World Inequality Database - WID

Data

Author

François Geerolf

Variable, age, pop

Code 
WID_data_FR %>%
  group_by(variable, age, pop) %>%
  summarise(Nobs = n()) %>%
  arrange(-Nobs) %>%
  print_table_conditional()

percentile

Code 
WID_data_FR %>%
  group_by(percentile) %>%
  summarise(Nobs = n()) %>%
  arrange(-Nobs) %>%
  print_table_conditional()

type

Code 
WID_metadata_FR %>%
  mutate(type = substr(variable, 1, 1)) %>%
  group_by(type, shorttype, longtype) %>%
  summarise(Nobs = n()) %>%
  arrange(-Nobs) %>%
  print_table_conditional()
type shorttype longtype Nobs
a Average Average income or wealth between two percentiles. When the associated percentile is of the form 'pX', intermediary average returns the average between percentile pX and the next consecutive percentile. When the associated percentile is of the form 'pXpY', the variable returns the average between percentiles pX and pY. 678
m Total Macroeconomic variable (i.e. corresponding to national economy rather than to a given group of individuals). The associated percentile is of the form 'pall'. 327
w Wealth-income ratio Ratio of net wealth (of a given sector) to net national income 171
n Population Number of units in a given group 117
e Total emissions Aggregrate emissions 26
k Per-capita emissions Per capita emissions 26
s Share Income or wealth shares. When the associated percentile is of the form 'pX', the raw data stores the share of total income or wealth detained by all the population between threshold pX and the top of the distribution. When the associated percentile is of the form 'pXpY', the raw data stores the share of the population between thresholds pX and pY. 17
t Threshold Percentile (i.e. threshold ) value at pX, whether the percentile is of the form 'pX' or of the form 'pXpY' 17
g Gini coefficient The Gini coefficient is a measure of statistical dispersion. A coefficient of zero expresses perfect equality between individuals. A coefficient of one expresses maximal inequality, where only one person owns all the income or wealth of the economy. 15
b Beta coefficient The beta coefficient corresponds to the inverted Pareto-Lorenz coefficient. It is equal to mean income over a certain income level divided by this level. A coefficient b=2=200% for an income level 100 000 EUR means that the average income above 100 000 EUR is 200 000 EUR, a coefficient of 3 or 300% at income level 1 000 000 EUR means that the average income level above 1 000 000 EUR in a given population is 3 000 000 EUR. 14
r Top 10/Bottom 50 ratio Ratio of Top 10% average income to Bottom 50% average income 12
x Exchange rates Exchange rate series 6
l Average per capita group emissions Average per capita group emissions for percentile pXpX+1 or PXpY 4
i Indices Indices 3
f Female pop. Female population size in a given group. 1
p Proportion of women in group Proportion of women in a given income or wealth group. The value of ppllin996i for the percentile group p90p100 corresponds to the proportion of women in the Top 10% earners in terms of pretax labor income 1

age

Code 
WID_data_FR %>%
  left_join(age, by = "age") %>%
  group_by(age, shortage, longage) %>%
  summarise(Nobs = n()) %>%
  arrange(-Nobs) %>%
  print_table_conditional()

pop

Code 
WID_data_FR %>%
  left_join(pop, by = "pop") %>%
  group_by(pop, shortpop, longpop) %>%
  summarise(Nobs = n()) %>%
  arrange(-Nobs) %>%
  print_table_conditional()
pop shortpop longpop Nobs
j equal-split adults The base unit is the individual (rather than the household) but resources are split equally within couples. 544096
i individuals The base unit is the individual (rather than the household). This is equivalent to assuming no sharing of resources within couples. 223015
t tax unit The base unit is the tax unit defined by national fiscal administrations to measure personal income taxes. 187690
m male The population looked at is comprised of male individuals only. 80049
f female The population looked at is comprised of female individuals only. 78733

name

Code 
WID_data_FR %>%
  mutate(name = substr(variable, 2, 6)) %>%
  left_join(name, by = "name") %>%
  group_by(name, shortname, simpledes, technicaldes) %>%
  summarise(Nobs = n()) %>%
  arrange(-Nobs) %>%
  print_table_conditional()

country

Code 
country %>%
  mutate(Flag = gsub(" ", "-", str_to_lower(gsub(" ", "-", Country))),
         Flag = paste0('<img src="../../icon/flag/vsmall/', Flag, '.png" alt="Flag">')) %>%
  select(Flag, everything()) %>%
  {if (is_html_output()) datatable(., filter = 'top', rownames = F, escape = F) else .}

WID_metadata_FR

Tous

Code 
WID_metadata_FR %>%
  select(variable, shortname, shorttype, shortpop, shortage) %>%
  print_table_conditional

Pre-tax

Code 
WID_metadata_FR %>%
  filter(grepl("Pre-tax", shortname)) %>%
  select(variable, shortname, shorttype, shortpop, shortage) %>%
  print_table_conditional

Post-tax

Code 
WID_metadata_FR %>%
  filter(grepl("Post-tax", shortname)) %>%
  select(variable, shortname, shorttype, shortpop, shortage) %>%
  print_table_conditional
variable shortname shorttype shortpop shortage
acaincj992 Post-tax disposable income Average equal-split adults Adults
adiincj992 Post-tax national income Average equal-split adults Adults
bcaincj992 Post-tax disposable income Beta coefficient equal-split adults Adults
bdiincj992 Post-tax national income Beta coefficient equal-split adults Adults
gcaincj992 Post-tax disposable income Gini coefficient equal-split adults Adults
gdiincj992 Post-tax national income Gini coefficient equal-split adults Adults
rcaincj992 Post-tax disposable income Top 10/Bottom 50 ratio equal-split adults Adults
rdiincj992 Post-tax national income Top 10/Bottom 50 ratio equal-split adults Adults
scaincj992 Post-tax disposable income Share equal-split adults Adults
sdiincj992 Post-tax national income Share equal-split adults Adults
tcaincj992 Post-tax disposable income Threshold equal-split adults Adults
tdiincj992 Post-tax national income Threshold equal-split adults Adults

Table

Code 
WID_data_FR %>%
  filter(percentile == "p99.9p100") %>%
  group_by(variable, age, pop) %>%
  summarise(Nobs = n()) %>%
  arrange(-Nobs) %>%
  print_table_conditional

ptinc, diinc - Pre-tax/Post-tax national income

France, United Kingdom

Top 10%

Code 
load_data("wid/name_fr.RData")
WID_data_FR %>%
  bind_rows(WID_data_GB) %>%
  mutate(name = substr(variable, 2, 6),
         type = substr(variable, 1, 1)) %>%
  filter(name %in% c("ptinc", "diinc"),
         type == "s",
         pop == "j",
         percentile %in% c("p90p100")) %>%
  year_to_date2() %>%
  filter(date >= as.Date("1914-01-01")) %>%
  left_join(country, by = "country") %>%
  left_join(name, by = "name") %>%
  rename(Location = Country) %>%
  left_join(colors, by = c("Location" = "country")) %>%
  ggplot + geom_line(aes(x = date, y = value, color = color, linetype = shortname)) +
  scale_color_identity() + ylab("Part du revenu national du 10% les + riches (%)") +
  xlab("") + theme_minimal() + add_4flags +
  scale_y_continuous(breaks = 0.01*seq(0, 100, 2),
                     labels = scales::percent_format(accuracy = 1)) +
  scale_x_date(breaks = seq(1800, 2025, 10) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  theme(legend.position = c(0.6, 0.9),
        legend.title = element_blank())

Top 1%

Code 
WID_data_DE %>%
  bind_rows(WID_data_FR) %>%
  bind_rows(WID_data_GB) %>%
  mutate(name = substr(variable, 2, 6),
         type = substr(variable, 1, 1)) %>%
  filter(name %in% c("ptinc", "diinc"),
         type == "s",
         pop == "j",
         percentile %in% c("p99p100"),
         country %in% c("FR", "GB")) %>%
  year_to_date2() %>%
  filter(date >= as.Date("1914-01-01")) %>%
  left_join(country, by = "country") %>%
  left_join(name, by = "name") %>%
  rename(Location = Country) %>%
  left_join(colors, by = c("Location" = "country")) %>%
  
  ggplot + geom_line(aes(x = date, y = value, color = color, linetype = shortname)) +
  scale_color_identity() + ylab("Part du revenu national du 1% les + riches (%)") +
  xlab("") + theme_minimal() + add_4flags +
  scale_y_continuous(breaks = 0.01*seq(0, 100, 2),
                     labels = scales::percent_format(accuracy = 1)) +
  scale_x_date(breaks = seq(1800, 2025, 10) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  theme(legend.position = c(0.6, 0.9),
        legend.title = element_blank())

Top 0.1%

Code 
WID_data_DE %>%
  bind_rows(WID_data_FR) %>%
  bind_rows(WID_data_GB) %>%
  mutate(name = substr(variable, 2, 6),
         type = substr(variable, 1, 1)) %>%
  filter(name %in% c("ptinc", "diinc"),
         type == "s",
         pop == "j",
         percentile %in% c("p99.9p100"),
         country %in% c("FR", "GB")) %>%
  year_to_date2() %>%
  filter(date >= as.Date("1914-01-01")) %>%
  left_join(country, by = "country") %>%
  left_join(name, by = "name") %>%
  rename(Location = Country) %>%
  left_join(colors, by = c("Location" = "country")) %>%
  
  ggplot + geom_line(aes(x = date, y = value, color = color, linetype = shortname)) +
  scale_color_identity() + ylab("Part du revenu national du 0.1% les + riches (%)") +
  xlab("") + theme_minimal() + add_4flags +
  scale_y_continuous(breaks = 0.01*seq(0, 100, 2),
                     labels = scales::percent_format(accuracy = 1)) +
  scale_x_date(breaks = seq(1800, 2025, 10) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  theme(legend.position = c(0.6, 0.9),
        legend.title = element_blank())

Top 0.01%

All

Code 
WID_data_DE %>%
  bind_rows(WID_data_FR) %>%
  bind_rows(WID_data_GB) %>%
  mutate(name = substr(variable, 2, 6),
         type = substr(variable, 1, 1)) %>%
  filter(name %in% c("ptinc", "diinc"),
         type == "s",
         pop == "j",
         percentile %in% c("p99.99p100"),
         country %in% c("FR", "GB")) %>%
  year_to_date2() %>%
  filter(date >= as.Date("1914-01-01")) %>%
  left_join(country, by = "country") %>%
  left_join(name, by = "name") %>%
  rename(Location = Country) %>%
  left_join(colors, by = c("Location" = "country")) %>%
  
  ggplot + geom_line(aes(x = date, y = value, color = color, linetype = shortname)) +
  scale_color_identity() + ylab("Part du revenu national du 0.01% les + riches (%)") +
  xlab("") + theme_minimal() + add_4flags +
  scale_y_continuous(breaks = 0.01*seq(0, 100, 1),
                     labels = scales::percent_format(accuracy = 1)) +
  scale_x_date(breaks = seq(1800, 2025, 10) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  theme(legend.position = c(0.6, 0.9),
        legend.title = element_blank())

1980-

Code 
WID_data_DE %>%
  bind_rows(WID_data_FR) %>%
  bind_rows(WID_data_GB) %>%
  mutate(name = substr(variable, 2, 6),
         type = substr(variable, 1, 1)) %>%
  filter(name %in% c("ptinc", "diinc"),
         type == "s",
         pop == "j",
         percentile %in% c("p99.99p100"),
         country %in% c("FR", "GB")) %>%
  year_to_date2() %>%
  filter(date >= as.Date("1980-01-01")) %>%
  left_join(country, by = "country") %>%
  left_join(name, by = "name") %>%
  rename(Location = Country) %>%
  left_join(colors, by = c("Location" = "country")) %>%
  
  ggplot + geom_line(aes(x = date, y = value, color = color, linetype = shortname)) +
  scale_color_identity() + ylab("Part du revenu national du 0.01% les + riches (%)") +
  xlab("") + theme_minimal() + add_4flags +
  scale_y_continuous(breaks = 0.01*seq(0, 100, .2),
                     labels = scales::percent_format(accuracy = .1)) +
  scale_x_date(breaks = seq(1800, 2025, 5) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  theme(legend.position = c(0.2, 0.9),
        legend.title = element_blank())

Top 0.001%

Code 
WID_data_DE %>%
  bind_rows(WID_data_FR) %>%
  bind_rows(WID_data_GB) %>%
  mutate(name = substr(variable, 2, 6),
         type = substr(variable, 1, 1)) %>%
  filter(name %in% c("ptinc", "diinc"),
         type == "s",
         pop == "j",
         percentile %in% c("p99.999p100"),
         country %in% c("FR", "GB")) %>%
  year_to_date2() %>%
  filter(date >= as.Date("1914-01-01")) %>%
  left_join(country, by = "country") %>%
  left_join(name, by = "name") %>%
  rename(Location = Country) %>%
  left_join(colors, by = c("Location" = "country")) %>%
  
  ggplot + geom_line(aes(x = date, y = value, color = color, linetype = shortname)) +
  scale_color_identity() + ylab("Part du revenu national du 0.001% les + riches (%)") +
  xlab("") + theme_minimal() + add_4flags +
  scale_y_continuous(breaks = 0.01*seq(0, 100, 1),
                     labels = scales::percent_format(accuracy = 1)) +
  scale_x_date(breaks = seq(1800, 2025, 10) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  theme(legend.position = c(0.6, 0.9),
        legend.title = element_blank())

European Union, United States

Top 10%

all

Code 
WID_data_QY %>%
  bind_rows(WID_data_US) %>%
  mutate(name = substr(variable, 2, 6),
         type = substr(variable, 1, 1)) %>%
  filter(name %in% c("ptinc", "diinc"),
         type == "s",
         pop == "j",
         percentile %in% c("p90p100")) %>%
  year_to_date2() %>%
  filter(date >= as.Date("1914-01-01")) %>%
  left_join(country, by = "country") %>%
  left_join(name, by = "name") %>%
  rename(Location = Country) %>%
  left_join(colors, by = c("Location" = "country")) %>%
  mutate(color = ifelse(Location == "United States", color2, color)) %>%
  ggplot + geom_line(aes(x = date, y = value, color = color, linetype = shortname)) +
  scale_color_identity() + ylab("Part du revenu national du 10% les + riches (%)") +
  xlab("") + theme_minimal() + add_4flags +
  scale_y_continuous(breaks = 0.01*seq(0, 100, 2),
                     labels = scales::percent_format(accuracy = 1)) +
  scale_x_date(breaks = seq(1800, 2025, 10) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  theme(legend.position = c(0.6, 0.9),
        legend.title = element_blank())

1980-

Code 
WID_data_QY %>%
  bind_rows(WID_data_US) %>%
  mutate(name = substr(variable, 2, 6),
         type = substr(variable, 1, 1)) %>%
  filter(name %in% c("ptinc", "diinc"),
         type == "s",
         pop == "j",
         percentile %in% c("p90p100")) %>%
  year_to_date2() %>%
  filter(date >= as.Date("1980-01-01")) %>%
  left_join(country, by = "country") %>%
  left_join(name, by = "name") %>%
  rename(Location = Country) %>%
  left_join(colors, by = c("Location" = "country")) %>%
  mutate(color = ifelse(Location == "United States", color2, color)) %>%
  ggplot + geom_line(aes(x = date, y = value, color = color, linetype = shortname)) +
  scale_color_identity() + ylab("Part du revenu national du 10% les + riches (%)") +
  xlab("") + theme_minimal() + add_4flags +
  scale_y_continuous(breaks = 0.01*seq(0, 100, 2),
                     labels = scales::percent_format(accuracy = 1)) +
  scale_x_date(breaks = c(seq(1979, 2100, 5), seq(1977, 2100, 5)) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  theme(legend.position = c(0.6, 0.9),
        legend.title = element_blank())

1999-

Code 
WID_data_QY %>%
  bind_rows(WID_data_US) %>%
  mutate(name = substr(variable, 2, 6),
         type = substr(variable, 1, 1)) %>%
  filter(name %in% c("ptinc", "diinc"),
         type == "s",
         pop == "j",
         percentile %in% c("p90p100")) %>%
  year_to_date2() %>%
  filter(date >= as.Date("1999-01-01")) %>%
  left_join(country, by = "country") %>%
  left_join(name, by = "name") %>%
  rename(Location = Country) %>%
  left_join(colors, by = c("Location" = "country")) %>%
  mutate(color = ifelse(Location == "United States", color2, color)) %>%
  ggplot + geom_line(aes(x = date, y = value, color = color, linetype = shortname)) +
  scale_color_identity() + ylab("Part du revenu national du 10% les + riches (%)") +
  xlab("") + theme_minimal() + add_4flags +
  scale_y_continuous(breaks = 0.01*seq(0, 100, 2),
                     labels = scales::percent_format(accuracy = 1)) +
  scale_x_date(breaks = c(seq(1999, 2100, 5), seq(1997, 2100, 5)) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  theme(legend.position = c(0.2, 0.9),
        legend.title = element_blank())

Top 1%

Code 
WID_data_QY %>%
  bind_rows(WID_data_US) %>%
  mutate(name = substr(variable, 2, 6),
         type = substr(variable, 1, 1)) %>%
  filter(name %in% c("ptinc", "diinc"),
         type == "s",
         pop == "j",
         percentile %in% c("p99p100")) %>%
  year_to_date2() %>%
  filter(date >= as.Date("1914-01-01")) %>%
  left_join(country, by = "country") %>%
  left_join(name, by = "name") %>%
  rename(Location = Country) %>%
  left_join(colors, by = c("Location" = "country")) %>%
  
  ggplot + geom_line(aes(x = date, y = value, color = color, linetype = shortname)) +
  scale_color_identity() + ylab("Part du revenu national du 1% les + riches (%)") +
  xlab("") + theme_minimal() + add_4flags +
  scale_y_continuous(breaks = 0.01*seq(0, 100, 2),
                     labels = scales::percent_format(accuracy = 1)) +
  scale_x_date(breaks = seq(1800, 2025, 10) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  theme(legend.position = c(0.6, 0.9),
        legend.title = element_blank())

Top 0.1%

Code 
WID_data_QY %>%
  bind_rows(WID_data_US) %>%
  mutate(name = substr(variable, 2, 6),
         type = substr(variable, 1, 1)) %>%
  filter(name %in% c("ptinc", "diinc"),
         type == "s",
         pop == "j",
         percentile %in% c("p99.9p100")) %>%
  year_to_date2() %>%
  filter(date >= as.Date("1914-01-01")) %>%
  left_join(country, by = "country") %>%
  left_join(name, by = "name") %>%
  rename(Location = Country) %>%
  left_join(colors, by = c("Location" = "country")) %>%
  
  ggplot + geom_line(aes(x = date, y = value, color = color, linetype = shortname)) +
  scale_color_identity() + ylab("Part du revenu national du 0.1% les + riches (%)") +
  xlab("") + theme_minimal() + add_4flags +
  scale_y_continuous(breaks = 0.01*seq(0, 100, 1),
                     labels = scales::percent_format(accuracy = 1)) +
  scale_x_date(breaks = seq(1800, 2025, 10) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  theme(legend.position = c(0.6, 0.9),
        legend.title = element_blank())

Top 0.01%

Code 
WID_data_QY %>%
  bind_rows(WID_data_US) %>%
  mutate(name = substr(variable, 2, 6),
         type = substr(variable, 1, 1)) %>%
  filter(name %in% c("ptinc", "diinc"),
         type == "s",
         pop == "j",
         percentile %in% c("p99.99p100")) %>%
  year_to_date2() %>%
  filter(date >= as.Date("1914-01-01")) %>%
  left_join(country, by = "country") %>%
  left_join(name, by = "name") %>%
  rename(Location = Country) %>%
  left_join(colors, by = c("Location" = "country")) %>%
  
  ggplot + geom_line(aes(x = date, y = value, color = color, linetype = shortname)) +
  scale_color_identity() + ylab("Part du revenu national du 0.01% les + riches (%)") +
  xlab("") + theme_minimal() + add_4flags +
  scale_y_continuous(breaks = 0.01*seq(0, 100, 1),
                     labels = scales::percent_format(accuracy = 1)) +
  scale_x_date(breaks = seq(1800, 2025, 10) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  theme(legend.position = c(0.6, 0.9),
        legend.title = element_blank())

Top 0.001%

Code 
WID_data_QY %>%
  bind_rows(WID_data_US) %>%
  mutate(name = substr(variable, 2, 6),
         type = substr(variable, 1, 1)) %>%
  filter(name %in% c("ptinc", "diinc"),
         type == "s",
         pop == "j",
         percentile %in% c("p99.999p100")) %>%
  year_to_date2() %>%
  filter(date >= as.Date("1914-01-01")) %>%
  left_join(country, by = "country") %>%
  left_join(name, by = "name") %>%
  rename(Location = Country) %>%
  left_join(colors, by = c("Location" = "country")) %>%
  
  ggplot + geom_line(aes(x = date, y = value, color = color, linetype = shortname)) +
  scale_color_identity() + ylab("Part du revenu national du 0.001% les + riches (%)") +
  xlab("") + theme_minimal() + add_4flags +
  scale_y_continuous(breaks = 0.01*seq(0, 100, 1),
                     labels = scales::percent_format(accuracy = 1)) +
  scale_x_date(breaks = seq(1800, 2025, 10) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  theme(legend.position = c(0.6, 0.9),
        legend.title = element_blank())

France, United States

Top 10%

Code 
WID_data_DE %>%
  bind_rows(WID_data_FR) %>%
  bind_rows(WID_data_US) %>%
  mutate(name = substr(variable, 2, 6),
         type = substr(variable, 1, 1)) %>%
  filter(name %in% c("ptinc", "diinc"),
         type == "s",
         pop == "j",
         percentile %in% c("p90p100"),
         country %in% c("FR", "US")) %>%
  year_to_date2() %>%
  filter(date >= as.Date("1914-01-01")) %>%
  left_join(country, by = "country") %>%
  left_join(name, by = "name") %>%
  rename(Location = Country) %>%
  left_join(colors, by = c("Location" = "country")) %>%
  ggplot + geom_line(aes(x = date, y = value, color = color, linetype = shortname)) +
  scale_color_identity() + ylab("Part du revenu national du 10% les + riches (%)") +
  xlab("") + theme_minimal() + add_4flags +
  scale_y_continuous(breaks = 0.01*seq(0, 100, 2),
                     labels = scales::percent_format(accuracy = 1)) +
  scale_x_date(breaks = seq(1800, 2025, 10) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  theme(legend.position = c(0.6, 0.9),
        legend.title = element_blank())

Top 1%

all

Code 
WID_data_DE %>%
  bind_rows(WID_data_FR) %>%
  bind_rows(WID_data_US) %>%
  mutate(name = substr(variable, 2, 6),
         type = substr(variable, 1, 1)) %>%
  filter(name %in% c("ptinc", "diinc"),
         type == "s",
         pop == "j",
         percentile %in% c("p99p100"),
         country %in% c("FR", "US")) %>%
  year_to_date2() %>%
  filter(date >= as.Date("1914-01-01")) %>%
  left_join(country, by = "country") %>%
  left_join(name, by = "name") %>%
  rename(Location = Country) %>%
  left_join(colors, by = c("Location" = "country")) %>%
  ggplot + geom_line(aes(x = date, y = value, color = color, linetype = shortname)) +
  scale_color_identity() + ylab("Part du revenu national du 1% les + riches (%)") +
  xlab("") + theme_minimal() + add_4flags +
  scale_y_continuous(breaks = 0.01*seq(0, 100, 2),
                     labels = scales::percent_format(accuracy = 1)) +
  scale_x_date(breaks = seq(1800, 2025, 10) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  theme(legend.position = c(0.6, 0.9),
        legend.title = element_blank())

1999-

Code 
WID_data_DE %>%
  bind_rows(WID_data_FR) %>%
  bind_rows(WID_data_US) %>%
  mutate(name = substr(variable, 2, 6),
         type = substr(variable, 1, 1)) %>%
  filter(name %in% c("ptinc", "diinc"),
         type == "s",
         pop == "j",
         percentile %in% c("p99p100"),
         country %in% c("FR", "US")) %>%
  year_to_date2() %>%
  filter(date >= as.Date("1999-01-01")) %>%
  left_join(country, by = "country") %>%
  left_join(name, by = "name") %>%
  rename(Location = Country) %>%
  left_join(colors, by = c("Location" = "country")) %>%
  ggplot + geom_line(aes(x = date, y = value, color = color, linetype = shortname)) +
  scale_color_identity() + ylab("Part du revenu national du 1% les + riches (%)") +
  xlab("") + theme_minimal() + add_4flags +
  scale_y_continuous(breaks = 0.01*seq(0, 100, 2),
                     labels = scales::percent_format(accuracy = 1)) +
  scale_x_date(breaks = seq(1999, 2025, 5) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  theme(legend.position = c(0.6, 0.9),
        legend.title = element_blank())

Top 0.1%

Code 
load_data("wid/name_fr.RData")
WID_data_DE %>%
  bind_rows(WID_data_FR) %>%
  bind_rows(WID_data_US) %>%
  mutate(name = substr(variable, 2, 6),
         type = substr(variable, 1, 1)) %>%
  filter(name %in% c("ptinc", "diinc"),
         type == "s",
         pop == "j",
         percentile %in% c("p99.9p100"),
         country %in% c("FR", "US")) %>%
  year_to_date2() %>%
  filter(date >= as.Date("1914-01-01")) %>%
  left_join(country, by = "country") %>%
  left_join(name, by = "name") %>%
  rename(Location = Country) %>%
  left_join(colors, by = c("Location" = "country")) %>%
  ggplot + geom_line(aes(x = date, y = value, color = color, linetype = shortname)) +
  scale_color_identity() + ylab("Part du revenu national du 0.1% les + riches (%)") +
  xlab("") + theme_minimal() + add_4flags +
  scale_y_continuous(breaks = 0.01*seq(0, 100, 1),
                     labels = scales::percent_format(accuracy = 1)) +
  scale_x_date(breaks = seq(1800, 2025, 10) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  theme(legend.position = c(0.6, 0.9),
        legend.title = element_blank())

Top 0.01%

Code 
WID_data_DE %>%
  bind_rows(WID_data_FR) %>%
  bind_rows(WID_data_US) %>%
  mutate(name = substr(variable, 2, 6),
         type = substr(variable, 1, 1)) %>%
  filter(name %in% c("ptinc", "diinc"),
         type == "s",
         pop == "j",
         percentile %in% c("p99.99p100"),
         country %in% c("FR", "US")) %>%
  year_to_date2() %>%
  filter(date >= as.Date("1914-01-01")) %>%
  left_join(country, by = "country") %>%
  left_join(name, by = "name") %>%
  rename(Location = Country) %>%
  left_join(colors, by = c("Location" = "country")) %>%
  ggplot + geom_line(aes(x = date, y = value, color = color, linetype = shortname)) +
  scale_color_identity() + ylab("Part du revenu national du 0.01% les + riches (%)") +
  xlab("") + theme_minimal() + add_4flags +
  scale_y_continuous(breaks = 0.01*seq(0, 100, 1),
                     labels = scales::percent_format(accuracy = 1)) +
  scale_x_date(breaks = seq(1800, 2025, 10) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  theme(legend.position = c(0.6, 0.9),
        legend.title = element_blank())

Top 0.001%

Code 
WID_data_DE %>%
  bind_rows(WID_data_FR) %>%
  bind_rows(WID_data_US) %>%
  mutate(name = substr(variable, 2, 6),
         type = substr(variable, 1, 1)) %>%
  filter(name %in% c("ptinc", "diinc"),
         type == "s",
         pop == "j",
         percentile %in% c("p99.999p100"),
         country %in% c("FR", "US")) %>%
  year_to_date2() %>%
  filter(date >= as.Date("1914-01-01")) %>%
  left_join(country, by = "country") %>%
  left_join(name, by = "name") %>%
  rename(Location = Country) %>%
  left_join(colors, by = c("Location" = "country")) %>%
  ggplot + geom_line(aes(x = date, y = value, color = color, linetype = shortname)) +
  scale_color_identity() + ylab("Part du revenu national du 0.001% les + riches (%)") +
  xlab("") + theme_minimal() + add_4flags +
  scale_y_continuous(breaks = 0.01*seq(0, 100, .2),
                     labels = scales::percent_format(accuracy = .1)) +
  scale_x_date(breaks = seq(1800, 2025, 10) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  theme(legend.position = c(0.6, 0.9),
        legend.title = element_blank())

Top 1%, Top 10%

Code 
WID_data_DE %>%
  bind_rows(WID_data_FR) %>%
  bind_rows(WID_data_US) %>%
  mutate(name = substr(variable, 2, 6),
         type = substr(variable, 1, 1)) %>%
  filter(name %in% c("ptinc", "diinc"),
         type == "s",
         pop == "j",
         percentile %in% c("p99p100", "p90p100"),
         country %in% c("FR", "US")) %>%
  year_to_date2() %>%
  filter(date >= as.Date("1914-01-01")) %>%
  left_join(country, by = "country") %>%
  left_join(name, by = "name") %>%
  rename(Location = Country) %>%
  left_join(colors, by = c("Location" = "country")) %>%
  ggplot + geom_line(aes(x = date, y = value, color = color, linetype = shortname, linetype2 = percentile)) +
  scale_color_identity() + ylab("Part du revenu national (%)") + xlab("") + theme_minimal() +
  add_8flags +
  scale_y_continuous(breaks = 0.01*seq(0, 100, 5),
                     labels = scales::percent_format(accuracy = 1)) +
  scale_x_date(breaks = seq(1800, 2025, 10) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  theme(legend.position = c(0.6, 0.9),
        legend.title = element_blank()) +
 annotate(geom="text", x = as.Date("1925-01-01"), y = 0.08, label = "par les 1% les + riches", size=3, fontface="bold") +
 annotate(geom="text", x = as.Date("1925-01-01"), y = 0.10, label = "Part du revenu détenue", size=3, fontface="bold") +
 annotate(geom="text", x = as.Date("1926-01-01"), y = 0.34, label = "par les 10% les + riches", size=3, fontface="bold") +
 annotate(geom="text", x = as.Date("1926-01-01"), y = 0.36, label = "Part du revenu détenue", size=3, fontface="bold")

France, Germany

All

Code 
WID_data_DE %>%
  bind_rows(WID_data_FR) %>%
  bind_rows(WID_data_US) %>%
  mutate(name = substr(variable, 2, 6),
         type = substr(variable, 1, 1)) %>%
  filter(name %in% c("ptinc", "diinc"),
         type == "s",
         pop == "j",
         percentile %in% c("p99p100", "p90p100"),
         country %in% c("FR", "DE")) %>%
  year_to_date2() %>%
  filter(date >= as.Date("1914-01-01")) %>%
  left_join(country, by = "country") %>%
  left_join(name, by = "name") %>%
  rename(Location = Country) %>%
  left_join(colors, by = c("Location" = "country")) %>%
  ggplot + geom_line(aes(x = date, y = value, color = color, linetype = shortname, linetype2 = percentile)) +
  scale_color_identity() + ylab("Part du revenu national (%)") + xlab("") + theme_minimal() +
  add_8flags +
  scale_y_continuous(breaks = 0.01*seq(0, 100, 5),
                     labels = scales::percent_format(accuracy = 1)) +
  scale_x_date(breaks = seq(1800, 2025, 10) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  theme(legend.position = c(0.6, 0.9),
        legend.title = element_blank()) +
 annotate(geom="text", x = as.Date("1925-01-01"), y = 0.08, label = "par les 1% les + riches", size=3, fontface="bold") +
 annotate(geom="text", x = as.Date("1925-01-01"), y = 0.10, label = "Part du revenu détenue", size=3, fontface="bold") +
 annotate(geom="text", x = as.Date("1926-01-01"), y = 0.34, label = "par les 10% les + riches", size=3, fontface="bold") +
 annotate(geom="text", x = as.Date("1926-01-01"), y = 0.36, label = "Part du revenu détenue", size=3, fontface="bold")

1999-

Code 
WID_data_DE %>%
  bind_rows(WID_data_FR) %>%
  bind_rows(WID_data_US) %>%
  mutate(name = substr(variable, 2, 6),
         type = substr(variable, 1, 1)) %>%
  filter(name %in% c("ptinc", "diinc"),
         type == "s",
         pop == "j",
         percentile %in% c("p99p100", "p90p100"),
         country %in% c("FR", "DE")) %>%
  year_to_date2() %>%
  filter(date >= as.Date("1999-01-01")) %>%
  left_join(country, by = "country") %>%
  left_join(name, by = "name") %>%
  rename(Location = Country) %>%
  left_join(colors, by = c("Location" = "country")) %>%
  ggplot + geom_line(aes(x = date, y = value, color = color, linetype = shortname, linetype2 = percentile)) +
  scale_color_identity() + ylab("Part du revenu national (%)") + xlab("") + theme_minimal() +
  add_8flags +
  scale_y_continuous(breaks = 0.01*seq(0, 100, 5),
                     labels = scales::percent_format(accuracy = 1)) +
  scale_x_date(breaks = c(seq(1999, 2025, 5), seq(1997, 2025, 5)) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  theme(legend.position = c(0.6, 0.9),
        legend.title = element_blank()) +
 annotate(geom="text", x = as.Date("2004-01-01"), y = 0.16, label = "par les 1% les + riches", size=3, fontface="bold") +
 annotate(geom="text", x = as.Date("2004-01-01"), y = 0.18, label = "Part du revenu détenue", size=3, fontface="bold") +
 annotate(geom="text", x = as.Date("2004-01-01"), y = 0.38, label = "par les 10% les + riches", size=3, fontface="bold") +
 annotate(geom="text", x = as.Date("2004-01-01"), y = 0.40, label = "Part du revenu détenue", size=3, fontface="bold")

United States, Germany

Code 
WID_data_DE %>%
  bind_rows(WID_data_FR) %>%
  bind_rows(WID_data_US) %>%
  mutate(name = substr(variable, 2, 6),
         type = substr(variable, 1, 1)) %>%
  filter(name %in% c("ptinc", "diinc"),
         type == "s",
         pop == "j",
         percentile %in% c("p99p100", "p90p100"),
         country %in% c("US", "DE")) %>%
  year_to_date2() %>%
  filter(date >= as.Date("1914-01-01")) %>%
  left_join(country, by = "country") %>%
  rename(Location = Country) %>%
  left_join(colors, by = c("Location" = "country")) %>%
  left_join(name, by = "name") %>%
  mutate(color = ifelse(country == "US", color2, color)) %>%
  ggplot + geom_line(aes(x = date, y = value, color = color, linetype = shortname, linetype2 = percentile)) +
  scale_color_identity() + ylab("Part du revenu national (%)") + xlab("") + theme_minimal() +
  add_8flags +
  scale_y_continuous(breaks = 0.01*seq(0, 100, 5),
                     labels = scales::percent_format(accuracy = 1)) +
  scale_x_date(breaks = seq(1800, 2025, 10) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  theme(legend.position = c(0.6, 0.9),
        legend.title = element_blank()) +
 annotate(geom="text", x = as.Date("1925-01-01"), y = 0.08, label = "par les 1% les + riches", size=3, fontface="bold") +
 annotate(geom="text", x = as.Date("1925-01-01"), y = 0.10, label = "Part du revenu détenue", size=3, fontface="bold") +
 annotate(geom="text", x = as.Date("1926-01-01"), y = 0.34, label = "par les 10% les + riches", size=3, fontface="bold") +
 annotate(geom="text", x = as.Date("1926-01-01"), y = 0.36, label = "Part du revenu détenue", size=3, fontface="bold")

Comparaison France-US, 1% versus 10%

Tous

Automatique

Code 
WID_Data_29122021 %>%
  mutate(date = paste0(year, "-01-01") %>% as.Date) %>%
  filter(date >= as.Date("1914-01-01")) %>%
  mutate(Location = ifelse(Location == "USA", "United States", Location),
         pre_post = case_when(grepl("Post-tax", Variable) ~ "Après redistribution",
                              grepl("Pre-tax", Variable) ~ "Avant redistribution"),
         top = case_when(grepl("Top 1%", Variable) ~ "Top 1%",
                              grepl("Top 10%", Variable) ~ "Top 10%")) %>%
  left_join(colors, by = c("Location" = "country")) %>%
  
  ggplot + geom_line(aes(x = date, y = value, color = color, linetype = pre_post, linetype2 = top)) +
  scale_color_identity() + ylab("Part du revenu national (%)") + xlab("") + theme_minimal() +
  add_8flags +
  scale_y_continuous(breaks = 0.01*seq(0, 100, 5),
                     labels = scales::percent_format(accuracy = 1)) +
  scale_x_date(breaks = seq(1800, 2025, 10) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  theme(legend.position = c(0.6, 0.9),
        legend.title = element_blank()) +
 annotate(geom="text", x = as.Date("1925-01-01"), y = 0.08, label = "par les 1% les + riches", size=3, fontface="bold") +
 annotate(geom="text", x = as.Date("1925-01-01"), y = 0.10, label = "Part du revenu détenue", size=3, fontface="bold") +
 annotate(geom="text", x = as.Date("1926-01-01"), y = 0.34, label = "par les 10% les + riches", size=3, fontface="bold") +
 annotate(geom="text", x = as.Date("1926-01-01"), y = 0.36, label = "Part du revenu détenue", size=3, fontface="bold")

Commencer à 0

all

Code 
WID_Data_29122021 %>%
  mutate(date = paste0(year, "-01-01") %>% as.Date) %>%
  filter(date >= as.Date("1914-01-01")) %>%
  mutate(Location = ifelse(Location == "USA", "United States", Location),
         pre_post = case_when(grepl("Post-tax", Variable) ~ "Après redistribution",
                              grepl("Pre-tax", Variable) ~ "Avant redistribution"),
         top = case_when(grepl("Top 1%", Variable) ~ "Top 1%",
                              grepl("Top 10%", Variable) ~ "Top 10%")) %>%
  left_join(colors, by = c("Location" = "country")) %>%
  
  ggplot + geom_line(aes(x = date, y = value, color = color, linetype = pre_post, linetype2 = top)) +
  scale_color_identity() + ylab("Part du revenu national (%)") + xlab("") + theme_minimal() +
  add_8flags +
  scale_y_continuous(breaks = 0.01*seq(0, 100, 5),
                     labels = scales::percent_format(accuracy = 1),
                     limits = c(0, 0.52)) +
  scale_x_date(breaks = seq(1800, 2025, 10) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  theme(legend.position = c(0.6, 0.9),
        legend.title = element_blank()) +
 annotate(geom="text", x = as.Date("1925-01-01"), y = 0.08, label = "par les 1% les + riches", size=3, fontface="bold") +
 annotate(geom="text", x = as.Date("1925-01-01"), y = 0.10, label = "Part du revenu détenue", size=3, fontface="bold") +
 annotate(geom="text", x = as.Date("1926-01-01"), y = 0.34, label = "par les 10% les + riches", size=3, fontface="bold") +
 annotate(geom="text", x = as.Date("1926-01-01"), y = 0.36, label = "Part du revenu détenue", size=3, fontface="bold")

1999

Code 
WID_Data_29122021 %>%
  mutate(date = paste0(year, "-01-01") %>% as.Date) %>%
  filter(date >= as.Date("1914-01-01")) %>%
  mutate(Location = ifelse(Location == "USA", "United States", Location),
         pre_post = case_when(grepl("Post-tax", Variable) ~ "Après redistribution",
                              grepl("Pre-tax", Variable) ~ "Avant redistribution"),
         top = case_when(grepl("Top 1%", Variable) ~ "Top 1%",
                              grepl("Top 10%", Variable) ~ "Top 10%")) %>%
  left_join(colors, by = c("Location" = "country")) %>%
  filter(date>= as.Date("1999-01-01")) %>%
  ggplot + geom_line(aes(x = date, y = value, color = color, linetype = pre_post, linetype2 = top)) +
  scale_color_identity() + ylab("Part du revenu national (%)") + xlab("") + theme_minimal() +
  add_8flags +
  scale_y_continuous(breaks = 0.01*seq(0, 100, 5),
                     labels = scales::percent_format(accuracy = 1),
                     limits = c(0, 0.52)) +
  scale_x_date(breaks = seq(1800, 2025, 10) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  theme(legend.position = c(0.6, 0.9),
        legend.title = element_blank()) +
 annotate(geom="text", x = as.Date("2005-01-01"), y = 0.08, label = "par les 1% les + riches", size=3, fontface="bold") +
 annotate(geom="text", x = as.Date("2005-01-01"), y = 0.10, label = "Part du revenu détenue", size=3, fontface="bold") +
 annotate(geom="text", x = as.Date("2005-01-01"), y = 0.34, label = "par les 10% les + riches", size=3, fontface="bold") +
 annotate(geom="text", x = as.Date("2005-01-01"), y = 0.36, label = "Part du revenu détenue", size=3, fontface="bold")

Pré-redistribution

Automatique

Code 
WID_Data_29122021 %>%
  mutate(date = paste0(year, "-01-01") %>% as.Date) %>%
  filter(date >= as.Date("1914-01-01")) %>%
  mutate(Location = ifelse(Location == "USA", "United States", Location),
         pre_post = case_when(grepl("Post-tax", Variable) ~ "Après redistribution",
                              grepl("Pre-tax", Variable) ~ "Avant redistribution"),
         top = case_when(grepl("Top 1%", Variable) ~ "Top 1%",
                              grepl("Top 10%", Variable) ~ "Top 10%")) %>%
  left_join(colors, by = c("Location" = "country")) %>%
  filter(grepl("Pre-tax", Variable)) %>%
  
  ggplot + geom_line(aes(x = date, y = value, color = color, linetype2 = top)) +
  scale_color_identity() + ylab("Part du revenu national, avant redistribution (%)") + xlab("") + theme_minimal() +
  add_4flags +
  scale_y_continuous(breaks = 0.01*seq(0, 100, 5),
                     labels = scales::percent_format(accuracy = 1)) +
  scale_x_date(breaks = seq(1800, 2025, 10) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  theme(legend.position = c(0.6, 0.9),
        legend.title = element_blank()) +
 annotate(geom="text", x = as.Date("1925-01-01"), y = 0.08, label = "par les 1% les + riches", size=3, fontface="bold") +
 annotate(geom="text", x = as.Date("1925-01-01"), y = 0.10, label = "Part du revenu détenue", size=3, fontface="bold") +
 annotate(geom="text", x = as.Date("1926-01-01"), y = 0.34, label = "par les 10% les + riches", size=3, fontface="bold") +
 annotate(geom="text", x = as.Date("1926-01-01"), y = 0.36, label = "Part du revenu détenue", size=3, fontface="bold")

Commencer à 0

Code 
WID_Data_29122021 %>%
  mutate(date = paste0(year, "-01-01") %>% as.Date) %>%
  filter(date >= as.Date("1914-01-01")) %>%
  mutate(Location = ifelse(Location == "USA", "United States", Location),
         pre_post = case_when(grepl("Post-tax", Variable) ~ "Après redistribution",
                              grepl("Pre-tax", Variable) ~ "Avant redistribution"),
         top = case_when(grepl("Top 1%", Variable) ~ "Top 1%",
                              grepl("Top 10%", Variable) ~ "Top 10%")) %>%
  left_join(colors, by = c("Location" = "country")) %>%
  filter(grepl("Pre-tax", Variable)) %>%
  
  ggplot + geom_line(aes(x = date, y = value, color = color, linetype2 = top)) +
  scale_color_identity() + ylab("Part du revenu national, avant redistribution (%)") + xlab("") + theme_minimal() +
  add_4flags +
  scale_y_continuous(breaks = 0.01*seq(0, 100, 5),
                     labels = scales::percent_format(accuracy = 1),
                     limits = c(0, 0.52)) +
  scale_x_date(breaks = seq(1800, 2025, 10) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  theme(legend.position = c(0.6, 0.9),
        legend.title = element_blank()) +
 annotate(geom="text", x = as.Date("1925-01-01"), y = 0.08, label = "par les 1% les + riches", size=3, fontface="bold") +
 annotate(geom="text", x = as.Date("1925-01-01"), y = 0.10, label = "Part du revenu détenue", size=3, fontface="bold") +
 annotate(geom="text", x = as.Date("1926-01-01"), y = 0.34, label = "par les 10% les + riches", size=3, fontface="bold") +
 annotate(geom="text", x = as.Date("1926-01-01"), y = 0.36, label = "Part du revenu détenue", size=3, fontface="bold")

Post-redistribution

Automatique

Code 
WID_Data_29122021 %>%
  mutate(date = paste0(year, "-01-01") %>% as.Date) %>%
  filter(date >= as.Date("1914-01-01")) %>%
  mutate(Location = ifelse(Location == "USA", "United States", Location),
         pre_post = case_when(grepl("Post-tax", Variable) ~ "Après redistribution",
                              grepl("Pre-tax", Variable) ~ "Avant redistribution"),
         top = case_when(grepl("Top 1%", Variable) ~ "Top 1%",
                              grepl("Top 10%", Variable) ~ "Top 10%")) %>%
  left_join(colors, by = c("Location" = "country")) %>%
  filter(grepl("Post-tax", Variable)) %>%
  
  ggplot + geom_line(aes(x = date, y = value, color = color, linetype2 = top)) +
  scale_color_identity() + ylab("Part du revenu national, après redistribution (%)") + xlab("") + theme_minimal() +
  add_4flags +
  scale_y_continuous(breaks = 0.01*seq(0, 100, 5),
                     labels = scales::percent_format(accuracy = 1)) +
  scale_x_date(breaks = seq(1800, 2025, 10) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  theme(legend.position = c(0.6, 0.9),
        legend.title = element_blank()) +
 annotate(geom="text", x = as.Date("1925-01-01"), y = 0.08, label = "par les 1% les + riches", size=3, fontface="bold") +
 annotate(geom="text", x = as.Date("1925-01-01"), y = 0.10, label = "Part du revenu détenue", size=3, fontface="bold") +
 annotate(geom="text", x = as.Date("1926-01-01"), y = 0.34, label = "par les 10% les + riches", size=3, fontface="bold") +
 annotate(geom="text", x = as.Date("1926-01-01"), y = 0.36, label = "Part du revenu détenue", size=3, fontface="bold")

Commencer à 0

Code 
WID_Data_29122021 %>%
  mutate(date = paste0(year, "-01-01") %>% as.Date) %>%
  filter(date >= as.Date("1914-01-01")) %>%
  mutate(Location = ifelse(Location == "USA", "United States", Location),
         pre_post = case_when(grepl("Post-tax", Variable) ~ "Après redistribution",
                              grepl("Pre-tax", Variable) ~ "Avant redistribution"),
         top = case_when(grepl("Top 1%", Variable) ~ "Top 1%",
                              grepl("Top 10%", Variable) ~ "Top 10%")) %>%
  left_join(colors, by = c("Location" = "country")) %>%
  filter(grepl("Post-tax", Variable)) %>%
  
  ggplot + geom_line(aes(x = date, y = value, color = color, linetype2 = top)) +
  scale_color_identity() + ylab("Part du revenu national, après redistribution (%)") + xlab("") + theme_minimal() +
  add_4flags +
  scale_y_continuous(breaks = 0.01*seq(0, 100, 5),
                     labels = scales::percent_format(accuracy = 1),
                     limits = c(0, 0.47)) +
  scale_x_date(breaks = seq(1800, 2025, 10) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  theme(legend.position = c(0.6, 0.9),
        legend.title = element_blank()) +
 annotate(geom="text", x = as.Date("1925-01-01"), y = 0.08, label = "par les 1% les + riches", size=3, fontface="bold") +
 annotate(geom="text", x = as.Date("1925-01-01"), y = 0.10, label = "Part du revenu détenue", size=3, fontface="bold") +
 annotate(geom="text", x = as.Date("1926-01-01"), y = 0.34, label = "par les 10% les + riches", size=3, fontface="bold") +
 annotate(geom="text", x = as.Date("1926-01-01"), y = 0.36, label = "Part du revenu détenue", size=3, fontface="bold")

Net personal wealth

US, France

Code 
WID_data_DE %>%
  bind_rows(WID_data_FR) %>%
  bind_rows(WID_data_US) %>%
  mutate(name = substr(variable, 2, 6),
         type = substr(variable, 1, 1)) %>%
  filter(name %in% c("hweal"),
         type == "s",
         pop == "j",
         percentile %in% c("p99p100", "p90p100"),
         country %in% c("US", "FR")) %>%
  year_to_date2() %>%
  filter(date >= as.Date("1914-01-01")) %>%
  left_join(country, by = "country") %>%
  rename(Location = Country) %>%
  left_join(colors, by = c("Location" = "country")) %>%
  left_join(name, by = "name") %>%
  ggplot + geom_line(aes(x = date, y = value, color = color, linetype = percentile)) +
  scale_color_identity() + ylab("Part de la richesse (%)") + xlab("") + theme_minimal() +
  add_4flags +
  scale_y_continuous(breaks = 0.01*seq(0, 100, 5),
                     labels = scales::percent_format(accuracy = 1)) +
  scale_x_date(breaks = seq(1800, 2025, 10) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  theme(legend.position = c(0.6, 0.9),
        legend.title = element_blank()) +
 annotate(geom="text", x = as.Date("1925-01-01"), y = 0.25, label = "par les 1% les + riches", size=3, fontface="bold") +
 annotate(geom="text", x = as.Date("1925-01-01"), y = 0.27, label = "Part de la richesse détenue", size=3, fontface="bold") +
 annotate(geom="text", x = as.Date("1926-01-01"), y = 0.65, label = "par les 10% les + riches", size=3, fontface="bold") +
 annotate(geom="text", x = as.Date("1926-01-01"), y = 0.67, label = "Part de la richesse détenue", size=3, fontface="bold")

US, France, Italy

Code 
WID_data_IT %>%
  bind_rows(WID_data_FR) %>%
  bind_rows(WID_data_US) %>%
  mutate(name = substr(variable, 2, 6),
         type = substr(variable, 1, 1)) %>%
  filter(name %in% c("hweal"),
         type == "s",
         pop == "j",
         percentile %in% c("p99p100", "p90p100"),
         country %in% c("US", "FR", "IT")) %>%
  year_to_date2() %>%
  filter(date >= as.Date("1914-01-01")) %>%
  left_join(country, by = "country") %>%
  rename(Location = Country) %>%
  left_join(colors, by = c("Location" = "country")) %>%
  left_join(name, by = "name") %>%
  ggplot + geom_line(aes(x = date, y = value, color = color, linetype = percentile)) +
  scale_color_identity() + ylab("Part de la richesse (%)") + xlab("") + theme_minimal() +
  add_4flags +
  scale_y_continuous(breaks = 0.01*seq(0, 100, 5),
                     labels = scales::percent_format(accuracy = 1)) +
  scale_x_date(breaks = seq(1800, 2025, 10) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  theme(legend.position = c(0.6, 0.9),
        legend.title = element_blank()) +
 annotate(geom="text", x = as.Date("1925-01-01"), y = 0.25, label = "par les 1% les + riches", size=3, fontface="bold") +
 annotate(geom="text", x = as.Date("1925-01-01"), y = 0.27, label = "Part de la richesse détenue", size=3, fontface="bold") +
 annotate(geom="text", x = as.Date("1926-01-01"), y = 0.65, label = "par les 10% les + riches", size=3, fontface="bold") +
 annotate(geom="text", x = as.Date("1926-01-01"), y = 0.67, label = "Part de la richesse détenue", size=3, fontface="bold")

US, France, Sweden

All

Code 
WID_data_SE %>%
  bind_rows(WID_data_FR) %>%
  bind_rows(WID_data_US) %>%
  mutate(name = substr(variable, 2, 6),
         type = substr(variable, 1, 1)) %>%
  filter(name %in% c("hweal"),
         type == "s",
         pop == "j",
         percentile %in% c("p99p100", "p90p100"),
         country %in% c("US", "FR", "SE")) %>%
  year_to_date2() %>%
  left_join(country, by = "country") %>%
  rename(Location = Country) %>%
  left_join(colors, by = c("Location" = "country")) %>%
  left_join(name, by = "name") %>%
  ggplot + geom_line(aes(x = date, y = value, color = color, linetype = percentile)) +
  scale_color_identity() + ylab("Part de la richesse (%)") + xlab("") + theme_minimal() +
  add_6flags +
  scale_y_continuous(breaks = 0.01*seq(0, 100, 5),
                     labels = scales::percent_format(accuracy = 1)) +
  scale_x_date(breaks = seq(1800, 2025, 20) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  theme(legend.position = c(0.6, 0.9),
        legend.title = element_blank()) +
 annotate(geom="text", x = as.Date("1925-01-01"), y = 0.25, label = "par les 1% les + riches", size=3, fontface="bold") +
 annotate(geom="text", x = as.Date("1925-01-01"), y = 0.27, label = "Part de la richesse détenue", size=3, fontface="bold") +
 annotate(geom="text", x = as.Date("1926-01-01"), y = 0.65, label = "par les 10% les + riches", size=3, fontface="bold") +
 annotate(geom="text", x = as.Date("1926-01-01"), y = 0.67, label = "Part de la richesse détenue", size=3, fontface="bold")

1914-

Code 
WID_data_SE %>%
  bind_rows(WID_data_FR) %>%
  bind_rows(WID_data_US) %>%
  mutate(name = substr(variable, 2, 6),
         type = substr(variable, 1, 1)) %>%
  filter(name %in% c("hweal"),
         type == "s",
         pop == "j",
         percentile %in% c("p99p100", "p90p100"),
         country %in% c("US", "FR", "SE")) %>%
  year_to_date2() %>%
  filter(date >= as.Date("1914-01-01")) %>%
  left_join(country, by = "country") %>%
  rename(Location = Country) %>%
  left_join(colors, by = c("Location" = "country")) %>%
  left_join(name, by = "name") %>%
  ggplot + geom_line(aes(x = date, y = value, color = color, linetype = percentile)) +
  scale_color_identity() + ylab("Part de la richesse (%)") + xlab("") + theme_minimal() +
  add_6flags +
  scale_y_continuous(breaks = 0.01*seq(0, 100, 5),
                     labels = scales::percent_format(accuracy = 1)) +
  scale_x_date(breaks = seq(1800, 2025, 10) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  theme(legend.position = c(0.6, 0.9),
        legend.title = element_blank()) +
 annotate(geom="text", x = as.Date("1925-01-01"), y = 0.25, label = "par les 1% les + riches", size=3, fontface="bold") +
 annotate(geom="text", x = as.Date("1925-01-01"), y = 0.27, label = "Part de la richesse détenue", size=3, fontface="bold") +
 annotate(geom="text", x = as.Date("1926-01-01"), y = 0.65, label = "par les 10% les + riches", size=3, fontface="bold") +
 annotate(geom="text", x = as.Date("1926-01-01"), y = 0.67, label = "Part de la richesse détenue", size=3, fontface="bold")

1995-

Code 
WID_data_SE %>%
  bind_rows(WID_data_FR) %>%
  bind_rows(WID_data_US) %>%
  mutate(name = substr(variable, 2, 6),
         type = substr(variable, 1, 1)) %>%
  filter(name %in% c("hweal"),
         type == "s",
         pop == "j",
         percentile %in% c("p99p100", "p90p100"),
         country %in% c("US", "FR", "SE")) %>%
  year_to_date2() %>%
  filter(date >= as.Date("1995-01-01")) %>%
  left_join(country, by = "country") %>%
  rename(Location = Country) %>%
  left_join(colors, by = c("Location" = "country")) %>%
  left_join(name, by = "name") %>%
  ggplot + geom_line(aes(x = date, y = value, color = color, linetype = percentile)) +
  scale_color_identity() + ylab("Part de la richesse (%)") + xlab("") + theme_minimal() +
  add_6flags +
  scale_y_continuous(breaks = 0.01*seq(0, 100, 5),
                     labels = scales::percent_format(accuracy = 1)) +
  scale_x_date(breaks = seq(1800, 2025, 5) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  theme(legend.position = c(0.6, 0.9),
        legend.title = element_blank()) +
 annotate(geom="text", x = as.Date("2000-01-01"), y = 0.25, label = "par les 1% les + riches", size=3, fontface="bold") +
 annotate(geom="text", x = as.Date("2000-01-01"), y = 0.27, label = "Part de la richesse détenue", size=3, fontface="bold") +
 annotate(geom="text", x = as.Date("2001-01-01"), y = 0.65, label = "par les 10% les + riches", size=3, fontface="bold") +
 annotate(geom="text", x = as.Date("2001-01-01"), y = 0.67, label = "Part de la richesse détenue", size=3, fontface="bold")

US, France, UK, Germany

Code 
WID_data_GB %>%
  bind_rows(WID_data_FR) %>%
  bind_rows(WID_data_US) %>%
  bind_rows(WID_data_QE) %>%
  mutate(name = substr(variable, 2, 6),
         type = substr(variable, 1, 1)) %>%
  filter(name %in% c("hweal"),
         type == "s",
         pop == "j",
         percentile %in% c("p99p100", "p90p100"),
         country %in% c("US", "FR", "GB", "QE")) %>%
  year_to_date2() %>%
  filter(date >= as.Date("1914-01-01")) %>%
  left_join(country, by = "country") %>%
  rename(Location = Country) %>%
  left_join(colors, by = c("Location" = "country")) %>%
  left_join(name, by = "name") %>%
  ggplot + geom_line(aes(x = date, y = value, color = color, linetype = percentile)) +
  scale_color_identity() + ylab("Part de la richesse (%)") + xlab("") + theme_minimal() +
  add_8flags +
  scale_y_continuous(breaks = 0.01*seq(0, 100, 5),
                     labels = scales::percent_format(accuracy = 1)) +
  scale_x_date(breaks = seq(1800, 2025, 10) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  theme(legend.position = c(0.6, 0.9),
        legend.title = element_blank()) +
 annotate(geom="text", x = as.Date("1925-01-01"), y = 0.25, label = "par les 1% les + riches", size=3, fontface="bold") +
 annotate(geom="text", x = as.Date("1925-01-01"), y = 0.27, label = "Part de la richesse détenue", size=3, fontface="bold") +
 annotate(geom="text", x = as.Date("1926-01-01"), y = 0.65, label = "par les 10% les + riches", size=3, fontface="bold") +
 annotate(geom="text", x = as.Date("1926-01-01"), y = 0.67, label = "Part de la richesse détenue", size=3, fontface="bold")