Electoral systems

Classification

Bormann, Nils-Christian, and Matt Golder. 2013. “Democratic Electoral Systems around the World, 1946–2011.” Electoral Studies 32(2): 360–69. — doi:10.1016/j.electstud.2013.01.005

Fig. 2. Classification of legislative electoral systems

library(DT)
library(sf)

Linking to GEOS 3.8.0, GDAL 3.0.4, PROJ 6.3.1

library(tidyverse)

es_raw <- read_rds("source__es-data-v3.rds")
es <- es_raw

es_ctry <- 
  es %>% 
  filter(presidential == 0) %>% 
  group_by(country) %>% 
  mutate(
    first = min(year),
    last = max(year),
    n = n()
  ) %>% 
  filter(row_number() == n()) %>% 
  ungroup() %>% 
  relocate(first, last, n, .after = country_name) %>% 
  relocate(elec_id, date, .after = last_col()) %>% 
  select(-presidential)

Maps

world <- read_rds("worldmap.rds")

pl_world <- 
  ggplot(data = world) + 
  geom_sf(size = 0.2) +
  theme_void()

get_dt_table <- function(dt, var1, var2) {
  dt %>% 
    as_tibble() %>% 
    filter(! is.na({{var2}})) %>% 
    count({{var1}}, {{var2}})
}

Electoral systems

# use `right_join()` to keep sf class and remove not convered countries
dt_map <- 
  world %>% 
  right_join(es_ctry) %>%
  mutate(classification = paste(type_short, classification, sep = "-"))
  
pl_world + 
  geom_sf(data = dt_map, aes(fill = type_short), size = 0.2) +
  coord_sf(crs = "+proj=robin") +
  scale_fill_brewer(type = "qual", palette = 1)

pl_class <- 
  pl_world + 
  geom_sf(data = dt_map %>% filter(! str_detect(classification, "NA")),
          aes(fill = classification), size = 0.2) +
  coord_sf(crs = "+proj=robin") +
  scale_fill_brewer(type = "qual", palette = 3)

print(pl_class)

ggsave("z-electoral-systems-map.png", pl_class)

get_dt_table(dt_map, legislative_type, classification)

Majoritarian types

pl_dt <- dt_map %>% filter(type_short == "Maj")

pl_world + 
  geom_sf(data = pl_dt, aes(fill = elecrule_short), size = 0.2) +
  coord_sf(crs = "+proj=robin") +
  scale_fill_brewer(type = "qual", palette = 7)

pl_dt %>% 
  as_tibble() %>% 
  count(classification, elecrule) %>% 
  arrange(-n)

PR formulas

pl_dt <- dt_map %>% filter(type_short == "Pro", ! is.na(tier1_short))

pl_world + 
  geom_sf(data = pl_dt, aes(fill = tier1_short), size = 0.2) +
  coord_sf(crs = "+proj=robin")

get_dt_table(pl_dt, classification, tier1_formula)

Mixed types

pl_dt <- dt_map %>% filter(type_short == "Mix", ! is.na(mixed_type))

pl_world + 
  geom_sf(data = pl_dt, aes(fill = mixed_type), size = 0.2) +
  coord_sf(crs = "+proj=robin") +
  scale_fill_brewer(type = "qual", palette = 6)

get_dt_table(pl_dt, classification, mixed_type)

Regions

rule_region <- 
  es_ctry %>% 
  arrange(country) %>% 
  group_by(region3_short, type_short, classification) %>% 
  summarise(
    n = n(),
    countries = paste(str_to_title(country), collapse = ", "),
    .groups = "drop"
    ) %>% 
  rename(region3 = region3_short, type = type_short)

rule_region %>% datatable()

Countries

Only last legislative election provided for each country.

es_ctry %>% datatable()

# write_csv(es_ctry, "es-data-v3-country_HD.csv")