Data

To model individual responses, I use the ANES aggregated time-series data from 1992 to 2016. This data set is intended to be nationally representative, and has a total of 24,122 observations. Given the sampling technique and relatively small sample size (relative to the CCES or the NAES), MRP is the best estimator for generating unbiased and reliable measures of district opinion. To account for over-time changes in district lines and public opinion, I model each decade separately, with 9,085 observations for the 1990s; 5,006 observations for the 2000s; and 10,031 observations for the 2010s. Feeling thermometer estimates are generated for each group in each of the three decades.

In each of these models, the dependent variable is the group feeling thermometer score. The individual-level predictor variables in each of these models includes a respondent’s gender (two categories: male, female),Footnote ² race/ethnicity (four categories: white, Black, Hispanic, other), education (five categories: less than high school completion, completed high school, some college, college graduate, graduate school), state, and congressional district. Additionally, district-level predictors (average income, percent urban, percent military, same-sex couples, percent Hispanic, and percent African American) and state-level predictors (region, percent union, and percent Evangelical or Mormon) were obtained using decennial US Census data, as well as data from the US Religion Census. Survey year is also included to account for any variation in context or questions.

Model

I generate estimates of district hostility by modeling individual responses as a function of individual-level demographic characteristics as well as district- and state-level predictors. I model this as a multilevel linear regression equation, using the lmer package in R.Footnote ³ The structure of the model estimating individual feelings toward the poor is given by the following:

$\begin{array}{c}{y}_i{}^{ftpoor}={\gamma }_0+{\alpha }_{r\left[i\right]}^{race}+{\alpha }_{f\left[i\right]}^{female}+{\alpha }_{e\left[i\right]}^{educ}+{\alpha }_{y\left[i\right]}^{year}+{\alpha }_{d\left[i\right]}^{district}\\ {\alpha }_r^{race}~N\left(0,{\sigma }_r^2\right),\text{ for}r=1,2,3,4\\ {\alpha }_f^{female}~N\left(0,{\sigma }_f^2\right)\\ {\alpha }_e^{educ}~N\left(0,{\sigma }_e^2\right),\text{ for}e=1,2,3,4,5\\ {\alpha }_p^{year}~N\left(0,{\sigma }_y^2\right),\text{ for}p=1,2\end{array}$(1)

The random effects across each level of these individual predictors (e.g., all five categories of education) are modeled.Footnote ⁴ These effects are expected to be normally distributed with a mean of 0, and a variance determined by the data. Both the district- and state-levels model random effects for each district and state (respectively) in the dataset as well as fixed effects for the other relevant predictors, while random effects are modeled for each of the four region categories:Footnote ⁵

$\begin{array}{c}{\alpha }_d^{district}~N\left(k_{s\left[d\right]}^{state}+{\gamma }^{inc}*incom{e}_d+{\gamma }^{urban}*urba{n}_d+{\gamma }^{mil}*militar{y}_d+{\gamma }^{hisp}*hispani{c}_d+{\gamma }^{black}*blac{k}_d,{\sigma }_{district}^2\right),\text{ for}d=1,\dots ,435\end{array}$

$\begin{array}{c}{\alpha }_s^{state}~N\left({\alpha }_{z\left[s\right]}^{region}+{\beta }^{union}*unio{n}_s+{\beta }^{relig}*religio{n}_s,{\sigma }_{state}^2\right),\text{ for}s=1,\dots ,50\end{array}$

$\begin{array}{c}{\alpha }_Z^{region}~N\left(0,{\sigma }_{region}^2\right),\text{ for}z=1,2,3,4\end{array}$

Poststratification

This model is then used to generate district hostility estimates for the average member of each of 17,400 subgroups. Each of these subgroups represents a unique combination of demographic categories by which the sample is weighted: race (4), gender (2),Footnote ⁶ education (5), and congressional district (435).Footnote ⁷ Once predictions for average feeling thermometer scores are generated for each of these subgroups (from white men with less than a high school education in the first district of Alabama to non-white, Black, or Hispanic women with a graduate education in the large district of Wyoming), these estimates are then weighted according to the proportion of a district that is composed of members of these subgroups, and summed across districts.

Formally, weighted district opinion estimates are obtained using this method:

$y_{district}=\frac{{\sum }_{c\in d}{N}_c{\theta }_c}{{\sum }_{c\in d}{N}_c}$(2)

where c represents each of the forty demographic subcategories (race, gender, and education) within d, a given congressional district, θ_c is the prediction associated with each subcategory, and N_c is the frequency of individuals within a district that belong to a demographic subcategory. To weight my estimates, I use the calculated frequency proportions for each demographic category in each state or district. A summary of the estimates generated is given in Table 4.1, and graphical illustrations of each of the estimates produced are given in Figure 4.1.