For increased enthusiasm due to negative partisanship on the part of democrats to result in actual wins at the ballot box, there needs to be a potential pool of democratic voters that can be mobilized in any given legislative district or state, than can out vote republicans. Dr. Bitecofer cites the special election in PA-18, where Conor Lamb beat Rick Saccone to flip the seat from red to blue. While Lamb was well funded, ran a great campaign, and gained the support of Independents, it was the massive turnout of Democrats in the Pittsburgh suburbs that his victory was built on. Democrats made up 46% of the electorate, compared to 41% of Republicans, in a R +11 district.
Dr. Bitecofer researched the factors that would have predicted which VA state legislative seats flipped in the 2017 election.
As expected, the level of college education, racial diversity, and urbanization in each district, as well as the district’s PVI are statistically significant predictors of Democrats’ share of the two-party vote. Interestingly, the presence of a Republican incumbent has no influence on Democrats’ share of the two-party vote.
The more republican the PVI (partisan voter index, a measure of how partisan a district votes compared to the national mean), the bigger the available pool of Democratic voters needs to be to flip the district. Since a district’s diversity and level of urbanization is reflected in the PVI, the final model that predicted the two party vote share of Virginia’s Democratic candidates factors in the district’s PVI score, how many residents have a college education, and whether the Democrat matched or outspent his or her opponent. Additionally, whether a district voted for Clinton is predictive of a likelihood to flip.
Armed with her model, Dr. Bitecofer sought to determine the Democratic share of the two party vote for the 2018 election for the House of Representatives and determine which districts were likely to flip from red to blue.
Once I have baseline estimates of the predicted two-party vote share for each analyzed district based on the linear regression model, I then consider other factors expected to influence the election outcome such as open seat status, Trump’s performance in the district relative to Mitt Romney’s performance in 2012, Trump’s approval in the state and region the district resides in, and primary turnout data.
Original House predictions, Democrats would flip 42 districts, were released in July and have been reassessed since as new information, including polling and fundraising numbers, came in. The most recent update of predictions was posted on the Wason Center blog October 11th. House districts are categorized as Will Flip, Likely Flip, Toss Up, Lean R, and Likely R. They are arranged in a table format with side to side comparisons of other election prognosticators; Cook, Crystal Ball, RCP, Inside Elections, and 538.
Currently 25 seats are rated as Will Flip. All of these districts include an urban/suburban area where a previously substantial untapped pool of Democratic voters is eager to send Trump and his Republican Party a message.
- AZ-2, CA-10, CA-25, CA-45, CA-48, CA-49, CO-6, FL-27, IA-1, IL-6, KS-3, MI-8, MI-11, MN-2, MN-3, NC-9, NJ-2, NJ-7, NJ-11, PA-6, PA-7, PA-17, VA-7, VA-10, WA-8
Likely to flip. These districts grab a chunk of an urban/suburban area but the PVI is more republican and thus a larger surge of Democrats is needed to win.
- CA-39, C0-3, FL-26, IL-14, NC-2, NE-2, NJ-3, NY-19, TX-7, TX-23, TX-32
Lean D includes the only two Democratic held districts that are considered in play.
Toss Ups.
- CA-21, FL-6, FL-15, FL-16, FL-18, FL-25, GA-6, GA-7, IL-12, IL-13, KS-2, ME-2, NC-13, NM-2, NY-11, NY-22, NY-24, NY-27, OH-1, OH-14, PA-1, PA-10, UT-4, VA-5, WA-3, WA-5, WI-1
Lean R.
- CA-22, CA-50, MI-7, MT-AL, NV-2, NY-1, OH-12, TX-21, TX-22, VA-2, WV-3
Likely R.
- AL-2, AR-2, IA-4, IN-2, IN-5, IN-9, MI-6, NC-8, PA-16, TX-10, TX-31, VA-1
After being introduced to Dr. Bitecofer’s negative partisanship theory of voting behavior, and the key factors to look for to judge whether a district (or state) will flip, I began to see (as she titles her post explaining her theory, research, and model) signs, signs, everywhere are signs!
Democrats raking in the dough.
Democrats are voting.
Dr. Bitecofer channeling Nate Silver: Trump is the signal. Everything else is noise. And God help us all, Trump has been signaling! I’m beyond negative partisanship. I loathe Trump with the fire of 1,000 suns!
Election forecasters’ predictions are aligning with what Dr. Bitecofer forecast last July.
There is so much information for election junkies on the Wason Center Blog. I recommend people start with Dr. Bitecofer’s post on her theory, research, model, and initial predictions for the 2018 House races, Signs, Signs, Everywhere Are Signs: Why Democrats Will Win Big in the 2018 Midterms. There are also blog posts on Senate and Governor’s races (Dr. B thinks Beto and Stacey Abrams can win), as well as analysis and polling done on Virginia congressional races.
Total number of House seats Democrats will gain increases from 42 to 47. The number of seats rated Will Flip more than doubles from 12 to 26. Likely Flip is now 11 seats. Lean D now 7. And 25 are rated Toss Ups.
In the Senate, AZ and NV flip to the democrats, but Democrats lose ND. Republicans keep TX and TN, while Democrats hold WV, IN, MT, FL, and MO. That leaves the Senate split 50:50 with Pence as a tie breaker for the Republicans. Dr. Bitecofer emphasizes that there is a high amount of uncertainty with Senate predictions since so many are literal tossups.
Democrats will win several Governorships. Dr. Bitecofer predicts all the following will flip; FL, GA, IA, IL, KS, ME, MI, NM, NV, OH, AND WI. Republicans will pick up Alaska and will keep AZ, MD, OK, and NH. Democrats will hold MN, CO, CT, RI, and OR.
Read her analysis and see the complete list of predictions at The Wason Center blog.