/Quick notes:

Our focus here is on the New Mexico State Senate. The 54th State Legislature concluded in January 2020, at the end of the second session. (The legislature actually ended with a small special session held in June 2020). However, the state has yet to make available details of the second session. As a result, analyses here are limited to the legislative activity of the first session of the 54th, which convened January-March 2019.

/Legislators

Details on the composition each state legislature are made available by Open States here. In the case of New Mexico, this list only includes legislators from the most recent session of the most recent congress, ie, the second session of the 54th legislature, which convened Jan-Feb 2020.

os_legislators <- read.csv(paste0(open_states_dir, '/legislators/nm.csv'))

Here, we make some small changes to the legislator data set to help align identifiers with those used in subsequent data sets.

legs2020 <- os_legislators %>%
  mutate(family_name = gsub('Lara Cadena', 'Cadena', family_name)) %>%
  arrange(current_chamber, family_name, given_name) %>%
  group_by(current_chamber, family_name) %>%
  mutate(n = n()) %>%
  ungroup() %>%
  mutate(family_name = ifelse(n>1, 
                              paste0(family_name, ', ', given_name),
                              family_name)) %>%
  select(current_chamber, name, family_name, given_name, current_party, current_district)

Not taken into account by the folks at Open States is the fact that the compositions of the both state houses in New Mexico have changed a bit from the 1st (2019) to 2nd (2020) session. The table below includes state lawmakers who served in the first session but not the second. Note that while our focus here will be the Senate, we present methods for sorting out data for both chambers.

current_chamber = c('upper', 'upper', 'lower', 'lower')
name <- c('Carlos Cisneros', 'John Pinto', 
          'William Praat', 'Roberto "Bobby" J. Gonzales')
family_name <- c('Cisneros', 'Pinto', 'Praat', 'Gonzales')
current_party <- rep('Democratic', 4)
current_district <- c(6, 3, 27, 42)
##  session_identifier <- '2019'

adds <- data.frame(current_chamber,
                  name,
                  family_name,
                  current_party,
                  current_district)

adds %>% knitr::kable()

To account for these changes, then, we build an independent table for first session lawmakers of the 54th Congress.

legs2019 <- legs2020 %>%
  filter(!paste0(current_chamber, current_district) %in%
           paste0(adds$current_chamber, adds$current_district)) %>%
  bind_rows(adds)%>%
  arrange(current_chamber, family_name, given_name)

/Bills

Details about bills - title, language, sponsors, etc - can be accessed via Open States here. Bill information is made available by legislative session; files for the 2019 session include:

list.files(path = open_states_dir, pattern = "NM_2019_bill_", recursive = T)
## [1] "bills/2019/NM_2019_bill_actions.csv"       
## [2] "bills/2019/NM_2019_bill_document_links.csv"
## [3] "bills/2019/NM_2019_bill_documents.csv"     
## [4] "bills/2019/NM_2019_bill_sources.csv"       
## [5] "bills/2019/NM_2019_bill_sponsorships.csv"  
## [6] "bills/2019/NM_2019_bill_version_links.csv" 
## [7] "bills/2019/NM_2019_bill_versions.csv"

Load files:

setwd(open_states_dir)
bill_files <- list.files(path = open_states_dir, pattern = "bills.csv", recursive = T)
os_bills <- lapply(bill_files, read.csv) %>% data.table::rbindlist() %>% 
  mutate(session_identifier = as.character(session_identifier))

/Roll-calls

Next, we load roll call data. These data are scattered across multiple tables, and to get a full picture of vote results and bill details, we need to do some table joining. Below, we load three tables: (1) the votes table, which contains meta data about the vote/bill, and helps cross votes to bill; (2) the votes_people table, which contains actual legislator-level roll calls; and (3) vote_counts, which summarizes vote results.

setwd(open_states_dir)
vdetails_files <- list.files(path = open_states_dir, pattern = "votes.csv", recursive = T)
os_vdetails <- lapply(vdetails_files, read.csv) %>% data.table::rbindlist() %>% 
  mutate(session_identifier = as.character(session_identifier))
vote_files <- list.files(path = open_states_dir, pattern = "vote_people.csv", recursive = T) 
os_votes <- lapply(vote_files, read.csv) %>% data.table::rbindlist() 

##
roll <- read.csv('bills/2019/NM_2019_vote_counts.csv') %>%
  select(-id) %>%
  spread(option, value) 

Here, we piece these three tables together to get a clearer perspective on things.

bill_votes <- os_vdetails %>% select(-identifier) %>%
  #mutate(session_identifier = as.character(session_identifier)) %>%
  left_join(os_bills, by = c('bill_id' = 'id',
                             'jurisdiction' = 'jurisdiction',
                             'session_identifier' = 'session_identifier')) %>%
  left_join(roll, by = c('id' = 'vote_event_id'))

A sample of votes on the Senate side for which there is some contention:

bill_votes %>%
  filter(no > 10 & motion_text == 'senate passage') %>%
  select(identifier, title, yes, no) %>%
  DT::datatable(rownames = FALSE, 
                options = list(dom = 't',
                               pageLength = 16,
                               scrollX = TRUE))

/Legislator votes

Lastly, we combine tables with (1) legislator details and (2) roll call information.

leg_votes <- os_votes %>% 
  select(-id) %>%
  left_join(bill_votes, by = c('vote_event_id' = 'id')) 

An issue with Open States data – at least in the case of New Mexico – is that legislators are not assigned a unique voter_id (in the votes_people data set) (the column exists, but is mostly empty - ?), and there are differences in how legislators are referred to in the legislator meta data and in the roll call data – so we can’t join the two data sets. For now, we rely on alphabetical order and some hacks to relate the two data sets, but surnames in New Mexico can present challenges on this front.

legs_in_rolls  <- leg_votes %>% 
  group_by(organization_classification, voter_name) %>% 
  count() %>% 
  filter(voter_name != 'LT. GOVERNOR' & n > 100) %>%
  select(-n) %>%
  ungroup()  %>% 
  bind_cols(legs2019)

Then we restructure data in a wider format to ready things for subsequent NOMINATE-based analyses.

dups <- c('ocd-vote/77a697c2-c53b-4856-8631-0773e72f9f06',
          'ocd-vote/2b8128f5-768e-4630-9af5-21e986dc2fa8',
          'ocd-vote/c7a04b77-f55d-47f0-a8f1-860ed4b3a3d6')

wide_rolls <- leg_votes %>%
  filter(!vote_event_id %in% dups) %>%
  mutate(tid = paste0(session_identifier, '_', 
                      gsub(' ', '-', identifier))) %>%
  select(voter_name, tid, option) %>%
  mutate(vote = case_when(option == "yes" ~ 1,
                          option == "no" ~ 6,
                          !option %in% c(1,6) ~ 9)) %>%
  dplyr::select(-option) %>%
  spread(key = tid, value = vote) %>%
  arrange(voter_name)

/Build roll call object

roll_obj <- wide_rolls %>%
  filter(voter_name %in% leg_sub$voter_name) %>%
  select(-voter_name) %>% 
  pscl::rollcall(yea = 1,
                 nay = 6,
                 missing = 9,
                 notInLegis = NA,
                 vote.names = grep('-', colnames(wide_rolls), value = T), 
                 legis.names = leg_sub$voter_name) 

/Build DW-NOMINATE model

if(chamber == 'lower') { pol <- c('Townsend', 'Rehm')} else
  {pol <- c('INGLE', 'INGLE')}

ideal_2d <- roll_obj %>%
  wnominate::wnominate (dims = 2, 
                        minvotes = 20,
                        lop = 0.025,
                        polarity = pol,
                        verbose = FALSE)

/Visualize two-dimensional solution

A two-dimensional model was specified. Per the multidimensional scaling procedure, the plot below represents legislators in two-dimensional political space based on roll call voting records for the first session of the 54th legislature. Dimensions:

• 1D: Right-Left –> Conservative – Liberal;

• 2D: North-South –> socially conservative – socially liberal.

The latter: a distinction that has become less useful (in accounting for variation in voting behavior at the federal level) over the past 20 years.

chamber_data <- ideal_2d$legislators %>% 
  bind_cols(leg_sub) 

chamber_data %>% 
  ggplot(aes(x=coord1D, 
             y=coord2D, 
             label = family_name)) +
  geom_point(aes(color = current_party),
             size= 3, 
             shape= 17) +
  
  wnomadds::scale_color_party() + 
  annotate("path",
           x = cos(seq(0,2*pi, length.out = 300)),
           y = sin(seq(0,2*pi, length.out = 300)),
           color = 'gray',
           size = .25) +
  
  ggrepel::geom_text_repel(
    data  = chamber_data,
    nudge_y =  -0.005,
    direction = "y",
    hjust = 0,
    size = 3.5) +
   
  theme_minimal() +
  theme(legend.position = 'none') +
  labs(title = "Ideal point estimates in two-dimensional space",
       subtitle = "New Mexico State Senate 2019") 

/wnomadds & cutting lines

## IF ROLL CALLS are dropped -- this may cause problems --
row.names(ideal_2d$rollcalls) <- colnames(wide_rolls)[2:ncol(wide_rolls)]

with_cuts <- wnomadds::wnm_get_cutlines(ideal_2d, 
                                        rollcall_obj = roll_obj, 
                                        add_arrows = TRUE,
                                        arrow_length = 0.05)
ggplot () + 
  wnomadds::scale_color_party() +
  theme_minimal() +
  theme(legend.position = 'none') + 
  annotate("path",
           x = cos(seq(0,2*pi, length.out = 300)),
           y = sin(seq(0,2*pi, length.out = 300)),
           color = 'lightgray',
           size = .25) +
  geom_point(data=chamber_data, 
               aes(x=coord1D, y=coord2D, color = current_party),
               size= 3, 
               shape= 17) +
  geom_segment(data=with_cuts, 
               aes(x = x_1, y = y_1, xend = x_2, yend = y_2), color='gray',) + 
  geom_segment(data=with_cuts, 
               aes(x = x_2, y = y_2, xend = x_2a, yend = y_2a), color='gray',
               arrow = arrow(length = unit(0.2,"cm"))) +
  geom_segment(data=with_cuts, 
               aes(x = x_1, y = y_1, xend = x_1a, yend = y_1a), color='gray',
               arrow = arrow(length = unit(0.2,"cm")))+
  geom_text(data=with_cuts, 
               aes(x = x_1a, y = y_1a, label = Bill_Code), 
               size=2.5, 
               nudge_y = 0.03,
               check_overlap = TRUE) +
  #coord_fixed(ratio=1) + 
  labs(title = "Cutting lines & legislator coordinates",
       subtitle = "New Mexico State Senate 2019")