Last observation: 2022 (N = 1472)
First observation: 1991 (N = 60)
Last data update: 01 août 2026, 21:27
Last compile: 14 sept. 2026, 22:12
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))