Germany district MPs

library(estimatr)   # robust standard errors
library(patchwork)  # arrrange plots
library(sf)         # plot maps and gespatial tools
library(tidyverse)

ggplot2::theme_set(theme_void())

crs_deu <- "+proj=lcc +lat_1=48 +lat_2=53 +lat_0=51 +lon_0=10"  # LCC Germany parameters

Source

The Federal Returning Officer ("Der Bundeswahlleiter): Bundestag election 2017

• results constituencies
  • © The Federal Returning Officer, Wiesbaden 2017
• geometric data of constituencies
  • © Der Bundeswahlleiter, Statistisches Bundesamt, Wiesbaden 2016, Wahlkreiskarte für die Wahl zum 19. Deutschen Bundestag, Basis of the geological information © Geobasis-DE / BKG (2016)

bw_raw <- read_csv("data/btw-2017-party.csv")

bw_tier1 <- bw_raw %>% filter(level == "district", votes_type == "Erststimmen")
bw_tier2 <- bw_raw %>% filter(level == "district", votes_type == "Zweitstimmen")

mp_raw <- 
  read_delim("data/btw17_gewaehlte_utf8.csv", delim = ";", skip = 6) %>% 
  janitor::clean_names()

mp <- 
  mp_raw %>% 
  filter(gewahlt_stimmenart == "E") %>% 
  mutate(
    sex = str_replace(geschlecht, "w", "f"),
    region = if_else(gewahlt_land %in% c("BB", "MV", "SN", "ST", "TH"), "east", "west"),  # "BE"
    region = factor(region) %>% fct_relevel("west")
    ) %>% 
  select(
    state = gewahlt_land,
    region,
    district_name = gewahlt_wahlkreis_bez,
    wkr_nr = gewahlt_wahlkreis_nr,
    party = partei_kurz_bez,
    sex
    )

wk_shp <- 
  read_rds("data/btw17-districts-geometry.rds") %>% 
      left_join(mp %>% distinct(wkr_nr, region))
wk_ctr <- read_rds("data/btw17-districts-centroid.rds")

Votes

Results

party votes share federal level

• candidate tier (first tier) — “Erststimmen”
• party tier (second tier) — (“Zweitstimmen”)

bw_raw %>%
  filter(level == "federal") %>% 
  mutate(party = if_else(party %in% c("CDU", "CSU"), "CDU/CSU", party)) %>% 
  group_by(votes_type, party) %>% 
  summarise(share = sum(share) %>% round(1)) %>% 
  pivot_wider(names_from = votes_type, values_from = share)

Party votes

pl_dt <- 
  wk_shp %>% 
  select(wkr_nr, geometry) %>% 
  right_join(bw_tier2) %>% 
  mutate(party = if_else(party %in% c("CDU", "CSU"), "CDU/CSU", party)) %>% 
  group_by(party) %>% 
  mutate(share_diff = share - mean(share))

party tier (“Zweitstimmen”) vote share electoral districts

pl1 <- 
  ggplot() + 
  geom_sf(data = pl_dt, aes(fill = share), lwd = 0.05) +
  scale_fill_viridis_c(option = "magma", direction = -1) +
  coord_sf(crs = crs_deu) +
  facet_wrap(vars(party))
pl1

difference (+/-) of votes share party district from national average votes share party

pl2 <- 
  ggplot() + 
  geom_sf(data = pl_dt, aes(fill = share_diff), lwd = 0.05) +
  scale_fill_gradient2(name = "+/-") +
  coord_sf(crs = crs_deu) +
  facet_wrap(vars(party))
pl2

ggsave("z-btw17-vote-share.png", pl1 / pl2, width = 5, height = 7)

Candidate votes

candidate tier (“Erststimmen”) vote share by party

• A — plurality vote winning party
• B — sum of candidate votes share difference by ideology
  • right — AfD, CDU, CSU, FDP
  • left — DIE LINKE, GRÜNE, SPD
• I – votes share plurality winner
• II – votes share difference top 2 results

party_scale <- c("#decbe4", "#b3cde3", "#ccebc5", "#ffffcc", "#fed9a6", "#fbb4ae")

ideo_share <- 
  bw_tier1 %>% 
  group_by(wkr_nr, ideology) %>% 
  summarise(share = sum(share)) %>% 
  pivot_wider(names_from = ideology, values_from = share) %>% 
  mutate(rl_diff = right - left)

pl_dt <- 
  wk_shp %>% 
  select(wkr_nr, geometry) %>% 
  inner_join(ideo_share) %>% 
  inner_join(mp %>% select(wkr_nr, party))

pl1 <- 
  ggplot() + 
  geom_sf(data = pl_dt, aes(fill = party), lwd = 0.05) +
  coord_sf(crs = crs_deu) +
  scale_fill_manual(values = party_scale)
# print(pl1)

pl2 <- 
  ggplot() + 
  geom_sf(data = pl_dt, aes(fill = rl_diff), lwd = 0.05) +
  coord_sf(crs = crs_deu) +
  scale_fill_gradient2(name = "right +/-")
# print(pl2)

pl1 + pl2 + plot_annotation(tag_levels = "A")

diff_1_2 <- 
  bw_tier1 %>% 
  arrange(wkr_nr, desc(share)) %>% 
  group_by(wkr_nr) %>% 
  filter(row_number() %in% c(1, 2)) %>% 
  summarise(
    top_2_diff = max(share) - min(share),
    share = max(share)
  )

pl_dt <- 
  wk_shp %>% 
  select(wkr_nr, geometry) %>% 
  inner_join(diff_1_2)

pl1 <- 
  ggplot() + 
  geom_sf(data = pl_dt, aes(fill = share), lwd = 0.05) +
  coord_sf(crs = crs_deu) +
  scale_fill_viridis_c(option = "cividis", direction = -1)
# print(pl1)

pl2 <- 
  ggplot() + 
  geom_sf(data = pl_dt, aes(fill = top_2_diff), lwd = 0.05) +
  coord_sf(crs = crs_deu) +
  scale_fill_viridis_c(option = "magma", direction = -1)
# print(pl2)

pl1 + pl2 + plot_annotation(tag_levels = "I")

Female MPs

Maps

plurality winner candidate (first) tier vote by party and sex

Germany

scale_sex <- c("red", "blue")

pl_dt_shp <- wk_shp %>% left_join(mp)
pl_dt_ctr <- wk_ctr %>% left_join(mp)

ggplot() + 
  geom_sf(data = pl_dt_shp, aes(fill = party), lwd = 0.1) +
  # scale_fill_brewer(type = "qual", palette = 4) +
  scale_fill_manual(values = party_scale) +
  geom_sf(data = pl_dt_ctr, aes(shape = sex, colour = sex), alpha = 0.9) +
  coord_sf(crs = crs_deu) +
  scale_colour_manual(values = scale_sex)

NRW and Berlin

plot_state <- function(state_select, party_scale_select) {
  pl_dt_shp_re <- pl_dt_shp %>% filter(state == state_select)
  pl_dt_ctr_re <- pl_dt_ctr %>% filter(state == state_select)
  
  ggplot() + 
    geom_sf(data = pl_dt_shp_re, aes(fill = party), lwd = 0.2) +
    scale_fill_manual(values = party_scale_select) +
    geom_sf(data = pl_dt_ctr_re, aes(shape = sex, colour = sex), size = 2) +
    scale_colour_manual(values = scale_sex) +
    guides(colour = FALSE, fill = FALSE, shape = FALSE)
}

pl1 <- plot_state("NW", party_scale[c(2, 6)])
pl2 <- plot_state("BE", party_scale[c(2, 4, 5, 6)])
# plot_state("HH", party_scale[c(2, 6)])
pl1 + pl2 + plot_layout(widths = c(55, 45))

Descriptives

number (n) of districts won by party and sex (f – female, m – male)

Party

count_party <- 
  mp %>% 
  count(party, sex) %>% 
  pivot_wider(names_from = sex, values_from = n) %>% 
  replace(is.na(.), 0) %>% 
  mutate(share_f = round(100 * f / (m + f)))

count_party %>% arrange(share_f)

mp %>% 
  count(party) %>% 
  mutate(share = round(100 * n / sum(n))) %>% 
  arrange(desc(share))

States

count_state <- 
  mp %>% 
  left_join(wk_shp %>% select(wkr_nr, state_name = land_name)) %>% 
  count(state_name, sex) %>% 
  pivot_wider(names_from = sex, values_from = n) %>% 
  mutate(share_f = round(100 * f / (m + f)))

count_state %>% arrange(share_f)

Models

• area_km2 — size of district as square length (root of area)
• coord_y — north/south position (latitude)
• left_share — share of left parties (SPD, Linke, Grüne)
• region – western or eastern (former GDR) state

library(broom)
library(ggeffects)

mo_dt <-  
  pl_dt_shp %>% 
  left_join(ideo_share) %>% 
  mutate(
    female = if_else(sex == "f", 1, 0),
    left_share = left
    )

Female first-tier winners

Logit model to predict female winners of candidate (first) tier vote

mo_glm <- glm(female ~ area_km2 + coord_y + left_share, data = mo_dt, family = "binomial")

tidy(mo_glm) %>% mutate(across(where(is.numeric), round, 2))

pl1 <- ggpredict(mo_glm, terms = "area_km2 [all]") %>% plot(add.data = TRUE, show.title = FALSE)
pl2 <- ggpredict(mo_glm, terms = "coord_y [all]") %>% plot(add.data = TRUE, show.title = FALSE)
pl1 + pl2

Left vote-share

Linear model of left parties (SPD, Linke, Grüne) vote share

mo_lm <- lm_robust(left_share ~ area_km2*region + coord_y*region, data = mo_dt)

tidy(mo_lm) %>% mutate(across(where(is.numeric), round, 2))

pl1 <- ggpredict(mo_lm, c("area_km2", "region")) %>% plot(add.data = TRUE, show.title = FALSE)
pl2 <- ggpredict(mo_lm, terms = c("coord_y", "region")) %>% plot(add.data = TRUE, show.title = FALSE)
pl1 + pl2

Party vote-share

Linear model of party vote share

mo_dt2 <-  
  bw_tier2 %>% 
  mutate(party = if_else(party %in% c("CDU", "CSU"), "CDU/CSU", party)) %>% 
  # mutate(party = fct_relevel(party, "CDU")) %>% 
  left_join(wk_shp)

mo_lm2 <- lm_robust(share ~ party*area_km2*region, data = mo_dt2)

# tidy(mo_lm2) %>% mutate(across(where(is.numeric), round, 2))

ggpredict(mo_lm2, terms = c( "area_km2", "region", "party")) %>% 
  plot(add.data = TRUE, show.title = FALSE)