Unemployment rate by sex and age (%) - UNE_DEAP_SEX_AGE_RT_Q

Data - ILO

Author

François Geerolf 

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Info

Last observation: 2026Q2 (N = 253)

First observation: 1948Q1 (N = 60)

Last data update: 01 aoû 2026, 21:45. Last compile: 17 aoû 2026, 22:27

classif1

Code 
UNE_DEAP_SEX_AGE_RT_Q |>
  left_join(classif1, by = "classif1") |>
  group_by(classif1, Classif1) |>
  summarise(Nobs = n()) |>
  arrange(-Nobs) |>
  print_table_conditional()
classif1 Classif1 Nobs
AGE_YTHADULT_YGE15 Age (Youth, adults): 15+ 29998
AGE_YTHADULT_YGE25 Age (Youth, adults): 25+ 26686
AGE_YTHADULT_Y15-24 Age (Youth, adults): 15-24 26681
AGE_YTHADULT_Y15-64 Age (Youth, adults): 15-64 25981
AGE_AGGREGATE_YGE15 NA 25978
AGE_AGGREGATE_Y15-24 Age (Aggregate bands): 15-24 25971
AGE_AGGREGATE_Y25-54 Age (Aggregate bands): 25-54 25927
AGE_10YRBANDS_YGE15 NA 25858
AGE_10YRBANDS_Y15-24 Age (10-year bands): 15-24 25851
AGE_AGGREGATE_Y55-64 Age (Aggregate bands): 55-64 25836
AGE_10YRBANDS_Y25-34 Age (10-year bands): 25-34 25793
AGE_10YRBANDS_Y35-44 Age (10-year bands): 35-44 25763
AGE_10YRBANDS_Y45-54 Age (10-year bands): 45-54 25727
AGE_10YRBANDS_Y55-64 Age (10-year bands): 55-64 25723
AGE_5YRBANDS_YGE15 NA 25243
AGE_5YRBANDS_Y20-24 Age (5-year bands): 20-24 25232
AGE_5YRBANDS_Y15-19 Age (5-year bands): 15-19 25225
AGE_5YRBANDS_Y25-29 Age (5-year bands): 25-29 24893
AGE_5YRBANDS_Y30-34 Age (5-year bands): 30-34 24889
AGE_5YRBANDS_Y35-39 Age (5-year bands): 35-39 24877
AGE_5YRBANDS_Y40-44 Age (5-year bands): 40-44 24861
AGE_5YRBANDS_Y45-49 Age (5-year bands): 45-49 24854
AGE_5YRBANDS_Y50-54 Age (5-year bands): 50-54 24809
AGE_5YRBANDS_Y55-59 Age (5-year bands): 55-59 24734
AGE_5YRBANDS_Y60-64 Age (5-year bands): 60-64 24150
AGE_AGGREGATE_YGE65 Age (Aggregate bands): 65+ 22612
AGE_10YRBANDS_YGE65 Age (10-year bands): 65+ 22255
AGE_5YRBANDS_YGE65 Age (5-year bands): 65+ 22068

source

Code 
UNE_DEAP_SEX_AGE_RT_Q |>
  left_join(source, by = "source") |>
  group_by(source, Source) |>
  summarise(Nobs = n()) |>
  arrange(-Nobs) |>
  print_table_conditional()

sex

Code 
UNE_DEAP_SEX_AGE_RT_Q |>
  left_join(sex, by = "sex") |>
  group_by(sex, Sex) |>
  summarise(Nobs = n()) |>
  arrange(-Nobs) |>
  print_table_conditional()
sex Sex Nobs
SEX_T Sex: Total 236937
SEX_M Sex: Male 236128
SEX_F Sex: Female 235324
SEX_O Sex: Other 86

Nombre de chômeurs

Code 
UNE_DEAP_SEX_AGE_RT_Q |>
  filter(sex == "SEX_T",
         classif1 == "AGE_AGGREGATE_YGE15") |>
  left_join(ref_area, by = "ref_area") |>
  mutate(obs_value = round(obs_value, 1)) |>
  group_by(ref_area, Ref_area) |>
  arrange(time) |>
  summarise(Nobs = n(),
            `Year 1` = first(time),
            `Inflation 1` = first(obs_value),
            `Year 2` = last(time),
            `Inflation 2` = last(obs_value)) |>
  arrange(-Nobs) |>
  mutate(Flag = gsub(" ", "-", str_to_lower(Ref_area)),
         Flag = paste0('<img src="../../bib/flags/vsmall/', Flag, '.png" alt="Flag">')) |>
  select(Flag, everything()) %>%
  {if (is_html_output()) datatable(., filter = 'top', rownames = F, escape = F) else .}

Canada, Japan, United States

Code 
UNE_DEAP_SEX_AGE_RT_Q |>
  filter(ref_area %in% c("USA", "JPN", "CAN"),
         sex == "SEX_T",
         classif1 == "AGE_AGGREGATE_YGE15") |>
  left_join(ref_area, by = "ref_area") |>
  quarter_to_date() |>
  mutate(obs_value = obs_value/100) |>
  left_join(colors, by = c("Ref_area" = "country")) |>
  ggplot() + geom_line(aes(x = date, y = obs_value, color = color)) + 
  scale_color_identity() + add_flags + theme_minimal() + 
  xlab("") + ylab("Unemployment rate") +
  theme(legend.title = element_blank(),
        legend.position = c(0.15, 0.85)) +
  scale_x_date(breaks = seq(1900, 2100, 5) |> paste0("-01-01") |> as.Date(),
               labels = date_format("%Y")) +
  scale_y_continuous(breaks = 0.01*seq(-100, 10000, 2),
                     labels = percent_format(a = 1)) + 
  geom_hline(yintercept = 0, linetype = "dashed", color = "grey")

France, Germany, Spain

Code 
UNE_DEAP_SEX_AGE_RT_Q |>
  filter(ref_area %in% c("FRA", "DEU", "ESP"),
         sex == "SEX_T",
         classif1 == "AGE_AGGREGATE_YGE15") |>
  left_join(ref_area, by = "ref_area") |>
  quarter_to_date() |>
  mutate(obs_value = obs_value/100) |>
  left_join(colors, by = c("Ref_area" = "country")) |>
  ggplot() + geom_line(aes(x = date, y = obs_value, color = color)) + 
  scale_color_identity() + add_flags + theme_minimal() + 
  xlab("") + ylab("Unemployment rate") +
  theme(legend.title = element_blank(),
        legend.position = c(0.15, 0.85)) +
  scale_x_date(breaks = seq(1900, 2100, 2) |> paste0("-01-01") |> as.Date(),
               labels = date_format("%Y")) +
  scale_y_continuous(breaks = 0.01*seq(-100, 10000, 2),
                     labels = percent_format(a = 1)) + 
  geom_hline(yintercept = 0, linetype = "dashed", color = "grey")

Hong Kong, Italy, Korea

Code 
UNE_DEAP_SEX_AGE_RT_Q |>
  filter(ref_area %in% c("HKG", "ITA", "KOR"),
         sex == "SEX_T",
         classif1 == "AGE_AGGREGATE_YGE15") |>
  left_join(ref_area, by = "ref_area") |>
  quarter_to_date() |>
  mutate(obs_value = obs_value/100) |>
  left_join(colors, by = c("Ref_area" = "country")) |>
  ggplot() + geom_line(aes(x = date, y = obs_value, color = color)) + 
  scale_color_identity() + add_flags + theme_minimal() + 
  xlab("") + ylab("Unemployment rate") +
  theme(legend.title = element_blank(),
        legend.position = c(0.15, 0.85)) +
  scale_x_date(breaks = seq(1900, 2100, 5) |> paste0("-01-01") |> as.Date(),
               labels = date_format("%Y")) +
  scale_y_continuous(breaks = 0.01*seq(-100, 10000, 2),
                     labels = percent_format(a = 1)) + 
  geom_hline(yintercept = 0, linetype = "dashed", color = "grey")