Mean nominal monthly earnings of employees by sex and economic activity – Harmonized series - EAR_4MTH_SEX_OCU_CUR_NB_A

Data - ILO

Author

François Geerolf

ref_area

Code 
EAR_4MTH_SEX_OCU_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_OCU_CUR_NB_A %>%
  left_join(indicator, by = "indicator") %>%
  group_by(indicator, Indicator) %>%
  summarise(Nobs = n()) %>%
  print_table_conditional
indicator Indicator Nobs
EAR_4MTH_SEX_OCU_CUR_NB Mean nominal monthly earnings of employees by sex and occupation -- Harmonized series 160971

sex

Code 
EAR_4MTH_SEX_OCU_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 55090
SEX_M Sex: Male 54224
SEX_F Sex: Female 51639
SEX_O Sex: Other 18

classif1

Code 
EAR_4MTH_SEX_OCU_CUR_NB_A %>%
  left_join(classif1, by = "classif1") %>%
  group_by(classif1, Classif1) %>%
  summarise(Nobs = n()) %>%
  print_table_conditional
classif1 Classif1 Nobs
OCU_ISCO08_0 Occupation (ISCO-08): 0. Armed forces occupations 3668
OCU_ISCO08_1 Occupation (ISCO-08): 1. Managers 7737
OCU_ISCO08_2 Occupation (ISCO-08): 2. Professionals 7722
OCU_ISCO08_3 Occupation (ISCO-08): 3. Technicians and associate professionals 7668
OCU_ISCO08_4 Occupation (ISCO-08): 4. Clerical support workers 7791
OCU_ISCO08_5 Occupation (ISCO-08): 5. Service and sales workers 7785
OCU_ISCO08_6 Occupation (ISCO-08): 6. Skilled agricultural, forestry and fishery workers 7017
OCU_ISCO08_7 Occupation (ISCO-08): 7. Craft and related trades workers 7746
OCU_ISCO08_8 Occupation (ISCO-08): 8. Plant and machine operators, and assemblers 7607
OCU_ISCO08_9 Occupation (ISCO-08): 9. Elementary occupations 7794
OCU_ISCO08_TOTAL Occupation (ISCO-08): Total 7965
OCU_ISCO08_X Occupation (ISCO-08): X. Not elsewhere classified 2488
OCU_ISCO88_0 Occupation (ISCO-88): 0. Armed forces 1814
OCU_ISCO88_1 Occupation (ISCO-88): 1. Legislators, senior officials and managers 3491
OCU_ISCO88_2 Occupation (ISCO-88): 2. Professionals 3491
OCU_ISCO88_3 Occupation (ISCO-88): 3. Technicians and associate professionals 3443
OCU_ISCO88_4 Occupation (ISCO-88): 4. Clerks 3488
OCU_ISCO88_5 Occupation (ISCO-88): 5. Service workers and shop and market sales workers 3467
OCU_ISCO88_6 Occupation (ISCO-88): 6. Skilled agricultural and fishery workers 3036
OCU_ISCO88_7 Occupation (ISCO-88): 7. Craft and related trades workers 3419
OCU_ISCO88_8 Occupation (ISCO-88): 8. Plant and machine operators and assemblers 3451
OCU_ISCO88_9 Occupation (ISCO-88): 9. Elementary occupations 3461
OCU_ISCO88_TOTAL Occupation (ISCO-88): Total 3507
OCU_ISCO88_X Occupation (ISCO-88): X. Not elsewhere classified 1427
OCU_SKILL_L1 Occupation (Skill level): Skill level 1 (low) 7760
OCU_SKILL_L2 Occupation (Skill level): Skill level 2 (medium) 7764
OCU_SKILL_L3-4 Occupation (Skill level): Skill levels 3 and 4 (high) 7764
OCU_SKILL_TOTAL Occupation (Skill level): Total 11463
OCU_SKILL_X Occupation (Skill level): Not elsewhere classified 5737

classif2

Code 
EAR_4MTH_SEX_OCU_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 60383
CUR_TYPE_PPP Currency: 2017 PPP $ 49433
CUR_TYPE_USD Currency: U.S. dollars 51155

source

Code 
EAR_4MTH_SEX_OCU_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_OCU_CUR_NB_A %>%
  filter(classif1 == "OCU_ISCO08_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 .}

Individual Countries

Argentina

Table

Code 
EAR_4MTH_SEX_OCU_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
classif1 Classif1 Nobs
OCU_ISCO08_1 Occupation (ISCO-08): 1. Managers 15
OCU_ISCO08_2 Occupation (ISCO-08): 2. Professionals 15
OCU_ISCO08_3 Occupation (ISCO-08): 3. Technicians and associate professionals 15
OCU_ISCO08_4 Occupation (ISCO-08): 4. Clerical support workers 15
OCU_ISCO08_5 Occupation (ISCO-08): 5. Service and sales workers 15
OCU_ISCO08_6 Occupation (ISCO-08): 6. Skilled agricultural, forestry and fishery workers 15
OCU_ISCO08_7 Occupation (ISCO-08): 7. Craft and related trades workers 15
OCU_ISCO08_8 Occupation (ISCO-08): 8. Plant and machine operators, and assemblers 15
OCU_ISCO08_9 Occupation (ISCO-08): 9. Elementary occupations 15
OCU_ISCO08_TOTAL Occupation (ISCO-08): Total 15
OCU_ISCO08_X Occupation (ISCO-08): X. Not elsewhere classified 15
OCU_SKILL_L1 Occupation (Skill level): Skill level 1 (low) 15
OCU_SKILL_L2 Occupation (Skill level): Skill level 2 (medium) 15
OCU_SKILL_L3-4 Occupation (Skill level): Skill levels 3 and 4 (high) 15
OCU_SKILL_TOTAL Occupation (Skill level): Total 15
OCU_SKILL_X Occupation (Skill level): Not elsewhere classified 15

Essai

Code 
EAR_4MTH_SEX_OCU_CUR_NB_A %>%
  filter(ref_area == "ARG",
         classif1 %in% c("OCU_ISCO08_TOTAL", "OCU_ISCO88_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_OCU_CUR_NB_A %>%
  filter(ref_area == "FRA",
         classif1 %in% c("OCU_ISCO08_TOTAL", "OCU_ISCO88_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))