Salaires mensuels nets moyens

Data - INSEE

Info

• En 2019, le salaire net moyen dans le secteur privé a progressé de 1,2 % en euros constants. pdf / xlsx / html

Figure 3 - histogramme

Tous les centiles

ip1863_fig3 %>%
  mutate(salaires_min = 1100 + 100*seq(0, 76, 1),
         salaires_min = ifelse(salaires_min == 1100, 0, salaires_min),
         salaires_max = 1200 + 100*seq(0, 76, 1),
         salaires_max = ifelse(salaires_max == 8800, Inf, salaires_max)) %>%
  mutate(effectifs_cum = 1 - cumsum(effectifs)/sum(effectifs)) %>%
  ggplot(.) + theme_minimal() + xlab("Wage") + ylab("Countercumulative Distribution Function") + 
  geom_point(aes(x = salaires_max, y = effectifs_cum)) +
  scale_x_log10(breaks = c(1000, 1500, 2000, 3000, 4000, 6000, 8000),
                labels = dollar_format(prefix = "", accuracy = 1, suffix = " €")) + 
  scale_y_log10(breaks = c(seq(0.01, 0.05, 0.01), 0.1, 0.2, 0.4, 1))

80 au 99ème centile

Regression

fit1 <- ip1863_fig3 %>%
  mutate(salaires_min = 1100 + 100*seq(0, 76, 1),
         salaires_min = ifelse(salaires_min == 1100, 0, salaires_min),
         salaires_max = 1200 + 100*seq(0, 76, 1),
         salaires_max = ifelse(salaires_max == 8800, Inf, salaires_max)) %>%
  mutate(effectifs_cum = 1 - cumsum(effectifs)/sum(effectifs)) %>%
  filter(effectifs_cum <= 0.2) %>%
  filter(is.finite(salaires_max)) %>%
  lm(log(effectifs_cum) ~ log(salaires_max), data = .)

summ(fit1)

Observations	58
Dependent variable	log(effectifs_cum)
Type	OLS linear regression

F(1,56)	1238345.44
R²	1.00
Adj. R²	1.00

	Est.	S.E.	t val.	p
(Intercept)	19.75	0.02	952.40	0.00
log(salaires_max)	-2.67	0.00	-1112.81	0.00
Standard errors: OLS

Graph

ip1863_fig3 %>%
  mutate(salaires_min = 1100 + 100*seq(0, 76, 1),
         salaires_min = ifelse(salaires_min == 1100, 0, salaires_min),
         salaires_max = 1200 + 100*seq(0, 76, 1),
         salaires_max = ifelse(salaires_max == 8800, Inf, salaires_max)) %>%
  mutate(effectifs_cum = 1 - cumsum(effectifs)/sum(effectifs)) %>%
  filter(effectifs_cum <= 0.2) %>%
  ggplot(.) + theme_minimal() + xlab("Wage") + ylab("Countercumulative Distribution Function") + 
  geom_point(aes(x = salaires_max, y = effectifs_cum)) +
  scale_x_log10(breaks = c(1000, 1500, 2000, 3000, 4000, 5000, 6000, 7000, 8000),
                labels = dollar_format(prefix = "", accuracy = 1, suffix = " €")) + 
  scale_y_log10(breaks = c(seq(0.01, 0.05, 0.01), 0.1, 0.2, 0.4, 1)) +
  geom_function(aes(colour = paste0("Pente (coeff Pareto): ", round(fit1$coefficients[2],3))), fun = function(x) exp(fit1$coefficients[1] + fit1$coefficients[2]*log(x))) +
  theme(legend.position = c(0.8, 0.9),
        legend.title = element_blank())

90 au 99ème centile

Regression

fit1 <- ip1863_fig3 %>%
  mutate(salaires_min = 1100 + 100*seq(0, 76, 1),
         salaires_min = ifelse(salaires_min == 1100, 0, salaires_min),
         salaires_max = 1200 + 100*seq(0, 76, 1),
         salaires_max = ifelse(salaires_max == 8800, Inf, salaires_max)) %>%
  mutate(effectifs_cum = 1 - cumsum(effectifs)/sum(effectifs)) %>%
  filter(effectifs_cum <= 0.1) %>%
  filter(is.finite(salaires_max)) %>%
  lm(log(effectifs_cum) ~ log(salaires_max), data = .)

summ(fit1)

Observations	49
Dependent variable	log(effectifs_cum)
Type	OLS linear regression

F(1,47)	3330251.79
R²	1.00
Adj. R²	1.00

	Est.	S.E.	t val.	p
(Intercept)	19.86	0.01	1547.21	0.00
log(salaires_max)	-2.69	0.00	-1824.90	0.00
Standard errors: OLS

Graph

ip1863_fig3 %>%
  mutate(salaires_min = 1100 + 100*seq(0, 76, 1),
         salaires_min = ifelse(salaires_min == 1100, 0, salaires_min),
         salaires_max = 1200 + 100*seq(0, 76, 1),
         salaires_max = ifelse(salaires_max == 8800, Inf, salaires_max)) %>%
  mutate(effectifs_cum = 1 - cumsum(effectifs)/sum(effectifs)) %>%
  filter(effectifs_cum <= 0.1) %>%
  ggplot(.) + theme_minimal() + xlab("Wage") + ylab("Countercumulative Distribution Function") + 
  geom_point(aes(x = salaires_max, y = effectifs_cum)) +
  scale_x_log10(breaks = c(1000, 1500, 2000, 3000, 4000, 5000, 6000, 7000, 8000),
                labels = dollar_format(prefix = "", accuracy = 1, suffix = " €")) + 
  scale_y_log10(breaks = seq(0.01, 0.1, 0.01)) +
  geom_function(aes(colour = paste0("Pente (coeff Pareto): ", round(fit1$coefficients[2],3))), fun = function(x) exp(fit1$coefficients[1] + fit1$coefficients[2]*log(x))) +
  theme(legend.position = c(0.8, 0.9),
        legend.title = element_blank())

Salaires distribution - ip1863

Tous les centiles

ip1863 %>%
  ggplot(.) + theme_minimal() + xlab("Wage") + ylab("Countercumulative Distribution Function") + 
  geom_point(aes(x = salaire, y = countercumulative)) +
  scale_x_log10(breaks = c(1000, 1500, 2000, 3000, 4000, 6000, 8000),
                labels = dollar_format(prefix = "", accuracy = 1, suffix = " €")) + 
  scale_y_log10(breaks = c(seq(0.01, 0.05, 0.01), 0.1, 0.2, 0.4, 1))

80 au 99ème centile

Regression

fit1 <- ip1863 %>%
  filter(countercumulative <= 0.2) %>%
  lm(log(countercumulative) ~ log(salaire), data = .)

summ(fit1)

Observations	20
Dependent variable	log(countercumulative)
Type	OLS linear regression

F(1,18)	158231.39
R²	1.00
Adj. R²	1.00

	Est.	S.E.	t val.	p
(Intercept)	19.58	0.06	352.69	0.00
log(salaire)	-2.65	0.01	-397.78	0.00
Standard errors: OLS

Graph

ip1863 %>%
  filter(countercumulative <= 0.2) %>%
  ggplot(.) + theme_minimal() + xlab("Wage") + ylab("Countercumulative Distribution Function") + 
  geom_point(aes(x = salaire, y = countercumulative)) +
  scale_x_log10(breaks = c(1000, 1500, 2000, 3000, 4000, 6000, 8000),
                labels = dollar_format(prefix = "", accuracy = 1, suffix = " €")) + 
  scale_y_log10(breaks = c(seq(0.01, 0.05, 0.01), 0.1, 0.2, 0.4, 1)) +
  geom_function(aes(colour = paste0("Pente (coeff Pareto): ", round(fit1$coefficients[2],3))), fun = function(x) exp(fit1$coefficients[1] + fit1$coefficients[2]*log(x))) +
  theme(legend.position = c(0.8, 0.9),
        legend.title = element_blank())

Evolution des salaires: 1996-2019

Tous

ip1863_fig5 %>%
  year_to_date2() %>%
  group_by(variable) %>%
  mutate(value = 100*value/value[date == as.Date("1996-01-01")]) %>%
  ggplot() + ylab("Salaire net en EQTP depuis 1996, en € constants") + xlab("") + theme_minimal() +
  geom_line(aes(x = date, y = value, color = variable)) +
  
  scale_x_date(breaks = seq(1920, 2025, 2) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  theme(legend.position = c(0.15, 0.85),
        legend.title = element_blank()) +
  scale_y_log10(breaks = seq(10, 300, 2),
                     labels = dollar_format(accuracy = 1, prefix = ""))

2008-

ip1863_fig5 %>%
  year_to_date2() %>%
  filter(date >= as.Date("2008-01-01")) %>%
  group_by(variable) %>%
  mutate(value = 100*value/value[date == as.Date("2008-01-01")]) %>%
  ggplot() + ylab("Salaire net en EQTP depuis 2008, en € constants") + xlab("") + theme_minimal() +
  geom_line(aes(x = date, y = value, color = variable)) +
  
  scale_x_date(breaks = seq(1920, 2025, 1) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  theme(legend.position = c(0.15, 0.85),
        legend.title = element_blank()) +
  scale_y_log10(breaks = seq(10, 300, 1),
                     labels = dollar_format(accuracy = 1, prefix = ""))