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

Data - ILO

Author

ref_area

Code

EAR_XEES_SEX_ECO_NB_Q %>%
  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_XEES_SEX_ECO_NB_Q %>%
  left_join(indicator, by = "indicator") %>%
  group_by(indicator, Indicator) %>%
  summarise(Nobs = n()) %>%
  print_table_conditional

indicator	Indicator	Nobs
EAR_XEES_SEX_ECO_NB	Mean nominal monthly earnings of employees by sex and economic activity (local currency)	144154

sex

Code

EAR_XEES_SEX_ECO_NB_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	48951
SEX_M	Sex: Male	47918
SEX_F	Sex: Female	47175
SEX_O	Sex: Other	110

classif1

Code

EAR_XEES_SEX_ECO_NB_Q %>%
  left_join(classif1, by = "classif1") %>%
  group_by(classif1, Classif1) %>%
  summarise(Nobs = n()) %>%
  print_table_conditional

source

Code

EAR_XEES_SEX_ECO_NB_Q %>%
  left_join(source, by = "source") %>%
  group_by(source, Source) %>%
  summarise(Nobs = n()) %>%
  arrange(-Nobs) %>%
  print_table_conditional