In zumbov2/swissparl: Interface to the Webservices of the Swiss Parliament

knitr::opts_chunk$set(echo = TRUE, dpi = 300, fig.width = 12, fig.height = 8, message = F)
pacman::p_load(dplyr, purrr, pscl, wnominate)
res <- readRDS("res.rds")

Polarization in the National Council

In political science, various dimensionality reduction methods are used to derive polarization patterns from voting data. The best known scaling method is probably [(D)W-NOMINATE](https://en.wikipedia.org/wiki/NOMINATE_(scaling_method), whose predecessor NOMINATE (Nominal Three-Step Estimation) was developed in the 1980s. We want to use this procedure to examine how polarised the National Council was in the 50th legislative period.

Data Download

Since large queries can lead to server-side errors that result in the loss of all data already downloaded, we fetch the voting data step stepwise and combine the locally stored files after the download.

For this purpose we use the following function:

get_voting_buffered <- function(id) {

  # Create folder
  folder <- "voting50"
  if(!dir.exists(folder)) dir.create(folder)

  # Download
  dt <- swissparl::get_data("Voting", Language = "DE", IdSession = id)

  # Save
  saveRDS(dt, paste0(folder, "/", id, ".rds"))

}

It is applied using purrr's walk function to all sessions of the 50th term.

# Get Session IDs
sessions50 <- swissparl::get_data("Session", Language = "DE", LegislativePeriodNumber = 50)

# Apply Function to Session IDs
purrr::walk(sessions50$ID, get_voting_buffered)

# Combine to One Dataset
v50 <- purrr::map_dfr(list.files("voting50", full.names = T), readRDS)

We find ~893,000 entries. These are now extended to include further information on the members of the Council.

# Get Councillor Data
council_members <- swissparl::get_data(
  table = "MemberCouncil", 
  Language = "DE", 
  PersonNumber = unique(v50$PersonNumber)
  )

# Join with Voting Data
v50 <- left_join(
  v50,
  council_members %>% 
    select(PersonNumber, GenderAsString, CantonAbbreviation, PartyAbbreviation),
  by = "PersonNumber"
)

Data Preparation

Before we can analyze our data with the NOMINATE scaling method, we have to convert it into an object of the class rollcall. For this we use the package pscl...this may takes a little getting used to.

# Load Packages
library(pscl)
library(dplyr)
library(tidyr)

# Wide Form
v50w <- v50 %>%
  mutate(ID = paste0(LastName, " (", ParlGroupNameAbbreviation, "/", CantonAbbreviation, ")" )) %>% 
  select(ID, GenderAsString, ParlGroupNameAbbreviation, PartyAbbreviation, IdVote, Decision) %>% 
  mutate(Decision_new = case_when(
    Decision == 1 ~ 1,
    Decision == 2 ~ 6,
    !Decision %in% c(1, 2) ~ 9
    )
  ) %>%
  select(-Decision) %>% 
  pivot_wider(names_from = IdVote, values_from = Decision_new)

# Generate rcObject
rc50 <- rollcall(
  data = v50w %>% select(-(1:4)),
  yea = 1,
  nay = 6,
  missing = 9,
  notInLegis = NA,
  legis.names = v50w$ID,
  vote.names = names(v50w %>% select(-(1:4))),
  legis.data = v50w %>% select(2:4)
)

Poole and Rosenthal W-NOMINATE

To calculate the relative positions of the politicians on 2 dimensions we use W-NOMINATE from Poole and Rosenthal. The algorithm is implented in the R-Package wnominate. The calculation takes about 10-15 minutes.

library(wnominate)

res <- wnominate::wnominate(
  rcObject = rc50, 
  minvotes = ceiling(0.25 * length(unique(v50$IdVote))),
  polarity = c("Wobmann (Fraktion V/SO)", "Wobmann (Fraktion V/SO)")
)

Political Landscape of the National Council

We now use the calculated coordinates to visualize the political polarization in the National Council in the 50th legislative period.

# Packages
library(ggplot2)
library(hrbrthemes)

# 2D-Plot
res$legislators %>%
  mutate(ParlGroupNameAbbreviation = factor(
    ParlGroupNameAbbreviation,
    levels = c("Fraktion S", "Fraktion V", "Fraktion RL", "Fraktion C", "Fraktion G", "Fraktion GL", "Fraktion BD")
    )
  ) %>% 
  ggplot(aes(x = coord1D, y = -coord2D)) +
  stat_density_2d(
    aes(alpha = stat(level), fill = ParlGroupNameAbbreviation), 
    geom = "polygon", 
    show.legend = FALSE
    ) +
  geom_point(
    aes(color = ParlGroupNameAbbreviation), 
    alpha = 0.6, 
    size = 1
    ) +
  expand_limits(x = c(-1, 1), y = c(-1, 1)) +
  coord_equal() +
  scale_fill_manual(values = c("#EE2A3B", "#00A55A", "#104FA0", "#FF850C", "#85B229", "#C2D93F", "#FFDD00")) +
  scale_color_manual(
    values = c("#EE2A3B", "#00A55A", "#104FA0", "#FF850C", "#85B229", "#C2D93F", "#FFDD00"),
    guide = guide_legend(override.aes = list(shape = 15, size = 4, alpha = 1))
    ) +
  labs(
    title = "Polarization in the National Council",
    subtitle = "W-NOMINATE Scores of the 50th Parliamentary Term",
    caption = "Data: Parliamentary Services of the Federal Assembly, Bern",
    color = ""
  ) +
  theme_ipsum_rc() +
  theme(
    legend.justification = "bottom",
    axis.title = element_blank(),
    panel.grid.minor = element_blank()
    )

Quite homogeneous parliamentary groups. Let's now foucs on the first W-NOMINATE dimension (left-right spectrum).

# Package
library(ggridges)

# 1D-Plot
res$legislators %>%
  mutate(ParlGroupNameAbbreviation = factor(
    ParlGroupNameAbbreviation,
    levels = rev(c("Fraktion S", "Fraktion G", "Fraktion GL", "Fraktion BD", "Fraktion C", "Fraktion RL", "Fraktion V"))
    )
  ) %>% 
  ggplot(aes(x = coord1D, y = ParlGroupNameAbbreviation, fill = ParlGroupNameAbbreviation)) +
  stat_density_ridges(quantile_lines = TRUE, quantiles = 2, alpha = 0.7) +
  scale_fill_manual(values = rev(c("#EE2A3B", "#85B229", "#C2D93F", "#FFDD00", "#FF850C", "#104FA0", "#00A55A"))) +
  labs(
    title = "Polarization in the National Council",
    subtitle = "W-NOMINATE 1st Dimension Scores of the 50th Parliamentary Term",
    caption = "Data: Parliamentary Services of the Federal Assembly, Bern"
  ) +
  theme_ipsum_rc() +
  theme(
    legend.position = "none",
    axis.title = element_blank(),
    panel.grid.minor = element_blank()
    )