+Political ideology

The dependent variable in the model is roll call voting behavior, or political ideology. DW-NOMINATE scores from the VoteView project are accessed here via the Rvoteview package. Our focus here, then, is first dimension scores from the 116th House. Scores range from -1 (the most liberal) to +1 (the most conservative). A score of 0, then, would represent a more moderate voter.

nominates <- Rvoteview:: member_search(chamber= 'House', congress = 116) %>%
  select(state_abbrev, district_code, nominate.dim1)

+Member party & member characteristics

The CivilServiceUSA makes available a collection of characteristics about house members, including age, gender, and race/ethnicity. I have cached these data in a package dubbed uspoliticalextras, available here.

devtools::install_github("jaytimm/uspoliticalextras")
reps <- uspoliticalextras::uspol_csusa_house_bios %>%
  filter(congress == 116) %>%
  select(state_fips:district_code, party, last_name, gender, ethnicity, date_of_birth) %>%
  mutate(age = round(lubridate::interval(date_of_birth, 
                                         Sys.Date())/lubridate::duration(num = 1, 
                                                                         units = "years"))) %>%
  select(-date_of_birth)%>%
  mutate(district_code = ifelse(district_code == 0, 1, district_code),
         ethnicity = ifelse(grepl('middle|multi|nativ|pacific', ethnicity), 
                            'other-race', 
                            ethnicity),
         ethnicity = gsub('-american', '', ethnicity),
         ethnicity = gsub('african', 'black', ethnicity))

+Constituency demographics

Some demographic characteristics for US congressional districts are also included uspoliticalextras. These were accessed via tidycensus, and included in the package out of convenience. 2018 1-year ACS estimates for 12 variables, which include:

unique(uspoliticalextras::uspol_dems2018_house$variable)
##  [1] "Median_HH_Income"          "Per_BachelorsHigher"      
##  [3] "Per_BachelorsHigher_White" "Per_Black"                
##  [5] "Per_clf_unemployed"        "Per_ForeignBorn"          
##  [7] "Per_Hispanic"              "Per_LessHS"               
##  [9] "Per_LessHS_White"          "Per_VotingAge"            
## [11] "Per_White"                 "CD_AREA"

For modeling purposes, constituency demographics are characterized in terms of % population that is Black, % population that is Hispanic, and % population that has obtained a bachelor’s degree or higher. Median household income for the district is also considered. Lastly, each district is identified as being a part of the south or not, where southern states are defined as the eleven states of the Confederacy, plus Oklahoma & Kentucky.

south <- c('SC', 'MS', 'FL', 
           'AL', 'GA', 'LA', 'TX', 
           'VA', 'AR', 'NC', 'TE',
           'OK', 'KE')

dems <- uspoliticalextras::uspol_dems2018_house %>% 
  spread(variable, estimate) %>%
  mutate(district_code = ifelse(district_code == 0, 1, district_code),
         is_south = ifelse(state_abbrev %in% south, 'Yes', 'No'))

We then join the three data sets, and are good to go. This full data set is available here.

full <- reps %>% left_join(dems) %>% left_join(nominates) %>%
  mutate(ethnicity = as.factor(ethnicity),
         gender = as.factor(gender),
         party = as.factor(party),
         is_south = as.factor(is_south))

+Three models

Per McCarty, Poole, and Rosenthal (2016), and largely for good measure here, we investigate the utility of three models in accounting for variation in DW-NOMINATE scores: (1) only constituent demographics, (2) constituent demographics and house member party, and (3) constituent demographics, house member party, and house member characteristics.

modA <- lm(dw_NOMINATE_DIM_1 ~ 
             cd_BACHELORS + 
             cd_HH_INCOME + 
             cd_BLACK +
             cd_HISPANIC + 
             cd_IS_SOUTH, 
           data = full1)

####
modB <- lm(dw_NOMINATE_DIM_1 ~ 
             cd_BACHELORS + 
             cd_HH_INCOME + 
             cd_BLACK +
             cd_HISPANIC + 
             cd_IS_SOUTH + 
             
             member_PARTY, 
           data = full1)

####
modC <- lm(dw_NOMINATE_DIM_1 ~ 
             cd_BACHELORS + 
             cd_HH_INCOME + 
             cd_BLACK +
             cd_HISPANIC + 
             cd_IS_SOUTH +
             
             member_PARTY +
             
             member_GENDER +
             
             member_ETHNICITY +
             member_AGE, 
           data = full1)

+Adjusted r-squared per model

Values are similar to those presented McCarty, Poole, and Rosenthal (2016).

data.frame(modA = round(summary(modA)$adj.r.squared, 3),
           modB = round(summary(modB)$adj.r.squared, 3),
           modC = round(summary(modC)$adj.r.squared, 3)) %>%
  knitr::kable()

+Coefficients: full model

td <- broom::tidy(modC) %>%
  mutate_if(is.numeric, round, 3) 

colors <- which(td$`p.value` < .05)

td %>%
  knitr::kable(booktabs = T, format = "html") %>%
  kableExtra::kable_styling() %>%
  kableExtra::row_spec(colors,  
                       background = "#e4eef4") #bold = T, color = "white",

+A visual summary

jtools::plot_summs(modC, scale = TRUE)

+Some interpretations

In terms of constituency demographics, then, house members representing districts with higher percentages of college grads and Hispanics tend to have lower NOMINATE scores, ie, are more liberal in voting behavior. Also, house members representing non-Southern districts have lower scores.

In terms of member characteristics, Black house members, female house members, and older house members all have lower scores as well. Party affiliation is the strongest predictor – simply getting elected as a Democrat amounts to a 0.776 decrease in NOMINATE scores (on average) relative to a Republican (again, on a scale from -1 to +1).

For the average present day American, model results are in no way surprising. However, as McCarty, Poole, and Rosenthal (2016) demonstrate (see Chapter 2 appendix), constituency demographics & member party have become increasingly more predictive of roll call voting behavior since the early 70s. As voting behavior in the house has become more extreme & ideologically divided.