Last observation: 2025 (N = 30)
First observation: 1995 (N = 30)
Last data update: 14 aoû 2026, 21:39. Last compile: 18 aoû 2026, 04:34
Compensation of employees - annual data, million units of national currency - tipslm13
Data - Eurostat
Info
Structure
France, Germany, Portugal
Code
tipslm13 |>
# CP_MNAC Current prices, million units of national currency
filter(geo %in% c("FR", "DE", "PT")) |>
year_to_enddate() |>
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, 1), "-01-01")),
labels = date_format("%Y")) +
theme(legend.position = c(0.3, 0.85),
legend.title = element_blank()) +
xlab("") + ylab("Compensation of employees")
Phillips curves
Germany
Code
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
Code
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
Code
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
Code
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
Code
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 (%)")