| source | dataset | Title | .html | .rData |
|---|---|---|---|---|
| oecd | AV_AN_WAGE | Average annual wages | 2026-01-11 | 2026-01-10 |
Average annual wages
Data - OECD
Info
Data on wages
Code
load_data("wages.RData")
wages %>%
arrange(-(dataset == "AV_AN_WAGE")) %>%
source_dataset_file_updates()| source | dataset | Title | .html | .rData |
|---|---|---|---|---|
| oecd | AV_AN_WAGE | Average annual wages | 2026-01-11 | 2026-01-10 |
| eurostat | earn_mw_cur | Monthly minimum wages - bi-annual data | 2026-01-11 | 2026-01-07 |
| eurostat | ei_lmlc_q | Labour cost index, nominal value - quarterly data | 2026-01-11 | 2026-01-07 |
| eurostat | lc_lci_lev | Labour cost levels by NACE Rev. 2 activity | 2026-01-09 | 2026-01-07 |
| eurostat | lc_lci_r2_q | Labour cost index by NACE Rev. 2 activity - nominal value, quarterly data | 2026-01-09 | 2026-01-07 |
| eurostat | nama_10_lp_ulc | Labour productivity and unit labour costs | 2026-01-09 | 2026-01-07 |
| eurostat | namq_10_lp_ulc | Labour productivity and unit labour costs | 2026-01-10 | 2026-01-07 |
| eurostat | tps00155 | Minimum wages | 2026-01-10 | 2026-01-07 |
| fred | wage | Wage | 2026-01-11 | 2026-01-07 |
| ilo | EAR_4MTH_SEX_ECO_CUR_NB_A | Mean nominal monthly earnings of employees by sex and economic activity -- Harmonized series | 2024-06-20 | 2023-06-01 |
| ilo | EAR_XEES_SEX_ECO_NB_Q | Mean nominal monthly earnings of employees by sex and economic activity -- Harmonized series | 2024-06-20 | 2023-06-01 |
| oecd | AWCOMP | Taxing Wages - Comparative tables | 2026-01-11 | 2023-09-09 |
| oecd | EAR_MEI | Hourly Earnings (MEI) | 2024-04-16 | 2024-04-16 |
| oecd | HH_DASH | Household Dashboard | 2026-01-11 | 2023-09-09 |
| oecd | MIN2AVE | Minimum relative to average wages of full-time workers - MIN2AVE | 2026-01-11 | 2023-09-09 |
| oecd | RMW | Real Minimum Wages - RMW | 2026-01-11 | 2024-03-12 |
| oecd | ULC_EEQ | Unit labour costs and labour productivity (employment based), Total economy | 2026-01-11 | 2024-04-15 |
LAST_COMPILE
| LAST_COMPILE |
|---|
| 2026-01-15 |
Last
| obsTime | Nobs |
|---|---|
| 2024 | 109 |
UNIT_MEASURE
Code
AV_AN_WAGE %>%
left_join(UNIT_MEASURE, by = "UNIT_MEASURE") %>%
group_by(UNIT_MEASURE, Unit_measure) %>%
summarise(nobs = n()) %>%
arrange(-nobs) %>%
print_table_conditional()| UNIT_MEASURE | Unit_measure | nobs |
|---|---|---|
| USD_PPP | US dollars, PPP converted | 1281 |
| EUR | Euro | 1171 |
| AUD | Australian dollar | 70 |
| CAD | Canadian dollar | 70 |
| CHF | Swiss franc | 70 |
| DKK | Danish krone | 70 |
| GBP | Pound sterling | 70 |
| ISK | Iceland krona | 70 |
| JPY | Yen | 70 |
| KRW | Won | 70 |
| MXN | Mexican peso | 70 |
| NOK | Norwegian krone | 70 |
| NZD | New Zealand dollar | 70 |
| SEK | Swedish krona | 70 |
| USD | US dollar | 70 |
| CRC | Costa Rican colon | 66 |
| CZK | Czech koruna | 60 |
| HUF | Forint | 60 |
| ILS | New Israeli sheqel | 60 |
| PLN | Zloty | 60 |
| CLP | Chilean peso | 56 |
| BGN | Bulgarian lev | 55 |
| RON | Romanian leu | 55 |
| TRY | Turkish lira | 52 |
| COP | Colombian peso | 38 |
PRICE_BASE
Code
AV_AN_WAGE %>%
left_join(PRICE_BASE, by = "PRICE_BASE") %>%
group_by(PRICE_BASE, Price_base) %>%
summarise(nobs = n()) %>%
arrange(-nobs) %>%
print_table_conditional()| PRICE_BASE | Price_base | nobs |
|---|---|---|
| Q | Constant prices | 2594 |
| V | Current prices | 1330 |
REF_AREA
Code
AV_AN_WAGE %>%
left_join(REF_AREA, by = "REF_AREA") %>%
group_by(REF_AREA, Ref_area) %>%
summarise(nobs = n()) %>%
arrange(-nobs) %>%
print_table_conditional()USD_PPP - US dollars, PPP converted
France, Germany, Italy, Spain, USA
Tous
Code
AV_AN_WAGE %>%
filter(PRICE_BASE == "Q",
UNIT_MEASURE == "USD_PPP",
REF_AREA %in% c("FRA", "DEU", "ITA", "ESP", "USA")) %>%
left_join(REF_AREA, by = "REF_AREA") %>%
year_to_date() %>%
#filter(date >= as.Date("1996-01-01")) %>%
group_by(Ref_area) %>%
arrange(date) %>%
mutate(obsValue = 100*obsValue/obsValue[1]) %>%
left_join(colors, by = c("Ref_area" = "country")) %>%
ggplot(.) + geom_line(aes(x = date, y = obsValue, color = color)) +
scale_color_identity() + add_5flags + theme_minimal() +
scale_x_date(breaks = seq(1920, 2100, 2) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
scale_y_log10(breaks = seq(10, 500, 10)) +
ylab("Current prices in NCU") + xlab("")
1996-
Code
AV_AN_WAGE %>%
filter(PRICE_BASE == "Q",
UNIT_MEASURE == "USD_PPP",
REF_AREA %in% c("FRA", "DEU", "ITA", "ESP", "USA")) %>%
left_join(REF_AREA, by = "REF_AREA") %>%
year_to_date() %>%
filter(date >= as.Date("1996-01-01")) %>%
group_by(Ref_area) %>%
arrange(date) %>%
mutate(obsValue = 100*obsValue/obsValue[1]) %>%
left_join(colors, by = c("Ref_area" = "country")) %>%
ggplot(.) + geom_line(aes(x = date, y = obsValue, color = color)) +
scale_color_identity() + add_5flags + theme_minimal() +
scale_x_date(breaks = seq(1920, 2100, 2) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
scale_y_log10(breaks = seq(10, 500, 10)) +
ylab("Current prices in NCU") + xlab("")
Current Prices
France, Germany, Italy, Spain, USA
Tous
Code
AV_AN_WAGE %>%
filter(PRICE_BASE == "V",
REF_AREA %in% c("FRA", "DEU", "ITA", "ESP", "USA")) %>%
left_join(REF_AREA, by = "REF_AREA") %>%
year_to_date() %>%
#filter(date >= as.Date("1996-01-01")) %>%
group_by(Ref_area) %>%
arrange(date) %>%
mutate(obsValue = 100*obsValue/obsValue[1]) %>%
left_join(colors, by = c("Ref_area" = "country")) %>%
ggplot(.) + geom_line(aes(x = date, y = obsValue, color = color)) +
scale_color_identity() + add_5flags + theme_minimal() +
scale_x_date(breaks = seq(1920, 2100, 2) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
scale_y_log10(breaks = seq(10, 500, 10)) +
ylab("Current prices in NCU") + xlab("")
1992-
Code
AV_AN_WAGE %>%
filter(PRICE_BASE == "V",
REF_AREA %in% c("FRA", "DEU", "ITA", "ESP", "USA")) %>%
left_join(REF_AREA, by = "REF_AREA") %>%
year_to_date() %>%
filter(date >= as.Date("1992-01-01")) %>%
group_by(Ref_area) %>%
arrange(date) %>%
mutate(obsValue = 100*obsValue/obsValue[1]) %>%
left_join(colors, by = c("Ref_area" = "country")) %>%
ggplot(.) + geom_line(aes(x = date, y = obsValue, color = color)) +
scale_color_identity() + add_5flags + theme_minimal() +
scale_x_date(breaks = seq(1920, 2100, 2) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
scale_y_log10(breaks = seq(10, 500, 10)) +
ylab("Current prices in NCU") + xlab("")
1996-
Code
AV_AN_WAGE %>%
filter(PRICE_BASE == "V",
REF_AREA %in% c("FRA", "DEU", "ITA", "ESP", "USA")) %>%
left_join(REF_AREA, by = "REF_AREA") %>%
year_to_date() %>%
filter(date >= as.Date("1996-01-01")) %>%
group_by(Ref_area) %>%
arrange(date) %>%
mutate(obsValue = 100*obsValue/obsValue[1]) %>%
left_join(colors, by = c("Ref_area" = "country")) %>%
ggplot(.) + geom_line(aes(x = date, y = obsValue, color = color)) +
scale_color_identity() + add_5flags + theme_minimal() +
scale_x_date(breaks = seq(1920, 2100, 2) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
scale_y_log10(breaks = seq(10, 500, 10)) +
ylab("Current prices in NCU") + xlab("")