Election Fundamentals and Polls Favor the Republicans

SY M P OS I U M Election Fundamentals and Polls Favor the Republicans Benjamin Highton, University of California, Davis Eric McGhee, Public Policy Institute of California John Sides, George Washington University O ur congressional forecasting model provides predictions of individual House and Senate races as well as aggregate party seat shares in each chamber. It does so by marrying an underlying structural or “fundamentals”-based model with available polling data—an approach similar to Linzer (2013). The structural portion of the model is based on contested House and Senate elections from 1980 to 2012, excluding those when an independent or third-party candidate won a signiﬁ cant share of the vote. 1 The dependent variable is the Democratic candidate’s share of the major-party vote. The independent vari- ables are drawn from the extensive literature that has identiﬁ ed signiﬁ cant national and state or district correlates of congres- sional election outcomes (e.g., Jacobson 2012). These include: • The nonannualized change in real gross domestic product (GDP) in the ﬁ rst two quarters of the election year. • The president’s average approval rating in June of the elec- tion year. • Whether it is a midterm or presidential election year. • Whether the seat is being contested by a Republican incum- bent, a Democratic incumbent, or no incumbent (i.e., it is an open seat). • The Democratic Party’s share of the two-party presidential vote in each political unit (states or districts) in the concur- rent or most recent election (mean-deviated by election year). • Relative candidate quality, which is the diﬀ erence between the Democrat’s level of previous elective oﬃ ce experience and the Republican’s. For House races, experience is coded as a dichotomy, distinguishing candidates who have held at least some elective oﬃ ce from candidates who have held no such oﬃ ce. For Senate races, each candidate is coded on a six-point scale: no elective oﬃ ce; a local oﬃ ce or state legislator; a statewide oﬃ ce other than governor (including former US senators); US House or large city mayor; governor; or incumbent US senator. • The Democratic candidate’s share of spending by the two major-party candidates. For Senate elections, the model also includes: • The previous Democratic vote share for the seat. The eﬀ ect of this variable is allowed to vary by whether the incumbent 786 PS • October 2014 senator is running for reelection. (Including lagged vote share in the model of House elections requires excluding election years that follow redistricting.) • Whether an appointed senator is running for his or her ﬁ rst election, and that senator’s party. All variables are coded so that higher values capture better circumstances for Democratic candidates. For the three national- level variables, we multiply each by a variable for the president’s party (–1=Republican; +1=Democrat) and also include that indi- cator in the model. We account for uncertainty in our forecasts by assigning probabilities to all our predictions based on the error param- eters in our models (Lauderdale and Linzer 2014). Likewise, because the structural model cannot account for all the factors that make each state and election year unique, we estimate a multilevel model with random intercepts for states and years so we can propagate that uncertainty more precisely into our predictions. 2 The results of the structural model for both House and Senate elections are shown in table 1. Across the two types of elections notable similarity exists in the apparent eﬀ ects of presidential approval and the midterm penalty. The estimated eﬀ ects of campaign spending are also almost identical across the models. The estimate for presiden- tial vote is larger for House elections, but that may be due to the inclusion of previous vote in the Senate models because both variables tap the local partisan balance. Also note that several candidate-related variables—incumbency, appointed, and qual- ity diﬀ erential—are not comparable across the election types because of the diﬀ erences in model speciﬁ cation and measure- ment noted previously. These parameter estimates may be used to make predictions for the 435 House and 36 Senate elections this year. We do this by substituting the 2014 values for each of the elections and computing the predicted vote shares. (Because ﬁ nal spending amounts will not be available until well after November, we use the most recent ﬁ gures for fundraising during the 2013–2014 cycle.) Our estimates take into account uncertainty about the model parameters (the standard errors of the coeﬃ cients) along with the overall uncertainty in the model. In this process, the random error terms for years and states can be thought of as “oﬀ sets” that identify how each year or state diﬀ ered from all others for unmeasured reasons. The multilevel model constrains these oﬀ sets to collectively form a normal distribution with © American Political Science Association, 2014 doi:10.1017/S1049096514001279