These data are taken from a 2014 article by Charles Barrilleaux and me 2013 article in State Politics and Policy titled “The Politics of Need: Examining Governors’ Decisions to Oppose the `Obamacare’ Medicaid Expansion.” See the article for the theortical and conceptual background.

The data set is at the state level, so that each row of the data set represents one state. The data are for September 2013.

Load Data

# load packages
library(dplyr)  # for data manipulation
library(ggplot2)  # for plotting

# load data
health <- readRDS("data/health.rds")

# quick look at data
glimpse(health)
## Observations: 50
## Variables: 17
## $ state                        <chr> "Alabama", "Alaska", "Arizona", "...
## $ state_abbr                   <chr> "AL", "AK", "AZ", "AR", "CA", "CO...
## $ gov_party                    <fctr> Repubican Governor, Repubican Go...
## $ sen_party                    <fctr> Republican Senate, Republican Se...
## $ house_party                  <fctr> Republican House, Republican Hou...
## $ percent_favorable_aca        <dbl> 38.27111, 37.44285, 39.67216, 36....
## $ percent_supporting_expansion <dbl> 57.76161, 47.42469, 53.21254, 54....
## $ obama_share_12               <dbl> 38.78377, 42.68471, 45.38662, 37....
## $ ideology                     <dbl> 0.24404363, 0.04723307, 0.1048642...
## $ percent_uninsured            <int> 14, 19, 18, 18, 19, 15, 8, 10, 21...
## $ infant_mortality_rate        <dbl> 9.2, 6.5, 6.4, 7.6, 5.1, 6.2, 6.1...
## $ cancer_incidence             <dbl> 472.9, 451.4, 387.1, 426.7, 434.0...
## $ heart_disease_death_rate     <dbl> 236.0, 151.5, 146.7, 222.5, 161.9...
## $ life_expectancy              <dbl> 75.4, 78.3, 79.6, 76.0, 80.8, 80....
## $ leg_party                    <fctr> Unified Republican Legislature, ...
## $ health_score                 <dbl> -2.09998657, 0.04841030, 0.644463...
## $ health_score_cat             <fctr> Bottom Tercile, Middle Tercile, ...

Variable Descriptions

state: State

  • Coding: The name of the state.
  • Type: character
# print variable
print(health$state)
##  [1] "Alabama"        "Alaska"         "Arizona"        "Arkansas"      
##  [5] "California"     "Colorado"       "Connecticut"    "Delaware"      
##  [9] "Florida"        "Georgia"        "Hawaii"         "Idaho"         
## [13] "Illinois"       "Indiana"        "Iowa"           "Kansas"        
## [17] "Kentucky"       "Louisiana"      "Maine"          "Maryland"      
## [21] "Massachusetts"  "Michigan"       "Minnesota"      "Mississippi"   
## [25] "Missouri"       "Montana"        "Nebraska"       "Nevada"        
## [29] "New Hampshire"  "New Jersey"     "New Mexico"     "New York"      
## [33] "North Carolina" "North Dakota"   "Ohio"           "Oklahoma"      
## [37] "Oregon"         "Pennsylvania"   "Rhode Island"   "South Carolina"
## [41] "South Dakota"   "Tennessee"      "Texas"          "Utah"          
## [45] "Vermont"        "Virginia"       "Washington"     "West Virginia" 
## [49] "Wisconsin"      "Wyoming"

state_abbr: State Abbreviation

  • Coding: The state’s two-letter abbreviation.
  • Type: character
# print variable
print(health$state_abbr)
##  [1] "AL" "AK" "AZ" "AR" "CA" "CO" "CT" "DE" "FL" "GA" "HI" "ID" "IL" "IN"
## [15] "IA" "KS" "KY" "LA" "ME" "MD" "MA" "MI" "MN" "MS" "MO" "MT" "NE" "NV"
## [29] "NH" "NJ" "NM" "NY" "NC" "ND" "OH" "OK" "OR" "PA" "RI" "SC" "SD" "TN"
## [43] "TX" "UT" "VT" "VA" "WA" "WV" "WI" "WY"

gov_party: Governor’s Party

  • Coding: The political party of the governor.
  • Type: character
# create table
table(health$gov_party)
## 
##  Democratic Governor Independent Governor   Repubican Governor 
##                   19                    1                   30

sen_party: Senate Majority Party

  • Coding: The majority party in the state senate.
  • Type: character
# create table
table(health$sen_party)
## 
## Democratic Senate       Tied Senate Republican Senate 
##                19                 1                30

house_party: House Majority Party

  • Coding: The majority party in the state senate.
  • Type: character
# create table
table(health$house_party)
## 
## Democratic House       Tied House Republican House 
##               21                0               29

leg_party: Party in Control of Legislature

  • Coding: Indicates where the Republicans or Democrats are the majority in both the house and the senate, or whether the legislature is divided.
  • Type: character
# create table
table(health$leg_party)
## 
## Unified Republican Legislature            Divided Legislature 
##                             27                              5 
## Unified Democratic Legislature 
##                             18

percent_favorable_aca: Percent Favorable to ACA

  • Coding: The percent of a state’s population with a favorable opinion toward the Affordable Care Act. See Barrilleaux and Rainey (2014) for the details.
  • Type: double
# dotplot
ggplot(health, aes(x = percent_favorable_aca, y = state)) + geom_point()