library(conflicted) # create errors for function name conflicts
library(tidyverse)
conflicts_prefer(dplyr::filter, .quiet = TRUE)
library(knitr) # layout // tables in RMarkdown with kable()
ggplot2::theme_set(theme_minimal())
options(scipen = 999) # suppress scientific notation
library(patchwork) # plots // arrange
library(reactable) # layout // interactive data tables
In the 2024 United Kingdom general election Labour won a 33.7% vote share and gained a 63.2% seat share. Compared to the 2019 UK election, it increased its vote share by 3.2% and its seat share by 27.3%.
Majoritarian electoral systems convert votes into seats disproportionally and provide a seats bonus for the winning party. However, the votes-seats ratio of the Labour party in the 2024 election is rather high.
Among more than 600 majoritarian elections in democratic regimes between 1900 and 2020 it is one of the highest votes-seats difference (see below).
Here, I provide a brief overview of the votes-seats translation for majoritarian electoral systems (first-past-the-post and two-round) in democratic regimes, following the V-Dem classifications.
For some background on electoral systems around the world, see the notebooks about types and evolution of electoral systems since 1900.
elec_raw <- read_csv("data/uk-election-2024_bbc.csv")
pl_dt <-
elec_raw |>
mutate(
seat_share = round(seats / 650 * 100, 1), # total seats UK Parliament: 650
party = fct_reorder(party, vote_share)
) |>
filter(vote_share >= 2.5)
party_colors <- c(
"Lab" = "#E91D0E",
"Con" = "#0575C9",
"R-UK" = "#12b6cf",
"Lib" = "#FF9A02",
"Green" = "#99CC33",
"SNP" = "#0AD1E0"
)
pl1 <-
ggplot(pl_dt, aes(x = vote_share, y = party, fill = party)) +
geom_col() +
xlim(0, 60) +
scale_fill_manual(values = party_colors) +
labs(y = NULL) +
theme(legend.position = "none")
pl2 <-
ggplot(pl_dt, aes(x = seat_share, y = party, fill = party)) +
geom_col() +
scale_fill_manual(values = party_colors) +
labs(y = NULL) +
theme(axis.text.y = element_blank(), legend.position = "none")
pl1 + pl2
Election results are from V-Party (v2) and electoral systems classifications from V-Dem (v11.1).
For the analysis, election results are selected for all majoritarian electoral systems (first-past-post and two round) in democratic regimes since 1900 (V-Dem classifications).
callr::rscript("data-raw.R")UK election results for Conservatives, Labour, and Liberals from 1900 to 2024 with 2024 results added from BBC News.
uk_3 <- read_csv("data/uk-elections_top-3_vparty.csv")Seat and vote share of three main parties in the UK.
pl <-
ggplot(uk_3, aes(x = year, color = party)) +
geom_line(aes(y = seat_share)) +
geom_point(aes(y = seat_share, shape = "Seats")) +
geom_line(aes(y = vote_share), alpha = 0.3) +
geom_point(aes(y = vote_share, shape = "Votes"), alpha = 0.6) +
labs(x = "Year", y = "Share (%)", color = "Party", shape = "Share") +
scale_color_manual(values = party_colors) +
scale_shape_manual(values = c("Seats" = 16, "Votes" = 1))
ggsave("figures/elections-uk.png", plot = pl, width = 10, height = 6, dpi = 300)
pl
Two highest seat share gains through positive votes-seats ratio by party.
uk_3 |>
slice_max(order_by = share_diff, n = 2, by = c(party)) |>
select(party, year, share_gain = share_diff, seat_share, vote_share) |>
arrange(party, desc(share_gain))| party | year | share_gain | seat_share | vote_share |
|---|---|---|---|---|
| Con | 1931 | 21.4 | 76.4 | 55.0 |
| Con | 1924 | 19.8 | 67.0 | 47.2 |
| Lab | 2024 | 29.7 | 63.4 | 33.7 |
| Lab | 2001 | 21.8 | 62.5 | 40.7 |
| Lib | 1906 | 15.2 | 59.7 | 44.5 |
| Lib | 1910 | -0.3 | 40.4 | 40.7 |
Seat share losses through negative votes-seats ratio.
uk_3 |>
slice_min(order_by = share_diff, n = 2, by = c(party)) |>
select(party, year, share_loss = share_diff, seat_share, vote_share) |>
arrange(party, share_loss)| party | year | share_loss | seat_share | vote_share |
|---|---|---|---|---|
| Con | 1906 | -17.2 | 19.6 | 36.8 |
| Con | 1910 | -7.2 | 33.4 | 40.6 |
| Lab | 1931 | -22.2 | 8.5 | 30.7 |
| Lab | 1935 | -12.9 | 25.0 | 37.9 |
| Lib | 1983 | -22.8 | 2.6 | 25.4 |
| Lib | 1987 | -20.0 | 2.6 | 22.6 |
Votes-seats differences for the two largest parties in an election.
Nine countries with the highest number of majoritarian elections are presented.
top_2 <- read_csv("data/majoritarian-elections_top-2_vparty.csv")ctry_count <-
top_2 |>
distinct(country, year) |>
count(country, sort = TRUE)
pl <-
top_2 |>
filter(country %in% ctry_count[["country"]][1:9]) |>
ggplot(aes(x = year, color = rank)) +
geom_line(aes(y = seat_share)) +
geom_point(aes(y = seat_share, shape = "Seats")) +
geom_line(aes(y = vote_share), alpha = 0.3) +
geom_point(aes(y = vote_share, shape = "Votes"), alpha = 0.6) +
labs(x = "Year", y = "Share (%)", color = "Party", shape = "Share") +
facet_wrap(vars(country_name))
ggsave("figures/elections-majoritarian.png",
plot = pl, width = 10, height = 6, dpi = 300
)
pl
Votes and seats share differences in majoritarian democratic elections.
vparty_elec <- read_csv("data/majoritarian-elections_vparty.csv")
pl1 <-
ggplot(vparty_elec, aes(x = vote_share, y = share_diff, colour = elec_sys)) +
geom_point(alpha = 0.3) +
guides(colour = "none")
pl2 <-
ggplot(vparty_elec, aes(x = share_diff, colour = elec_sys)) +
geom_histogram()
pl1 + pl2 + plot_layout(widths = c(3, 2))
Highest vote/seat share difference for all democratic regimes with majoritarian elections.
top_2 |>
slice_max(order_by = share_diff, n = 1, by = c(country)) |>
select(country, country_name, year, party, vote_share, seat_share, share_diff) |>
arrange(desc(share_diff)) |>
reactable(searchable = TRUE, striped = TRUE)Types of electoral systems in democratic regimes classified by Bormann and Golder (2013).
uk_3 |>
select(year:seats_total) |>
reactable(searchable = TRUE, striped = TRUE)top_2 |>
select(country, country_name, year, party, vote_share, seat_share, share_diff) |>
reactable(searchable = TRUE, striped = TRUE)Electoral systems
top_2 |>
distinct(country, country_name, elec_sys) |>
reactable(searchable = TRUE, striped = TRUE)library(httr2)
get_doi_reference <- function(doi) {
request(paste0("https://doi.org/", doi)) |>
req_headers("Accept" = "text/x-bibliography") |>
req_perform() |>
resp_body_string()
}
references_doi <- c(
"10.1016/j.electstud.2013.01.005",
"10.2139/ssrn.3831905",
"10.1080/13569775.2021.1993564",
"10.23696/vpartydsv2"
)
references <- map_chr(references_doi, get_doi_reference)