Compensation of employees - annual data, million units of national currency - tipslm13

Data - Eurostat

Author

François Geerolf

geo

Code 
tipslm13 %>%
  left_join(geo, by = "geo") %>%
  group_by(geo, Geo) %>%
  summarise(Nobs = n()) %>%
  arrange(-Nobs) %>%
  {if (is_html_output()) datatable(., filter = 'top', rownames = F) else .}

unit

Code 
tipslm13 %>%
  left_join(unit, by = "unit") %>%
  group_by(unit, Unit) %>%
  summarise(Nobs = n()) %>%
  arrange(-Nobs) %>%
  {if (is_html_output()) print_table(.) else .}
unit Unit Nobs
CP_MNAC Current prices, million units of national currency 783

na_item

Code 
tipslm13 %>%
  left_join(na_item, by = "na_item") %>%
  group_by(na_item, Na_item) %>%
  summarise(Nobs = n()) %>%
  arrange(-Nobs) %>%
  {if (is_html_output()) print_table(.) else .}
na_item Na_item Nobs
D1 Compensation of employees 783

France, Germany, Portugal

Code 
tipslm13 %>%
  # CP_MNAC Current prices, million units of national currency
  filter(geo %in% c("FR", "DE", "PT")) %>%
  year_to_enddate %>%
  left_join(geo, by = "geo") %>%
  ggplot + geom_line() + theme_minimal()  +
  aes(x = date, y = values/100, color = Geo, linetype = Geo) +
  scale_color_manual(values = viridis(4)[1:3]) +
  scale_x_date(breaks = as.Date(paste0(seq(1960, 2020, 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, 2020, 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, 2020, 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, 2020, 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, 2020, 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, 2020, 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 (%)")