Code
i_g("bib/oecd/Minimum-wages-in-times-of-rising-inflation/figure2.png")
Data - OECD
SMIC Brut: 1.709,28€ / mois
Net: 1.353,07€ / mois
Selon le calculateur URSSAF. html
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%
i_g("bib/oecd/Minimum-wages-in-times-of-rising-inflation/figure2.png")
i_g("bib/france/SMIC-salaire.jpeg")
i_g("bib/Tresor/SMIC_Rapport_2020/rapport-cout.png")
i_g("bib/france/URSSAF-1204.png")
i_g("bib/france/URSSAF-1940.png")
i_g("bib/france/URSSAF-2424.png")
%>%
MIN2AVE left_join(MIN2AVE_var$COUNTRY, by = "COUNTRY") %>%
group_by(COUNTRY, Country) %>%
summarise(Nobs = n()) %>%
arrange(-Nobs) %>%
mutate(Flag = gsub(" ", "-", str_to_lower(Country)),
Flag = paste0('<img src="../../icon/flag/vsmall/', Flag, '.png" alt="Flag">')) %>%
select(Flag, everything()) %>%
if (is_html_output()) datatable(., filter = 'top', rownames = F, escape = F) else .} {
%>%
MIN2AVE left_join(MIN2AVE_var$SERIES, by = "SERIES") %>%
group_by(SERIES, Series) %>%
summarise(Nobs = n()) %>%
arrange(-Nobs) %>%
print_table_conditional()
SERIES | Series | Nobs |
---|---|---|
MEAN | Mean | 1220 |
MEDIAN | Median | 1197 |
%>%
MIN2AVE filter(SERIES == "MEDIAN") %>%
left_join(MIN2AVE_var$COUNTRY, by = "COUNTRY") %>%
group_by(COUNTRY, Country) %>%
summarise(Nobs = n(),
`1999` = obsValue[obsTime == "1999"],
`2009` = obsValue[obsTime == "2009"],
`2019` = obsValue[obsTime == "2019"]) %>%
mutate(Flag = gsub(" ", "-", str_to_lower(Country)),
Flag = paste0('<img src="../../icon/flag/vsmall/', Flag, '.png" alt="Flag">')) %>%
select(Flag, everything()) %>%
if (is_html_output()) datatable(., filter = 'top', rownames = F, escape = F) else .} {
%>%
MIN2AVE filter(SERIES == "MEDIAN",
%in% c("USA", "JPN", "GBR", "CAN")) %>%
COUNTRY %>%
year_to_date left_join(MIN2AVE_var$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, 2025, 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("")
%>%
MIN2AVE filter(SERIES == "MEDIAN",
%in% c("FRA", "ESP", "DEU", "PRT")) %>%
COUNTRY %>%
year_to_date left_join(MIN2AVE_var$COUNTRY, by = "COUNTRY") %>%
left_join(colors, by = c("Country" = "country")) %>%
rename(Location = Country) %>%
ggplot() + scale_color_identity() + theme_minimal() +
geom_line(aes(x = date, y = obsValue, color = color)) + add_4flags +
scale_x_date(breaks = seq(1920, 2025, 5) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%y")) +
theme(legend.position = c(0.2, 0.8),
legend.title = element_blank()) +
scale_y_continuous(breaks = 0.01*seq(0, 100, 5),
labels = percent_format(accuracy = 1)) +
ylab("Ratio of Minimum Wage to Average (%)") + xlab("")
%>%
MIN2AVE filter(COUNTRY == "FRA") %>%
%>%
year_to_date left_join(MIN2AVE_var$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, 2025, 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("")
%>%
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, 2025, 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("")