Structure of earnings survey - annual earnings - earn_ses_annual

Data - Eurostat

Info

Last observation: 2022 (N = 91773)

First observation: 2002 (N = 887925)

Last data update: 14 aoû 2026, 22:49. Last compile: 18 aoû 2026, 00:13

Structure

freq

nace_r2

isco08

worktime

age

sex

indic_se

geo

Mean Annual Earnings, Total Economy

France, Germany, Italy, Spain, Poland

earn_ses_annual |>
  filter(geo %in% c("FR", "DE", "IT", "ES", "PL"),
         nace_r2 == "B-S_X_O",
         isco08 == "TOTAL",
         worktime == "TOTAL",
         age == "TOTAL",
         sex == "T",
         indic_se == "MEAN_E_EUR") |>
  year_to_date() |>
  left_join(colors, by = c("Geo" = "country")) |>
  ggplot() + geom_line(aes(x = date, y = values, color = color)) +
  theme_minimal() + scale_color_identity() + add_flags +
  scale_x_date(breaks = as.Date(paste0(seq(2000, 2100, 4), "-01-01")),
               labels = date_format("%Y")) +
  xlab("") + ylab("Mean annual earnings, total economy (€)") +
  scale_y_continuous(labels = scales::comma)

Gender Pay Gap

Latest Available Year

latest_yr <- earn_ses_annual |>
  filter(geo %in% c("FR", "DE", "IT", "ES", "PL"),
         nace_r2 == "B-S_X_O",
         isco08 == "TOTAL",
         worktime == "TOTAL",
         age == "TOTAL",
         sex %in% c("M", "F"),
         indic_se == "MEAN_E_EUR",
         !is.na(values)) |>
  summarise(m = max(time)) |>
  pull(m)

earn_ses_annual |>
  filter(geo %in% c("FR", "DE", "IT", "ES", "PL"),
         nace_r2 == "B-S_X_O",
         isco08 == "TOTAL",
         worktime == "TOTAL",
         age == "TOTAL",
         sex %in% c("M", "F"),
         indic_se == "MEAN_E_EUR",
         time == latest_yr) |>
  select(Geo, Sex, values) |>
  spread(Sex, values) |>
  mutate(`Gap (%)` = round(100 * (Males - Females) / Males, 1)) |>
  arrange(-`Gap (%)`) |>
  print_table_conditional()

Geo	Females	Males	Gap (%)
Germany	29666	45492	34.8
Italy	27530	34777	20.8
France	30402	37942	19.9
Poland	14465	17552	17.6
Spain	24173	29139	17.0

Table

Javascript

earn_ses_annual |>
  filter(nace_r2 == "B-N",
         isco08 == "TOTAL",
         worktime == "TOTAL", 
         age == "TOTAL",
         sex == "T",
         indic_se == "MEAN_E_EUR",
         time %in% c("2002", "2014", "2018")) |>
  
  select(geo, Geo, time, values) |>
  na.omit() |>
  mutate(Geo = ifelse(geo == "DE", "Germany", Geo),
         values = round(values)) |>
  spread(time, values) %>%
  mutate_at(vars(-1, -2), funs(ifelse(is.na(.), "", paste0(., " €")))) %>%
  {if (is_html_output()) datatable(., filter = 'top', rownames = F) else .}

Maps

2018

earn_ses_annual |>
  filter(nace_r2 == "B-N",
         isco08 == "TOTAL",
         worktime == "TOTAL", 
         age == "TOTAL",
         sex == "T",
         indic_se == "MEAN_E_EUR",
         time %in% c("2018")) |>
  
  select(geo, Geo, values) |>
  right_join(europe_NUTS0, by = "geo") |>
  filter(long >= -15, lat >= 33) |>
  ggplot(aes(x = long, y = lat, group = group, fill = values)) +
  geom_polygon() + coord_map() +
  scale_fill_viridis_c(na.value = "white",
                       labels = scales::dollar_format(accuracy = 1, prefix = "", suffix = "€"),
                       breaks = seq(0, 100000, 10000),
                       values = c(0, 0.1, 0.2, 0.3, 0.4, 0.5, 1)) +
  theme_void() + theme(legend.position = c(0.25, 0.85)) + 
  labs(fill = "Avg Wage")