Mean nominal monthly earnings of employees by sex and economic activity – Harmonized series - EAR_4MTH_SEX_ECO_CUR_NB_A

Data - ILO

Author

François Geerolf

Last observation: 2022 (N = 2628)

First observation: 1969 (N = 240)

Last data update: 01 août 2026, 21:27

Last compile: 02 sept. 2026, 22:57

ref_area

Code 
EAR_4MTH_SEX_ECO_CUR_NB_A |>
  left_join(ref_area, by = "ref_area") |>
  group_by(ref_area, Ref_area) |>
  summarise(Nobs = n()) |>
  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 .}

indicator

Code 
EAR_4MTH_SEX_ECO_CUR_NB_A |>
  left_join(indicator, by = "indicator") |>
  group_by(indicator, Indicator) |>
  summarise(Nobs = n()) |>
  print_table_conditional()
indicator Indicator Nobs
EAR_4MTH_SEX_ECO_CUR_NB Mean nominal monthly earnings of employees by sex and economic activity -- Harmonized series 340750

sex

Code 
EAR_4MTH_SEX_ECO_CUR_NB_A |>
  left_join(sex, by = "sex") |>
  group_by(sex, Sex) |>
  summarise(Nobs = n()) |>
  arrange(-Nobs) |>
  print_table_conditional()
sex Sex Nobs
SEX_T Sex: Total 135340
SEX_M Sex: Male 104335
SEX_F Sex: Female 101057
SEX_O Sex: Other 18

classif1

Code 
EAR_4MTH_SEX_ECO_CUR_NB_A |>
  left_join(classif1, by = "classif1") |>
  group_by(classif1, Classif1) |>
  summarise(Nobs = n()) |>
  print_table_conditional()

classif2

Code 
EAR_4MTH_SEX_ECO_CUR_NB_A |>
  left_join(classif2, by = "classif2") |>
  group_by(classif2, Classif2) |>
  summarise(Nobs = n()) |>
  print_table_conditional()
classif2 Classif2 Nobs
CUR_TYPE_LCU Currency: Local currency 129067
CUR_TYPE_PPP Currency: 2017 PPP $ 98895
CUR_TYPE_USD Currency: U.S. dollars 112788

source

Code 
EAR_4MTH_SEX_ECO_CUR_NB_A |>
  left_join(source, by = "source") |>
  group_by(source, Source) |>
  summarise(Nobs = n()) |>
  arrange(-Nobs) |>
  print_table_conditional()

ECO_AGGREGATE_TOTAL, CUR_TYPE_LCU

Table

Code 
EAR_4MTH_SEX_ECO_CUR_NB_A |>
  filter(classif1 == "ECO_AGGREGATE_TOTAL",
         sex == "SEX_T") |>
  left_join(ref_area, by = "ref_area") |>
  group_by(ref_area, Ref_area, classif2) |>
  summarise(Nobs = n()) |>
  spread(classif2, Nobs) |>
  arrange(-CUR_TYPE_LCU) |>
  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 .}

China

Code 
EAR_4MTH_SEX_ECO_CUR_NB_A |>
  filter(classif1 == "ECO_AGGREGATE_TOTAL",
         sex == "SEX_T",
         ref_area == "CHN") |>
  left_join(classif2, by = "classif2") |>
  year_to_date() |>
  ggplot() + geom_line(aes(x = date, y = obs_value, color = Classif2)) +
  scale_color_manual(values = viridis(4)[1:3]) +
  theme_minimal() + xlab("") + ylab("") +
  scale_x_date(breaks = as.Date(paste0(seq(1960, 2020, 5), "-01-01")),
               labels = date_format("%Y")) +
  theme(legend.position = c(0.3, 0.8),
        legend.title = element_blank()) +
  scale_y_log10(breaks = c(1000, 2000, 3000, 5000, 8000, 10000, 20000, 30000, 50000),
                labels = dollar_format(suffix = "", prefix = "", accuracy = 1))

Individual Countries

Argentina

Table

Code 
EAR_4MTH_SEX_ECO_CUR_NB_A |>
  filter(ref_area == "ARG",
         classif2 == "CUR_TYPE_LCU",
         sex == "SEX_T") |>
  left_join(classif1, by = "classif1") |>
  group_by(classif1, Classif1) |>
  summarise(Nobs = n()) |>
  print_table_conditional()

Essai

Code 
EAR_4MTH_SEX_ECO_CUR_NB_A |>
  filter(ref_area == "ARG",
         classif1 %in% c("ECO_AGGREGATE_MAN", "ECO_AGGREGATE_TOTAL"),
         classif2 == "CUR_TYPE_LCU",
         sex == "SEX_T") |>
  left_join(classif1, by = "classif1") |>
  year_to_date() |>
  ggplot() + geom_line(aes(x = date, y = obs_value, color = Classif1)) +
  scale_color_manual(values = viridis(3)[1:2]) +
  theme_minimal() + xlab("") + ylab("") +
  scale_x_date(breaks = as.Date(paste0(seq(1960, 2020, 1), "-01-01")),
               labels = date_format("%Y")) +
  theme(legend.position = c(0.3, 0.8),
        legend.title = element_blank()) +
  scale_y_log10(breaks = c(1000, 2000, 3000, 5000, 8000, 10000, 20000, 30000, 50000),
                labels = dollar_format(suffix = "", prefix = "", accuracy = 1))

France

Code 
EAR_4MTH_SEX_ECO_CUR_NB_A |>
  filter(ref_area == "FRA",
         classif1 %in% c("ECO_AGGREGATE_MAN", "ECO_AGGREGATE_TOTAL"),
         classif2 == "CUR_TYPE_LCU",
         sex == "SEX_T") |>
  left_join(classif1, by = "classif1") |>
  year_to_date() |>
  ggplot() + geom_line(aes(x = date, y = obs_value, color = Classif1)) +
  scale_color_manual(values = viridis(3)[1:2]) +
  theme_minimal() + xlab("") + ylab("") +
  scale_x_date(breaks = as.Date(paste0(seq(1960, 2020, 1), "-01-01")),
               labels = date_format("%Y")) +
  theme(legend.position = c(0.7, 0.2),
        legend.title = element_blank()) +
  scale_y_log10(breaks = seq(200, 3000, 200),
                labels = dollar_format(suffix = "", prefix = "", accuracy = 1))