Income Inequality Before and After Taxes

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slope
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Author

Rodrigo Barreiro

Published

August 5, 2025

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About the Data

Note

Check the data in TidyTuesday GitHub repository.

This week we’re exploring Income Inequality Before and After Taxes, as processed and visualized by Joe Hasell at Our World in Data: “Income inequality before and after taxes: how much do countries redistribute income?”

The Gini coefficient measures inequality on a scale from 0 to 1. Higher values indicate higher inequality. Inequality is measured here in terms of income before and after taxes and benefits.

1 Initializing

1.1 Load libraries

library(tidyverse)
library(glue)
library(scales)
library(showtext)
library(ggtext)
library(shadowtext)
library(maps)
library(ggpattern)
library(ggrepel)
library(patchwork)
library(tidylog)

font_add_google("Ubuntu", "Ubuntu", regular.wt = 400, bold.wt = 700)
showtext_auto()
showtext_opts(dpi = 300)

1.2 Set theme

cool_gray0 <- "#323955"
cool_gray1 <- "#5a6695"
cool_gray2 <- "#7e89bb"
cool_gray3 <- "#a4aee2"
cool_gray4 <- "#cbd5ff"
cool_gray5 <- "#e7efff"

cool_red0 <- "#A31C44"
cool_red1 <- "#F01B5B"
cool_red2 <- "#F43E75"
cool_red3 <- "#E891AB"
cool_red4 <- "#FAC3D3"
cool_red5 <- "#FCE0E8"

theme_set(
  theme_minimal() +
    theme(
      # axis.line.x.bottom = element_line(color = 'cool_gray0', linewidth = .3),
      # axis.ticks.x= element_line(color = 'cool_gray0', linewidth = .3),
      # axis.line.y.left = element_line(color = 'cool_gray0', linewidth = .3),
      # axis.ticks.y= element_line(color = 'cool_gray0', linewidth = .3),
      # # panel.grid = element_line(linewidth = .3, color = 'grey90'),
      panel.grid.major = element_blank(),
      panel.grid.minor = element_blank(),
      axis.ticks.length = unit(-0.15, "cm"),
      plot.background = element_blank(),
      # plot.title.position = "plot",
      plot.title = element_text(family = "Ubuntu", size = 14, face = 'bold'),
      plot.caption = element_text(
        size = 8,
        color = cool_gray3,
        margin = margin(20, 0, 0, 0),
        hjust = 0
      ),
      plot.subtitle = element_text(
        size = 9,
        lineheight = 1.15,
        margin = margin(5, 0, 15, 0)
      ),
      axis.title.x = element_markdown(
        family = "Ubuntu",
        hjust = .5,
        size = 8,
        color = cool_gray1
      ),
      axis.title.y = element_markdown(
        family = "Ubuntu",
        hjust = .5,
        size = 8,
        color = cool_gray1
      ),
      axis.text = element_text(
        family = "Ubuntu",
        hjust = .5,
        size = 8,
        color = cool_gray1
      ),
      legend.position = "top",
      text = element_text(family = "Ubuntu", color = cool_gray1),
      # plot.margin = margin(25, 25, 25, 25)
    )
)

1.3 Load this week’s data

income_inequality_processed <- readr::read_csv('https://raw.githubusercontent.com/rfordatascience/tidytuesday/main/data/2025/2025-08-05/income_inequality_processed.csv')

2 Data analysis

How many countries?

income_inequality_processed |>
  count(Entity, sort = TRUE)
# A tibble: 52 × 2
   Entity             n
   <chr>          <int>
 1 United States     61
 2 United Kingdom    54
 3 Canada            45
 4 Germany           40
 5 Brazil            38
 6 Luxembourg        37
 7 France            30
 8 Spain             30
 9 Austria           27
10 Sweden            27
# ℹ 42 more rows

How many years?

income_inequality_processed |>
  count(Year, sort = TRUE)
# A tibble: 61 × 2
    Year     n
   <dbl> <int>
 1  2010    41
 2  2016    41
 3  2013    40
 4  2007    37
 5  2015    37
 6  2014    36
 7  2017    36
 8  2018    35
 9  2004    34
10  2011    34
# ℹ 51 more rows

Both:

income_inequality_processed |>
  mutate(Entity = fct_infreq(Entity)) |>
  ggplot(aes(x = Year, y = Entity)) +
  geom_tile(aes(fill = gini_mi_eq + gini_dhi_eq), show.legend = FALSE)

Many NAs.

Lets add some filters

income_inequality_processed |>
  filter(!is.na(gini_dhi_eq) & !is.na(gini_mi_eq)) |>
  group_by(Entity) |>
  filter(n() > 10) |>
  ungroup() |>
  mutate(Entity = fct_infreq(Entity)) |>
  ggplot(aes(x = Year, y = Entity)) +
  geom_tile(aes(fill = gini_mi_eq - gini_dhi_eq))

How it change over time?

min_year <- 
  income_inequality_processed |>
  filter(!is.na(gini_dhi_eq) & !is.na(gini_mi_eq)) |>
  filter(n() > 10) |>
  group_by(Entity) |>
  slice_min(Year, n = 3) |>
  summarise(
    pre_tax = mean(gini_mi_eq), sd_pre_tax = sd(gini_mi_eq),
    pos_tax = mean(gini_dhi_eq), sd_pos_tax = sd(gini_dhi_eq)) |>
  ungroup() |>
  mutate(limit = 'lower')

max_year <- 
  income_inequality_processed |>
  filter(!is.na(gini_dhi_eq) & !is.na(gini_mi_eq)) |>
  filter(n() > 10) |>
  group_by(Entity) |>
  slice_max(Year, n = 3) |>
  summarise(
    pre_tax = mean(gini_mi_eq), sd_pre_tax = sd(gini_mi_eq),
    pos_tax = mean(gini_dhi_eq), sd_pos_tax = sd(gini_dhi_eq)) |>
  ungroup() |>
  mutate(limit = 'upper')

bind_rows(min_year, max_year) |>
  mutate(reduction = 1 - pos_tax / pre_tax ) |>
  ggplot(aes(x = limit, y = reduction)) +
  geom_point(aes(color = Entity), show.legend = FALSE) +
  geom_line(aes(color = Entity, group = Entity), show.legend = FALSE)

3 Transform Data for Plotting

min_year <- 
  income_inequality_processed |>
  filter(!is.na(gini_dhi_eq) & !is.na(gini_mi_eq)) |>
  filter(Year > 2000) |>
  filter(n() > 10) |>
  group_by(Entity) |>
  slice_min(Year, n = 3) |>
  summarise(
    pre_tax = mean(gini_mi_eq), sd_pre_tax = sd(gini_mi_eq),
    pos_tax = mean(gini_dhi_eq), sd_pos_tax = sd(gini_dhi_eq),
    year = min(Year)) |>
  ungroup() |>
  mutate(limit = 'first 3 records')

max_year <- 
  income_inequality_processed |>
  filter(!is.na(gini_dhi_eq) & !is.na(gini_mi_eq)) |>
  filter(Year > 2000) |>
  filter(n() > 10) |>
  group_by(Entity) |>
  slice_max(Year, n = 3) |>
  summarise(
    pre_tax = mean(gini_mi_eq), sd_pre_tax = sd(gini_mi_eq),
    pos_tax = mean(gini_dhi_eq), sd_pos_tax = sd(gini_dhi_eq),
    year = max(Year)) |>
  ungroup() |>
  mutate(limit = 'last 3 records')

data2plot <- 
  bind_rows(min_year, max_year) |>
  mutate(reduction = 1 - pos_tax / pre_tax ) |>
  janitor::clean_names()

# -- Raw
data2plot2 <- 
  income_inequality_processed |>
  filter(!is.na(gini_dhi_eq) & !is.na(gini_mi_eq)) |>
  filter(Year > 2000) |>
  filter(n() > 10) |>
  janitor::clean_names() |>
  rename(gini_pre_tax = gini_mi_eq,  gini_pos_tax = gini_dhi_eq)

4 Time to plot!

4.1 Before

data2plot |>
  mutate(reduction = 1 - pos_tax / pre_tax ) |>
  ggplot(aes(x = limit, y = reduction)) +
  geom_point(aes(color = entity), show.legend = FALSE) +
  geom_line(aes(color = entity, group = entity), show.legend = FALSE) +
  theme_grey()

4.2 Final

reduction_change <-
  data2plot |>
  select(entity, reduction, limit) |>
  pivot_wider(names_from = limit, values_from = reduction) |>
  janitor::clean_names() |>
  mutate(reduction_change = first_3_records - last_3_records)

more_reduction_country <- reduction_change |>
  slice_max(reduction_change, n = 1) |>
  pull(entity)
less_reduction_country <- reduction_change |>
  slice_min(reduction_change, n = 1) |>
  pull(entity)

more_reduction_data <- data2plot |> filter(entity %in% more_reduction_country)
less_reduction_data <- data2plot |> filter(entity %in% less_reduction_country)

p1 <-
  data2plot |>
  filter(
    !entity %in% c(more_reduction_data$entity, less_reduction_data$entity)
  ) |>
  mutate(reduction = 1 - pos_tax / pre_tax) |>
  ggplot(aes(x = limit, y = reduction)) +
  geom_point(show.legend = FALSE, color = cool_gray5, size = 0.5) +
  geom_line(
    aes(group = entity),
    linewidth = .2,
    color = cool_gray5,
    show.legend = FALSE
  ) +
  # add more
  geom_point(
    data = more_reduction_data,
    show.legend = FALSE,
    color = cool_gray1
  ) +
  geom_line(
    data = more_reduction_data,
    aes(group = entity),
    color = cool_gray1,
    show.legend = FALSE
  ) +
  # add less
  geom_point(
    data = less_reduction_data,
    show.legend = FALSE,
    color = cool_red1,
  ) +
  geom_line(
    data = less_reduction_data,
    aes(group = entity),
    color = cool_red1,
    show.legend = FALSE
  ) +
  # add name
  geom_text(
    data = more_reduction_data |> filter(limit == 'first 3 records'),
    aes(label = entity),
    hjust = 1,
    nudge_x = -0.05,
    size = 3,
    color = cool_gray1
  ) +
  geom_text(
    data = less_reduction_data |> filter(limit == 'first 3 records'),
    aes(label = entity),
    hjust = 1,
    nudge_x = -0.05,
    size = 3,
    color = cool_red1
  ) +
  # add change
  geom_text(
    data = more_reduction_data |>
      left_join(reduction_change, by = 'entity') |>
      filter(limit == 'last 3 records'),
    aes(label = percent(reduction_change, accuracy = 0.1)),
    hjust = 0,
    nudge_x = 0.05,
    size = 3,
    color = cool_gray1
  ) +
  geom_text(
    data = less_reduction_data |>
      left_join(reduction_change, by = 'entity') |>
      filter(limit == 'last 3 records'),
    aes(label = percent(reduction_change, accuracy = 0.1)),
    hjust = 0,
    nudge_x = 0.05,
    size = 3,
    color = cool_red1
  ) +
  # Labels
  labs(
    x = NULL,
    y = "**Income inequality reduction by taxes**<br>(Gini coefficient)",
    title = "The Redistributive Power of Government:\nIncome Inequality Before and After Taxes",
    subtitle = str_wrap("The Gini coefficient is a widely used measure of income inequality, but its value may be affected by the inclusion of taxes in the calculation. Here we compare the reduction of the Gini coefficient after taxes from the earlier years to the later year and take a closer look to countries with higher and lower inequality reduction change. Solid color bars represent the Gini coefficient before taxes, while dashed color bars represent the Gini coefficient before taxes.", width = 110),
    caption = str_wrap(
      "NOTE This visualization offers a preliminary look at the data and may not capture the full complexity of the underlying reality. SOURCE #Tidytuesday 2025-06-17 GITHUB barreiro-r",
      width = 120,
    )
  ) +
  scale_y_continuous(
    label = percent,
    limits = c(0, .5)
  ) +
  scale_x_discrete(
    label = c(
      `first 3 records` = 'First 3 records',
      `last 3 records` = 'Last 3 records'
    )
  )


# --- Bar plot

data2plot2_more_reduction <-
  bind_rows(
    data2plot2 |>
      filter(entity %in% c(more_reduction_data$entity)) |>
      slice_max(year, n = 3) |>
      mutate(limit = 'first 3 records'),
    data2plot2 |>
      filter(entity %in% c(more_reduction_data$entity)) |>
      slice_min(year, n = 3) |>
      mutate(limit = 'last 3 records')
  )

data2plot2_less_reduction <-
  bind_rows(
    data2plot2 |>
      filter(entity %in% c(less_reduction_data$entity)) |>
      slice_max(year, n = 3) |>
      mutate(limit = 'first 3 records'),
    data2plot2 |>
      filter(entity %in% c(less_reduction_data$entity)) |>
      slice_min(year, n = 3) |>
      mutate(limit = 'last 3 records')
  )

data2plot3 <-
  bind_rows(data2plot2_less_reduction, data2plot2_more_reduction) |>
  arrange(entity, year) |>
  group_by(entity) |>
  mutate(bar_position = row_number()) |>
  # create a gap
  mutate(
    bar_position = if_else(bar_position > 3, bar_position + 1, bar_position)
  ) |>
  ungroup() |>
  pivot_longer(
    cols = c(gini_pre_tax, gini_pos_tax),
    names_to = 'tax',
    values_to = 'gini'
  ) |>
  mutate(
    entity = factor(
      entity,
      levels = c(less_reduction_country, more_reduction_country)
    )
  ) |>
  mutate(limit = factor(limit, levels = c('first 3 records', 'last 3 records')))

p2 <-
  data2plot3 |>
  ggplot(aes(x = bar_position, y = gini)) +
  geom_col_pattern(
    data = subset(data2plot3, tax == 'gini_pre_tax'),
    pattern = "stripe",
    aes(pattern_fill = entity),
    color = NA,
    pattern_color = NA,
    fill = 'white',
    pattern_spacing = .02,
    pattern_angle = 45,
    show.legend = FALSE
  ) +
  geom_col(
    data = subset(data2plot3, tax == 'gini_pos_tax'),
    aes(fill = entity),
    show.legend = FALSE
  ) +
  geom_text(
    data = subset(data2plot3, tax == 'gini_pos_tax'),
    aes(label = year),
    angle = 90,
    y = 0.01,
    hjust = 0,
    color = "white",
    size = 2.5
  ) +
  facet_wrap(vars(entity)) +
  labs(
    x = NULL,
    y = "**Income inequality** (Gini coefficient)",
  ) +
  scale_y_continuous(
    expand = c(0,0,0,0)
  ) +
  scale_fill_manual(values = c(cool_red1, cool_gray1)) +
  scale_pattern_fill_manual(values = c(cool_red1, cool_gray1)) +
  theme(
    axis.text.x = element_blank(),
    strip.text = element_text(family = "Ubuntu", size = 8, color = cool_gray0),
  )


# --- Combine
p1 + p2 + plot_layout(widths = c(1.7, 2))