Minimum relative to average wages of full-time workers - MIN2AVE

Data - OECD

Author

François Geerolf

Info

Last observation: A: 2022 (N = 64)

First observation: A: 1960 (N = 10)

Last data update: 02 aoû 2026, 09:01. Last compile: 18 aoû 2026, 01:07

Structure

1er janvier 2023

  • SMIC Brut: 1.709,28€ / mois

  • Net: 1.353,07€ / mois

  • Selon le calculateur URSSAF. html

2019 - ordres de grandeur - france

  • SMIC / Médian (Net) = 1204€ / 1940€ = 62%

  • SMIC / Médian (Brut) = 1549€ / 2478€ = 62.5%

  • SMIC / Médian (Super Brut) = 1616€ / 3320€ = 48.6%

  • SMIC / Moyen (Net) = 1204€ / 2424€ = 49.6%

  • SMIC / Moyen (Brut) = 1549€ / 3090€ = 50.1%

  • SMIC / Moyen (Super Brut) = 1616€ / 4191€ = 38.5%

Minimum wages

Code 
i_g("bib/oecd/Minimum-wages-in-times-of-rising-inflation/figure2.png")

Données

Code 
i_g("bib/france/SMIC-salaire.jpeg")

Groupe d’expert

Code 
i_g("bib/Tresor/SMIC_Rapport_2020/rapport-cout.png")

Estimateur de cotisations URSSAF

Code 
i_g("bib/france/URSSAF-1204.png")

Code 
i_g("bib/france/URSSAF-1940.png")

Code 
i_g("bib/france/URSSAF-2424.png")

MIN2AVE

Code 
MIN2AVE |>
  left_join(MIN2AVE_var$SERIES, by = "SERIES") |>
  group_by(SERIES, Series) |>
  summarise(Nobs = n()) |>
  arrange(-Nobs) |>
  print_table_conditional()
SERIES Series Nobs
MEAN Mean 1220
MEDIAN Median 1197

Minimum to Median Wage

Table

Code 
MIN2AVE |>
  filter(SERIES == "MEDIAN") |>
  left_join(MIN2AVE_var$COUNTRY, by = "COUNTRY") |>
  group_by(COUNTRY, Country) |>
  summarise(Nobs = n(),
            `1999` = ifelse(any(obsTime == "1999"), obsValue[obsTime == "1999"], NA_real_),
            `2009` = ifelse(any(obsTime == "2009"), obsValue[obsTime == "2009"], NA_real_),
            `2019` = ifelse(any(obsTime == "2019"), obsValue[obsTime == "2019"], NA_real_)) |>
  mutate(Flag = gsub(" ", "-", str_to_lower(Country)),
         Flag = paste0('<img src="../../icon/flag/vsmall/', Flag, '.png" alt="Flag">')) |>
  select(Flag, everything()) %>%
  {if (is_html_output()) datatable(., filter = 'top', rownames = F, escape = F) else .}

US, Japan, UK, Canada

Code 
MIN2AVE |>
  filter(SERIES == "MEDIAN",
         COUNTRY %in% c("USA", "JPN", "GBR", "CAN")) |>
  year_to_date() |>
  left_join(MIN2AVE_var$COUNTRY, by = "COUNTRY") |>
  left_join(colors, by = c("Country" = "country")) |>
  rename(Location = Country) |>
  ggplot() +  scale_color_identity() + theme_minimal() +
  geom_line(aes(x = date, y = obsValue, color = color)) + add_4flags +
  scale_x_date(breaks = seq(1920, 2100, 5) |> paste0("-01-01") |> as.Date(),
               labels = date_format("%Y")) +
  theme(legend.position = c(0.2, 0.8),
        legend.title = element_blank()) +
  scale_y_continuous(breaks = 0.01*seq(0, 100, 5),
                     labels = percent_format(accuracy = 1)) + 
  ylab("Ratio of Minimum Wage to Average (%)") + xlab("")

France, Germany, Spain, Portugal

Code 
MIN2AVE |>
  filter(SERIES == "MEDIAN",
         COUNTRY %in% c("FRA", "ESP", "DEU", "PRT")) |>
  year_to_date() |>
  left_join(MIN2AVE_var$COUNTRY, by = "COUNTRY") |>
  left_join(colors, by = c("Country" = "country")) |>
  rename(Location = Country) |>
  ggplot() +  scale_color_identity() + theme_minimal() +
  geom_line(aes(x = date, y = obsValue, color = color)) + add_4flags +
  scale_x_date(breaks = seq(1920, 2100, 5) |> paste0("-01-01") |> as.Date(),
               labels = date_format("%Y")) +
  theme(legend.position = c(0.2, 0.8),
        legend.title = element_blank()) +
  scale_y_continuous(breaks = 0.01*seq(0, 100, 5),
                     labels = percent_format(accuracy = 1)) + 
  ylab("Ratio of Minimum Wage to Average (%)") + xlab("")

Example countries

France

Code 
MIN2AVE |>
  filter(COUNTRY == "FRA") |>
  year_to_date() |>
  left_join(MIN2AVE_var$SERIES, by = "SERIES") |>
  ggplot() + 
  geom_line(aes(x = date, y = obsValue, color = Series, linetype = Series)) +
  scale_color_manual(values = viridis(3)[1:2]) +
  theme_minimal() +
  scale_x_date(breaks = seq(1920, 2100, 5) |> paste0("-01-01") |> as.Date(),
               labels = date_format("%Y")) +
  theme(legend.position = c(0.2, 0.8),
        legend.title = element_blank()) +
  scale_y_continuous(breaks = 0.01*seq(0, 100, 5),
                     labels = percent_format(accuracy = 1)) + 
  ylab("Ratio of Minimum Wage to Average (%)") + xlab("")

Germany

Code 
MIN2AVE |>
  filter(COUNTRY == "DEU") |>
  year_to_date() |>
  left_join(MIN2AVE_var$SERIES, by = "SERIES") |>
  ggplot() + 
  geom_line(aes(x = date, y = obsValue, color = Series, linetype = Series)) +
  scale_color_manual(values = viridis(3)[1:2]) + theme_minimal() +
  scale_x_date(breaks = seq(1920, 2100, 1) |> paste0("-01-01") |> as.Date(),
               labels = date_format("%Y")) +
  theme(legend.position = c(0.65, 0.6),
        legend.title = element_blank()) +
  scale_y_continuous(breaks = 0.01*seq(0, 100, 1),
                     labels = percent_format(accuracy = 1)) + 
  ylab("Ratio of Minimum Wage to Average (%)") + xlab("")