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

Data - ILO

Author

Info

Data on wages

ILO. html

Code

wages %>%
  mutate(Title = read_lines(paste0("~/Library/Mobile\ Documents/com~apple~CloudDocs/website/data/", source, "/",dataset, ".qmd"), skip = 1, n_max = 1) %>% gsub("title: ", "", .) %>% gsub("\"", "", .)) %>%
  mutate(Download = as.Date(file.info(paste0("~/Library/Mobile\ Documents/com~apple~CloudDocs/website/data/", source, "/", dataset, ".RData"))$mtime),
         Compile = as.Date(file.info(paste0("~/Library/Mobile\ Documents/com~apple~CloudDocs/website/data/", source, "/", dataset, ".html"))$mtime)) %>%
  mutate(Compile = paste0("[", Compile, "](https://fgeerolf.com/data/", source, "/", dataset, '.html)')) %>%
  print_table_conditional()

source	dataset	Title	Download	Compile
eurostat	earn_mw_cur	Monthly minimum wages - bi-annual data	2024-06-08	[2024-06-20]
eurostat	ei_lmlc_q	Labour cost index, nominal value - quarterly data	2024-06-08	[2024-06-20]
eurostat	lc_lci_lev	Labour cost levels by NACE Rev. 2 activity	2024-06-08	[2024-06-20]
eurostat	lc_lci_r2_q	Labour cost index by NACE Rev. 2 activity - nominal value, quarterly data	2024-06-08	[2024-06-18]
eurostat	nama_10_lp_ulc	Labour productivity and unit labour costs	2024-06-08	[2024-06-20]
eurostat	namq_10_lp_ulc	Labour productivity and unit labour costs	2024-06-08	[2024-06-20]
eurostat	tps00155	Minimum wages	2024-06-08	[2024-06-20]
fred	wage	Wage	2024-06-07	[2024-06-20]
ilo	EAR_4MTH_SEX_ECO_CUR_NB_A	Mean nominal monthly earnings of employees by sex and economic activity -- Harmonized series	2023-06-01	[2024-06-20]
ilo	EAR_XEES_SEX_ECO_NB_Q	Mean nominal monthly earnings of employees by sex and economic activity -- Harmonized series	2023-06-01	[2024-06-20]
oecd	AV_AN_WAGE	Average annual wages	2023-09-09	[2024-04-16]
oecd	AWCOMP	Taxing Wages - Comparative tables	2023-09-09	[2024-06-19]
oecd	EAR_MEI	Hourly Earnings (MEI)	2024-04-16	[2024-04-16]
oecd	HH_DASH	Household Dashboard	2023-09-09	[2024-06-19]
oecd	MIN2AVE	Minimum relative to average wages of full-time workers - MIN2AVE	2023-09-09	[2024-06-19]
oecd	RMW	Real Minimum Wages - RMW	2024-03-12	[2024-06-19]
oecd	ULC_EEQ	Unit labour costs and labour productivity (employment based), Total economy	2024-04-15	[2024-06-20]

Données sur les salaires

Code

salaires %>%
  mutate(Title = read_lines(paste0("~/Library/Mobile\ Documents/com~apple~CloudDocs/website/data/", source, "/",dataset, ".qmd"), skip = 1, n_max = 1) %>% gsub("title: ", "", .) %>% gsub("\"", "", .)) %>%
  mutate(Download = as.Date(file.info(paste0("~/Library/Mobile\ Documents/com~apple~CloudDocs/website/data/", source, "/", dataset, ".RData"))$mtime),
         Compile = as.Date(file.info(paste0("~/Library/Mobile\ Documents/com~apple~CloudDocs/website/data/", source, "/", dataset, ".html"))$mtime)) %>%
  mutate(Compile = paste0("[", Compile, "](https://fgeerolf.com/data/", source, "/", dataset, '.html)')) %>%
  print_table_conditional()

source	dataset	Title	Download	Compile
dares	les-indices-de-salaire-de-base	Les indices de salaire de base	2024-03-25	[2024-06-17]
insee	CNA-2014-RDB	Revenu et pouvoir d’achat des ménages	2024-06-18	[2024-06-20]
insee	CNT-2014-CSI	Comptes de secteurs institutionnels	2024-06-18	[2024-06-20]
insee	ECRT2023	Emploi, chômage, revenus du travail - Edition 2023	2023-06-30	[2024-06-20]
insee	INDICE-TRAITEMENT-FP	Indice de traitement brut dans la fonction publique de l'État	2024-06-18	[2024-06-20]
insee	SALAIRES-ACEMO	Indices trimestriels de salaires dans le secteur privé - Résultats par secteur d’activité	2024-06-19	[2024-06-19]
insee	SALAIRES-ACEMO-2017	Indices trimestriels de salaires dans le secteur privé	2024-06-19	[2024-06-20]
insee	SALAIRES-ANNUELS	Salaires annuels	2024-06-19	[2024-06-19]
insee	if230	Séries longues sur les salaires dans le secteur privé	2021-12-04	[2024-06-20]
insee	ir_salaires_SL_csv	Séries longues sur les salaires dans le secteur privé - Base Tous salariés - Insee Résultats	NA	[2024-06-20]
insee	t_7401	7.401 – Compte des ménages (S14) (En milliards d'euros)	2023-12-23	[2024-06-19]
insee	t_salaire_val	Salaire moyen par tête - SMPT (données CVS)	2024-03-04	[2024-06-19]

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))