Unemployment by sex and age – annual data - une_rt_a

Data - Eurostat

Info

Last observation: Annual: 2025 (N = 2,328)

First observation: Annual: 2003 (N = 63)

Last data update: 11 aoû 2026, 18:34. Last compile: 12 aoû 2026, 04:04

Structure

freq

age

unit

sex

geo

France, Germany, Portugal

une_rt_a |>
  filter(geo %in% c("FR", "DE", "PT"),
         age == "Y20-64",
         sex == "T",
         unit == "PC_ACT") |>
  year_to_date() |>
  
  left_join(colors, by = c("Geo" = "country")) |>
  mutate(values = values/100) |>
  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(1960, 2100, 1), "-01-01")),
               labels = date_format("%Y")) +
  xlab("") + ylab("Unemployment, Percentage of active population") +
  scale_y_continuous(breaks = 0.01*seq(0, 200, 1),
                     labels = scales::percent_format(accuracy = 1))

Greece

English

age <- read_parquet("age.parquet")
une_rt_a |>
  filter(geo %in% c("EL"),
         age %in% c("Y20-64", "Y15-24", "Y25-54"),
         sex == "T",
         unit == "PC_ACT") |>
  year_to_date() |>
  
  filter(date >= as.Date("2007-01-01")) |>
  ggplot() + geom_line() + theme_minimal()  +
  aes(x = date, y = values/100, color = Age, linetype = Age) +
  scale_color_manual(values = viridis(4)[1:3]) +
  scale_x_date(breaks = as.Date(paste0(seq(1960, 2100, 1), "-01-01")),
               labels = date_format("%Y")) +
  theme(legend.position = c(0.15, 0.85),
        legend.title = element_blank()) +
  xlab("") + ylab("Unemployment in Greece (% of Active Population)") +
  scale_y_continuous(breaks = 0.01*seq(0, 200, 5),
                     labels = scales::percent_format(accuracy = 1))

Français

age <- read_parquet("age_fr.parquet")
une_rt_a |>
  filter(geo %in% c("EL"),
         age %in% c("Y20-64", "Y15-24", "Y25-54"),
         sex == "T",
         unit == "PC_ACT") |>
  year_to_date() |>
  
  filter(date >= as.Date("2007-01-01")) |>
  ggplot() + geom_line() + theme_minimal()  +
  aes(x = date, y = values/100, color = Age, linetype = Age) +
  scale_color_manual(values = viridis(4)[1:3]) +
  scale_x_date(breaks = as.Date(paste0(seq(1960, 2100, 1), "-01-01")),
               labels = date_format("%Y")) +
  theme(legend.position = c(0.15, 0.85),
        legend.title = element_blank()) +
  xlab("") + ylab("Chômage en Grèce (% de la population active)") +
  scale_y_continuous(breaks = 0.01*seq(0, 200, 5),
                     labels = scales::percent_format(accuracy = 1))

Euro Area

age <- read_parquet("age.parquet")
une_rt_a |>
  filter(geo %in% c("EA19", "EU15", "DE"),
         age == "Y20-64",
         sex == "T",
         unit == "PC_ACT") |>
  year_to_date() |>
  
  mutate(values = values/100) |>
  left_join(colors, by = c("Geo" = "country")) |>
  ggplot() + geom_line() + theme_minimal()  +
  aes(x = date, y = values, color = color) +
  scale_color_identity() + add_flags +
  scale_x_date(breaks = as.Date(paste0(seq(1960, 2100, 2), "-01-01")),
               labels = date_format("%Y")) +
  theme(legend.position = c(0.3, 0.25),
        legend.title = element_blank()) +
  xlab("") + ylab("Percentage of active population") +
  scale_y_continuous(breaks = 0.01*seq(0, 200, 1),
                     labels = scales::percent_format(accuracy = 1))

Phillips curves

Germany

tipsna62 |>
  filter(geo %in% c("DE"),
         unit == "THS_PER") |>
  select(geo, time, emp = values) |>
  left_join(tipslm13 |>
              select(geo, time, comp = values),
            by = c("geo", "time")) |>
  left_join(une_rt_a |>
              filter(age == "Y20-64",
                     sex == "T",
                     unit == "PC_ACT") |>
              select(geo, time, unr = values),
            by = c("geo", "time")) |>
  mutate(comp_emp = comp/emp,
         comp_emp_d1 = comp_emp/lag(comp_emp, 1)-1) |>
  year_to_enddate() |>
  transmute(date, comp_emp_d1=100*comp_emp_d1, unr = unr) |>
  gather(variable, value, -date) |>
  mutate(Variable = case_when(variable == "comp_emp_d1" ~ "Wage Inflation (%)",
                              variable == "unr" ~ "Unemployment Rate (%)")) |>
  ggplot() + geom_line() + theme_minimal()  +
  aes(x = date, y = value/100, color = Variable, linetype = Variable) +
  scale_color_manual(values = viridis(3)[1:2]) +
  scale_x_date(breaks = as.Date(paste0(seq(1960, 2100, 1), "-01-01")),
               labels = date_format("%Y")) +
  scale_y_continuous(breaks = 0.01*seq(-100, 100, 1),
                     labels = percent_format(accuracy = 1)) +
  theme(legend.position = c(0.8, 0.85),
        legend.title = element_blank()) +
  xlab("") + ylab("Unemployment Rate, Wage Inflation (%)")

France

tipsna62 |>
  filter(geo %in% c("FR"),
         unit == "THS_PER") |>
  select(geo, time, emp = values) |>
  left_join(tipslm13 |>
              select(geo, time, comp = values),
            by = c("geo", "time")) |>
  left_join(une_rt_a |>
              filter(age == "Y20-64",
                     sex == "T",
                     unit == "PC_ACT") |>
              select(geo, time, unr = values),
            by = c("geo", "time")) |>
  mutate(comp_emp = comp/emp,
         comp_emp_d1 = comp_emp/lag(comp_emp, 1)-1) |>
  year_to_enddate() |>
  transmute(date, comp_emp_d1=100*comp_emp_d1, unr = unr) |>
  gather(variable, value, -date) |>
  mutate(Variable = case_when(variable == "comp_emp_d1" ~ "Wage Inflation (%)",
                              variable == "unr" ~ "Unemployment Rate (%)")) |>
  ggplot() + geom_line() + theme_minimal()  +
  aes(x = date, y = value/100, color = Variable, linetype = Variable) +
  scale_color_manual(values = viridis(3)[1:2]) +
  scale_x_date(breaks = as.Date(paste0(seq(1960, 2100, 1), "-01-01")),
               labels = date_format("%Y")) +
  scale_y_continuous(breaks = 0.01*seq(-100, 100, 1),
                     labels = percent_format(accuracy = 1)) +
  theme(legend.position = c(0.2, 0.85),
        legend.title = element_blank()) +
  xlab("") + ylab("Unemployment Rate, Wage Inflation (%)")

Portugal

tipsna62 |>
  filter(geo %in% c("PT"),
         unit == "THS_PER") |>
  select(geo, time, emp = values) |>
  left_join(tipslm13 |>
              select(geo, time, comp = values),
            by = c("geo", "time")) |>
  left_join(une_rt_a |>
              filter(age == "Y20-64",
                     sex == "T",
                     unit == "PC_ACT") |>
              select(geo, time, unr = values),
            by = c("geo", "time")) |>
  mutate(comp_emp = comp/emp,
         comp_emp_d1 = comp_emp/lag(comp_emp, 1)-1) |>
  year_to_enddate() |>
  transmute(date, comp_emp_d1=100*comp_emp_d1, unr = unr) |>
  gather(variable, value, -date) |>
  mutate(Variable = case_when(variable == "comp_emp_d1" ~ "Wage Inflation (%)",
                              variable == "unr" ~ "Unemployment Rate (%)")) |>
  ggplot() + geom_line() + theme_minimal()  +
  aes(x = date, y = value/100, color = Variable, linetype = Variable) +
  scale_color_manual(values = viridis(3)[1:2]) +
  scale_x_date(breaks = as.Date(paste0(seq(1960, 2100, 1), "-01-01")),
               labels = date_format("%Y")) +
  scale_y_continuous(breaks = 0.01*seq(-100, 100, 1),
                     labels = percent_format(accuracy = 1)) +
  theme(legend.position = c(0.2, 0.85),
        legend.title = element_blank()) +
  xlab("") + ylab("Unemployment Rate, Wage Inflation (%)")

Spain

tipsna62 |>
  filter(geo %in% c("ES"),
         unit == "THS_PER") |>
  select(geo, time, emp = values) |>
  left_join(tipslm13 |>
              select(geo, time, comp = values),
            by = c("geo", "time")) |>
  left_join(une_rt_a |>
              filter(age == "Y20-64",
                     sex == "T",
                     unit == "PC_ACT") |>
              select(geo, time, unr = values),
            by = c("geo", "time")) |>
  mutate(comp_emp = comp/emp,
         comp_emp_d1 = comp_emp/lag(comp_emp, 1)-1) |>
  year_to_enddate() |>
  transmute(date, comp_emp_d1=100*comp_emp_d1, unr = unr) |>
  gather(variable, value, -date) |>
  mutate(Variable = case_when(variable == "comp_emp_d1" ~ "Wage Inflation (%)",
                              variable == "unr" ~ "Unemployment Rate (%)")) |>
  ggplot() + geom_line() + theme_minimal()  +
  aes(x = date, y = value/100, color = Variable, linetype = Variable) +
  scale_color_manual(values = viridis(3)[1:2]) +
  scale_x_date(breaks = as.Date(paste0(seq(1960, 2100, 1), "-01-01")),
               labels = date_format("%Y")) +
  scale_y_continuous(breaks = 0.01*seq(-100, 100, 1),
                     labels = percent_format(accuracy = 1)) +
  theme(legend.position = c(0.2, 0.85),
        legend.title = element_blank()) +
  xlab("") + ylab("Unemployment Rate, Wage Inflation (%)")

Italy

tipsna62 |>
  filter(geo %in% c("IT"),
         unit == "THS_PER") |>
  select(geo, time, emp = values) |>
  left_join(tipslm13 |>
              select(geo, time, comp = values),
            by = c("geo", "time")) |>
  left_join(une_rt_a |>
              filter(age == "Y20-64",
                     sex == "T",
                     unit == "PC_ACT") |>
              select(geo, time, unr = values),
            by = c("geo", "time")) |>
  mutate(comp_emp = comp/emp,
         comp_emp_d1 = comp_emp/lag(comp_emp, 1)-1) |>
  year_to_enddate() |>
  transmute(date, comp_emp_d1=100*comp_emp_d1, unr = unr) |>
  gather(variable, value, -date) |>
  mutate(Variable = case_when(variable == "comp_emp_d1" ~ "Wage Inflation (%)",
                              variable == "unr" ~ "Unemployment Rate (%)")) |>
  ggplot() + geom_line() + theme_minimal()  +
  aes(x = date, y = value/100, color = Variable, linetype = Variable) +
  scale_color_manual(values = viridis(3)[1:2]) +
  scale_x_date(breaks = as.Date(paste0(seq(1960, 2100, 1), "-01-01")),
               labels = date_format("%Y")) +
  scale_y_continuous(breaks = 0.01*seq(-100, 100, 1),
                     labels = percent_format(accuracy = 1)) +
  theme(legend.position = c(0.4, 0.9),
        legend.title = element_blank()) +
  xlab("") + ylab("Unemployment Rate, Wage Inflation (%)")