Median saving rate by age of the reference person - experimental statistics - icw_sr_01

Data - Eurostat

Author

François Geerolf

Info

Last observation: Annual: 2020 (N = 377)

First observation: Annual: 2010 (N = 433)

Last data update: 11 aoû 2026, 18:11. Last compile: 12 aoû 2026, 00:28

Structure

2010

Code 
icw_sr_01 |>
  filter(time == "2010") |>
  select(age, geo, Geo, values) |>

  spread(age, values) |>
  select(geo, Geo, TOTAL, everything()) |>
  print_table_conditional()
geo Geo TOTAL UNK Y_GE60 Y_GE70 Y_GE75 Y_LT30 Y_LT35 Y30-39 Y30-44 Y35-44 Y40-49 Y45-54 Y45-59 Y50-59 Y55-64 Y60-69 Y65-74
AT Austria 18.0 NA 21.7 21.5 20.1 -8.0 1.1 14.9 16.1 21.3 22.8 22.5 22.2 20.7 15.8 16.0 22.1
BE Belgium 8.9 NA 3.1 -3.0 -3.4 5.9 9.8 12.8 12.8 14.0 14.1 12.1 15.2 13.5 12.7 7.2 1.7
BG Bulgaria 38.0 NA 29.5 NA NA 35.0 NA NA 40.4 NA NA NA 44.3 NA NA NA NA
CY Cyprus 12.2 NA 21.3 18.4 16.4 -0.9 2.4 3.9 0.6 2.9 4.3 9.2 13.1 19.0 22.0 20.0 23.3
CZ Czechia 23.9 NA 19.8 15.6 15.1 25.7 29.5 27.2 25.9 25.2 25.9 25.9 28.9 28.4 26.2 21.8 19.2
DE Germany 13.5 NA 8.0 7.4 7.7 4.0 11.8 19.2 18.9 20.2 19.2 17.9 17.0 15.6 11.4 8.2 7.5
DK Denmark 4.3 NA -0.9 -7.6 -10.8 -30.4 -8.2 11.0 13.2 9.3 11.1 13.2 14.9 15.9 14.9 6.8 1.7
EE Estonia 34.9 NA 37.0 36.7 36.9 26.2 33.0 35.7 36.2 31.3 32.3 35.1 32.1 34.8 34.2 37.0 38.2
EL Greece -11.2 NA 2.4 9.1 7.4 -67.6 -47.8 -34.5 -20.4 -31.5 -26.1 -21.0 -9.7 -9.8 -1.8 7.3 14.9
ES Spain 16.1 NA 25.1 22.4 22.6 -3.2 8.4 14.2 13.4 13.8 10.0 10.0 15.4 16.2 19.9 22.7 24.9
FI Finland 22.2 NA 28.0 31.0 30.5 -1.6 5.7 16.5 22.2 21.1 21.1 23.6 23.6 25.4 25.1 28.4 31.5
FR France 29.1 NA 36.2 35.6 35.3 9.2 16.3 23.5 24.2 24.7 26.2 28.2 29.8 32.1 33.9 35.1 37.2
HR Croatia 4.0 NA 0.1 -1.6 -3.1 2.7 9.0 8.5 6.3 4.6 4.2 6.2 6.6 8.6 7.5 4.7 2.9
HU Hungary 16.3 NA 16.7 17.2 17.3 7.3 13.9 17.7 17.2 16.5 15.8 16.6 15.6 16.9 15.3 15.7 18.0
IE Ireland 18.4 NA 21.9 23.6 22.0 1.6 12.7 18.3 17.6 18.0 17.7 17.6 21.1 20.1 19.6 18.8 23.6
IT Italy 26.4 NA NA 33.8 33.9 -31.6 1.7 16.3 NA 18.2 19.2 21.8 NA 27.3 30.4 35.3 37.2
LT Lithuania 22.8 NA 13.7 21.2 20.2 20.4 22.4 22.3 21.0 20.8 23.3 24.1 22.7 23.6 25.2 24.1 23.9
LU Luxembourg 28.4 NA 35.7 32.0 33.2 13.7 19.3 23.9 26.3 25.2 26.9 28.8 29.7 31.9 34.5 34.0 32.4
LV Latvia 10.5 NA 12.7 12.4 11.8 7.9 11.0 12.6 15.9 10.0 10.3 10.6 7.8 7.9 6.5 10.1 13.2
MT Malta 13.7 NA 15.1 17.5 17.0 4.6 9.4 4.7 1.4 -0.2 4.8 12.6 15.7 19.5 19.3 18.0 19.9
PL Poland 24.7 NA 24.3 22.8 23.0 20.6 24.2 25.4 25.5 24.3 25.7 27.4 26.7 26.6 24.7 24.6 24.0
PT Portugal 13.1 NA 21.7 23.7 22.0 -3.4 0.8 2.6 6.8 2.4 2.7 7.2 13.0 16.6 21.4 23.0 26.3
RO Romania -5.1 NA -3.1 -5.2 -7.0 -15.5 -8.4 -6.1 -7.3 -7.7 -8.6 -6.1 -4.2 -2.6 -1.9 -0.8 -0.4
SE Sweden 18.0 NA 20.3 11.0 8.8 -6.1 2.8 18.6 21.1 20.2 23.2 28.0 27.1 28.4 24.7 23.0 21.2
SI Slovenia 18.0 NA 20.4 19.9 17.5 -4.7 8.2 14.5 14.9 17.5 18.6 19.6 20.2 21.6 17.1 17.9 24.4
SK Slovakia 24.4 NA 19.8 16.6 15.8 22.2 25.8 26.7 25.8 26.5 27.5 29.3 28.7 29.5 26.4 22.0 18.4
UK United Kingdom 22.9 NA 22.3 22.4 21.6 12.5 19.4 26.8 25.4 24.4 24.4 24.6 25.9 24.7 23.1 22.8 23.8

Table - 2015

All

Code 
icw_sr_01 |>
  filter(time == "2015") |>
  select(age, geo, Geo, values) |>
  
  spread(age, values) |>
  arrange(Geo) |>
  select(geo, Geo, TOTAL, everything()) |>
  mutate(Geo = ifelse(geo == "DE", "Germany", Geo)) |>
  mutate(Flag = gsub(" ", "-", str_to_lower(Geo)),
         Flag = paste0('<img src="../../bib/flags/vsmall/', Flag, '.png" alt="Flag">')) |>
  select(Flag, everything()) |>
  arrange(-`Y65-74`) %>%
  {if (is_html_output()) datatable(., filter = 'top', rownames = F, escape = F) else .}

Total, Y30-39, Y40-49, Y50-59, Y60-69, Y_GE70

Code 
icw_sr_01 |>
  filter(time == "2015") |>
  select(age, geo, Geo, values) |>
  
  spread(age, values) |>
  arrange(Geo) |>
  select(geo, Geo, TOTAL, everything()) |>
  mutate(Geo = ifelse(geo == "DE", "Germany", Geo)) |>
  mutate(Flag = gsub(" ", "-", str_to_lower(Geo)),
         Flag = paste0('<img src="../../bib/flags/vsmall/', Flag, '.png" alt="Flag">')) |>
  select(Flag, everything()) |>
  select(1, 2, 3, 4, 9, 11, 13, 15, 5) |>
  arrange(-`Y60-69`) %>%
  {if (is_html_output()) datatable(., filter = 'top', rownames = F, escape = F) else .}

Germany, France, United Kingdom, Spain

Code 
icw_sr_01 |>
  filter(time == "2015",
         geo %in% c("UK", "FR", "DE", "ES"),
         grepl("-", age) | age %in% c("Y_GE75", "Y_LT30")) |>
  mutate(age = ifelse(age == "Y_GE75", "Y75+", age),
         age = ifelse(age == "Y_LT30", "Y30-", age)) |>
  mutate(age = ifelse(age == "Y_GE75", "Y75+", age)) |>
  
  left_join(colors, by = c("Geo" = "country")) |>
  mutate(values = values/100) |>
  ggplot() + geom_line(aes(x = age, y = values, color = color, group = color)) +
  scale_color_identity() + theme_minimal() +
  geom_image(data = . %>%
               group_by(age) %>%
               filter(n() == 4) %>%
               arrange(values) %>%
               mutate(dist = min(values[2]-values[1],values[3]-values[2],values[4]-values[3])) %>%
               arrange(-dist, age) %>%
               head(4) %>%
               mutate(image = paste0("../../icon/flag/", str_to_lower(gsub(" ", "-", Geo)), ".png")),
             aes(x = age, y = values, image = image), asp = 1.5) +
  xlab("Age") + ylab("Taux d'épargne médian par âge") +
  scale_y_continuous(breaks = 0.01*seq(-30, 50, 5),
                     labels = percent_format(accuracy = 1),
                     limits = c(0, 0.5))

Germany, France, United Kingdom

Code 
icw_sr_01 |>
  filter(time == "2015",
         geo %in% c("UK", "FR", "DE"),
         grepl("-", age) | age %in% c("Y_GE75", "Y_LT30")) |>
  mutate(age = ifelse(age == "Y_GE75", "Y75+", age),
         age = ifelse(age == "Y_LT30", "Y30-", age)) |>
  mutate(age = ifelse(age == "Y_GE75", "Y75+", age)) |>
  
  left_join(colors, by = c("Geo" = "country")) |>
  mutate(values = values/100) |>
  ggplot() + geom_line(aes(x = age, y = values, color = color, group = color)) +
  scale_color_identity() + theme_minimal() +
  geom_image(data = . %>%
               group_by(age) %>%
               filter(n() == 3) %>%
               arrange(values) %>%
               mutate(dist = min(values[2]-values[1],values[3]-values[2])) %>%
               arrange(-dist, age) %>%
               head(3) %>%
               mutate(image = paste0("../../icon/flag/", str_to_lower(gsub(" ", "-", Geo)), ".png")),
             aes(x = age, y = values, image = image), asp = 1.5) +
  xlab("Age") + ylab("Median saving rate by age of the reference person") +
  scale_y_continuous(breaks = 0.01*seq(-30, 50, 5),
                     labels = percent_format(accuracy = 1),
                     limits = c(0, 0.5))

Portugal, Spain, United Kingdom

Code 
icw_sr_01 |>
  filter(time == "2015",
         geo %in% c("UK", "ES", "PT"),
         grepl("-", age) | age %in% c("Y_GE75", "Y_LT30")) |>
  mutate(age = ifelse(age == "Y_GE75", "Y75+", age),
         age = ifelse(age == "Y_LT30", "Y30-", age)) |>
  
  left_join(colors, by = c("Geo" = "country")) |>
  mutate(values = values/100) |>
  ggplot() + geom_line(aes(x = age, y = values, color = color, group = color)) +
  scale_color_identity() + theme_minimal() +
  geom_image(data = . %>%
               group_by(age) %>%
               filter(n() == 3) %>%
               arrange(values) %>%
               mutate(dist = min(values[2]-values[1],values[3]-values[2])) %>%
               arrange(-dist, age) %>%
               head(3) %>%
               mutate(image = paste0("../../icon/flag/", str_to_lower(gsub(" ", "-", Geo)), ".png")),
             aes(x = age, y = values, image = image), asp = 1.5) +
  xlab("Age") + ylab("Median saving rate by age of the reference person") +
  scale_y_continuous(breaks = 0.01*seq(-30, 50, 5),
                     labels = percent_format(accuracy = 1))

Austria, Belgium, Bulgaria

Code 
icw_sr_01 |>
  filter(time == "2015",
         geo %in% c("AT", "BE", "BG"),
         grepl("-", age) | age %in% c("Y_GE75", "Y_LT30")) |>
  mutate(age = ifelse(age == "Y_GE75", "Y75+", age),
         age = ifelse(age == "Y_LT30", "Y30-", age)) |>
  mutate(age = ifelse(age == "Y_GE75", "Y75+", age)) |>
  
  left_join(colors, by = c("Geo" = "country")) |>
  mutate(values = values/100) |>
  ggplot() + geom_line(aes(x = age, y = values, color = color, group = color)) +
  scale_color_identity() + theme_minimal() +
  geom_image(data = . %>%
               group_by(age) %>%
               filter(n() == 3) %>%
               arrange(values) %>%
               mutate(dist = min(values[2]-values[1],values[3]-values[2])) %>%
               arrange(-dist, age) %>%
               head(3) %>%
               mutate(image = paste0("../../icon/flag/", str_to_lower(gsub(" ", "-", Geo)), ".png")),
             aes(x = age, y = values, image = image), asp = 1.5) +
  xlab("Age") + ylab("Median saving rate by age of the reference person") +
  scale_y_continuous(breaks = 0.01*seq(-30, 50, 5),
                     labels = percent_format(accuracy = 1))

Estonia, Spain, Croatia

Code 
icw_sr_01 |>
  filter(time == "2015",
         geo %in% c("EE", "ES", "HR"),
         grepl("-", age) | age %in% c("Y_GE75", "Y_LT30")) |>
  mutate(age = ifelse(age == "Y_GE75", "Y75+", age),
         age = ifelse(age == "Y_LT30", "Y30-", age)) |>
  mutate(age = ifelse(age == "Y_GE75", "Y75+", age)) |>
  
  left_join(colors, by = c("Geo" = "country")) |>
  mutate(values = values/100) |>
  ggplot() + geom_line(aes(x = age, y = values, color = color, group = color)) +
  scale_color_identity() + theme_minimal() +
  geom_image(data = . %>%
               group_by(age) %>%
               filter(n() == 3) %>%
               arrange(values) %>%
               mutate(dist = min(values[2]-values[1],values[3]-values[2])) %>%
               arrange(-dist, age) %>%
               head(3) %>%
               mutate(image = paste0("../../icon/flag/", str_to_lower(gsub(" ", "-", Geo)), ".png")),
             aes(x = age, y = values, image = image), asp = 1.5) +
  xlab("Age") + ylab("Median saving rate by age of the reference person") +
  scale_y_continuous(breaks = 0.01*seq(-30, 50, 5),
                     labels = percent_format(accuracy = 1))

Hungary, Ireland, Lithuania

Code 
icw_sr_01 |>
  filter(time == "2015",
         geo %in% c("HU", "IE", "LT"),
         grepl("-", age) | age %in% c("Y_GE75", "Y_LT30")) |>
  mutate(age = ifelse(age == "Y_GE75", "Y75+", age),
         age = ifelse(age == "Y_LT30", "Y30-", age)) |>
  mutate(age = ifelse(age == "Y_GE75", "Y75+", age)) |>
  
  left_join(colors, by = c("Geo" = "country")) |>
  mutate(values = values/100) |>
  ggplot() + geom_line(aes(x = age, y = values, color = color, group = color)) +
  scale_color_identity() + theme_minimal() +
  geom_image(data = . %>%
               group_by(age) %>%
               filter(n() == 3) %>%
               arrange(values) %>%
               mutate(dist = min(values[2]-values[1],values[3]-values[2])) %>%
               arrange(-dist, age) %>%
               head(3) %>%
               mutate(image = paste0("../../icon/flag/", str_to_lower(gsub(" ", "-", Geo)), ".png")),
             aes(x = age, y = values, image = image), asp = 1.5) +
  xlab("Age") + ylab("Median saving rate by age of the reference person") +
  scale_y_continuous(breaks = 0.01*seq(-30, 50, 5),
                     labels = percent_format(accuracy = 1),
                     limits = c(0, 0.4))