Les indices de salaire de base - les-indices-de-salaire-de-base

Données - Dares

Last observation: 2026T1 (N = 793)

First observation: 2017T2 (N = 792)

Last data update: 20 sept. 2026, 19:20

Last compile: 23 sept. 2026, 21:39

sheets

`les-indices-de-salaire-de-base` |>
  group_by(sheets) |>
  summarise(Nobs = n()) |>
  arrange(-Nobs) |>
  print_table_conditional()

sheets	Nobs
Sal. mens. ensemble	4549
Sal. mens. PI	4235
Sal. mens. emp.	4060
Sal. mens. cadres	3955
Sal. mens. ouv.	3675
Sal. horaire ouv.	3640
Sal. horaire ouv. emp.	3640

secteur

`les-indices-de-salaire-de-base` |>
  group_by(secteur, Secteur) |>
  summarise(Nobs = n()) |>
  arrange(-Nobs) |>
  print_table_conditional()

TIME_PERIOD

`les-indices-de-salaire-de-base` |>
  group_by(TIME_PERIOD) |>
  summarise(Nobs = n()) |>
  arrange(desc(TIME_PERIOD)) |>
  print_table_conditional()

Par secteur

Métallurgie

Tous

`les-indices-de-salaire-de-base` |>
  filter(secteur == "24") |>
  mutate(date = zoo::as.yearqtr(TIME_PERIOD, format = "%YT%q")) |>
  ggplot() + geom_line(aes(x = date, y = value, color = sheets)) + 
  theme_minimal() + xlab("") + ylab("Croissance vs. 2017T2") +
  zoo::scale_x_yearqtr(labels = date_format("%YT%q"), n = 24) +
  scale_y_log10(breaks = seq(100, 200, 2),
                labels = percent(seq(100, 200, 2)/100-1, acc = 1)) +
  theme(legend.position = c(0.2, 0.7),
        legend.title = element_blank(),
        axis.text.x = element_text(angle = 45, vjust = 1, hjust = 1))

2019T4-

`les-indices-de-salaire-de-base` |>
  filter(secteur == "24") |>
  mutate(date = zoo::as.yearqtr(TIME_PERIOD, format = "%YT%q")) |>
  filter(date >=  zoo::as.yearqtr("2019 Q4")) |>
  group_by(sheets) |>
  mutate(value = 100*value/value[1]) |>
  ggplot() + geom_line(aes(x = date, y = value, color = sheets)) + 
  theme_minimal() + xlab("") + ylab("") +
  zoo::scale_x_yearqtr(labels = date_format("%YT%q"), n = 24) +
  scale_y_log10(breaks = seq(100, 200, 1)) +
  theme(legend.position = c(0.2, 0.7),
        legend.title = element_blank(),
        axis.text.x = element_text(angle = 45, vjust = 1, hjust = 1))

2021T3-

`les-indices-de-salaire-de-base` |>
  filter(secteur == "24") |>
  mutate(date = zoo::as.yearqtr(TIME_PERIOD, format = "%YT%q")) |>
  filter(date >=  zoo::as.yearqtr("2021 Q3")) |>
  group_by(sheets) |>
  mutate(value = 100*value/value[1]) |>
  ggplot() + geom_line(aes(x = date, y = value, color = sheets)) + 
  theme_minimal() + xlab("") + ylab("Metallurgie") +
  zoo::scale_x_yearqtr(labels = date_format("%YT%q"),
                       breaks = expand.grid(2017:2100, c(1, 3)) |>
                         mutate(breaks = zoo::as.yearqtr(paste0(Var1, "Q", Var2))) |>
                         pull(breaks)) +
  scale_y_log10(breaks = seq(100, 200, 1)) +
  theme(legend.position = c(0.2, 0.7),
        legend.title = element_blank(),
        axis.text.x = element_text(angle = 45, vjust = 1, hjust = 1))

Salaires, Ensemble, Ensemble

2014T4-2024T4

Salaires

data1 <- `les-indices-de-salaire-de-base` |>
  filter(secteur == "ENS",
         sheets == "Sal. mens. ensemble") |>
  select(TIME_PERIOD, value) |>
  
  mutate(variable = "Salaire mensuel du secteur privé") |>
  mutate(date = zoo::as.yearqtr(TIME_PERIOD, format = "%YT%q")) |>
  filter(date >=  zoo::as.yearqtr("2014 Q4"),
         date <=  zoo::as.yearqtr("2024 Q4")) |>
  mutate(value = 100*value/value[1]) |>
  ungroup()
data1 |>
  ggplot() + geom_line(aes(x = date, y = value, color = variable)) + 
  theme_minimal() + xlab("") + ylab("") +
  zoo::scale_x_yearqtr(labels = date_format("%YT%q"),
                       breaks = expand.grid(2017:2100, c(2, 4)) |>
                         mutate(breaks = zoo::as.yearqtr(paste0(Var1, "Q", Var2))) |>
                         pull(breaks)) +
  scale_y_log10(breaks = seq(100, 200, 1)) +
  theme(legend.position = c(0.2, 0.7),
        legend.title = element_blank(),
        axis.text.x = element_text(angle = 45, vjust = 1, hjust = 1)) +
  geom_text(data = . %>% 
              filter(quarter(as.Date(date)) %in% c(2, 4)),
            aes(x = date, y = value, label = round(value, 1)))

Prix

`IPCH-IPC-2025-ensemble` <- read_parquet(here::here("data", "insee", "IPCH-IPC-2025-ensemble.parquet"))
data2 <- `IPCH-IPC-2025-ensemble` |>
  filter(date >= as.Date("2014-12-01"),
         month(date) %in% c(3, 6, 9, 12),
         date <= as.Date("2024-12-01")) |>
  group_by(INDICATEUR) |>
  mutate(value = 100*OBS_VALUE/OBS_VALUE[1]) |>
  transmute(TIME_PERIOD = paste0(year(date), "T", month(date)/3),
            value = 100*OBS_VALUE/OBS_VALUE[1],
            VARIABLE = INDICATEUR) |>
  mutate(variable = ifelse(VARIABLE == "IPC", "Prix Insee (IPC)", "Prix Eurostat (IPCH)")) |>
  mutate(date = zoo::as.yearqtr(TIME_PERIOD, format = "%YT%q")) |>
  ungroup()
data2 |>
  ggplot() + geom_line(aes(x = date, y = value, color = variable)) + 
  theme_minimal() + xlab("") + ylab("") +
  zoo::scale_x_yearqtr(labels = date_format("%YT%q"),
                       breaks = expand.grid(2017:2100, c(2, 4)) |>
                         mutate(breaks = zoo::as.yearqtr(paste0(Var1, "Q", Var2))) |>
                         pull(breaks)) +
  scale_y_log10(breaks = seq(100, 200, 1)) +
  theme(legend.position = c(0.3, 0.9),
        legend.title = element_blank(),
        axis.text.x = element_text(angle = 45, vjust = 1, hjust = 1)) +
  geom_text(data = . %>% 
              filter(quarter(as.Date(date)) %in% c(2, 4)),
            aes(x = date, y = value, label = round(value, 1)))

Bind

data2 |>
  bind_rows(data1) |>
  ggplot() + geom_line(aes(x = date, y = value, color = variable)) + 
  theme_minimal() + xlab("") + ylab("") +
  zoo::scale_x_yearqtr(labels = date_format("%YT%q"),
                       breaks = expand.grid(2017:2100, c(2, 4)) |>
                         mutate(breaks = zoo::as.yearqtr(paste0(Var1, "Q", Var2))) |>
                         pull(breaks)) +
  scale_y_log10(breaks = seq(100, 200, 2)) +
  theme(legend.position = c(0.3, 0.9),
        legend.title = element_blank(),
        axis.text.x = element_text(angle = 45, vjust = 1, hjust = 1)) +
  geom_text(data = . %>%
              filter(date %in% zoo::as.yearqtr(c("2017 Q2", "2020 Q4", "2022 Q4", "2024 Q2"))),
            aes(x = date, y = value, label = round(value, 1)))

2017T2-

Salaires

data1 <- `les-indices-de-salaire-de-base` |>
  filter(secteur == "ENS",
         sheets == "Sal. mens. ensemble") |>
  select(TIME_PERIOD, value) |>
  
  mutate(variable = "Salaire mensuel du secteur privé") |>
  mutate(date = zoo::as.yearqtr(TIME_PERIOD, format = "%YT%q")) |>
  filter(date >=  zoo::as.yearqtr("2017 Q2")) |>
  mutate(value = 100*value/value[1]) |>
  ungroup()
data1 |>
  ggplot() + geom_line(aes(x = date, y = value, color = variable)) + 
  theme_minimal() + xlab("") + ylab("") +
  zoo::scale_x_yearqtr(labels = date_format("%YT%q"),
                       breaks = expand.grid(2017:2100, c(2, 4)) |>
                         mutate(breaks = zoo::as.yearqtr(paste0(Var1, "Q", Var2))) |>
                         pull(breaks)) +
  scale_y_log10(breaks = seq(100, 200, 1)) +
  theme(legend.position = c(0.2, 0.7),
        legend.title = element_blank(),
        axis.text.x = element_text(angle = 45, vjust = 1, hjust = 1)) +
  geom_text(data = . %>% 
              filter(quarter(as.Date(date)) %in% c(2, 4)),
            aes(x = date, y = value, label = round(value, 1)))

Prix

`IPCH-IPC-2025-ensemble` <- read_parquet(here::here("data", "insee", "IPCH-IPC-2025-ensemble.parquet"))
data2 <- `IPCH-IPC-2025-ensemble` |>
  filter(date >= as.Date("2017-06-01"),
         month(date) %in% c(3, 6, 9, 12)) |>
  group_by(INDICATEUR) |>
  mutate(value = 100*OBS_VALUE/OBS_VALUE[1]) |>
  transmute(TIME_PERIOD = paste0(year(date), "T", month(date)/3),
            value = 100*OBS_VALUE/OBS_VALUE[1],
            VARIABLE = INDICATEUR) |>
  mutate(variable = ifelse(VARIABLE == "IPC", "Prix Insee (IPC)", "Prix Eurostat (IPCH)")) |>
  mutate(date = zoo::as.yearqtr(TIME_PERIOD, format = "%YT%q")) |>
  ungroup()
data2 |>
  ggplot() + geom_line(aes(x = date, y = value, color = variable)) + 
  theme_minimal() + xlab("") + ylab("") +
  zoo::scale_x_yearqtr(labels = date_format("%YT%q"),
                       breaks = expand.grid(2017:2100, c(2, 4)) |>
                         mutate(breaks = zoo::as.yearqtr(paste0(Var1, "Q", Var2))) |>
                         pull(breaks)) +
  scale_y_log10(breaks = seq(100, 200, 1)) +
  theme(legend.position = c(0.3, 0.9),
        legend.title = element_blank(),
        axis.text.x = element_text(angle = 45, vjust = 1, hjust = 1)) +
  geom_text(data = . %>% 
              filter(quarter(as.Date(date)) %in% c(2, 4)),
            aes(x = date, y = value, label = round(value, 1)))

Bind

data2 |>
  bind_rows(data1) |>
  #filter(variable != "Prix Insee (IPC)") %>%
  ggplot() + geom_line(aes(x = date, y = value, color = variable)) + 
  theme_minimal() + xlab("") + ylab("") +
  zoo::scale_x_yearqtr(labels = date_format("%YT%q"),
                       breaks = expand.grid(2017:2100, c(2, 4)) |>
                         mutate(breaks = zoo::as.yearqtr(paste0(Var1, "Q", Var2))) |>
                         pull(breaks)) +
  scale_y_log10(breaks = seq(100, 200, 2)) +
  theme(legend.position = c(0.3, 0.9),
        legend.title = element_blank(),
        axis.text.x = element_text(angle = 45, vjust = 1, hjust = 1)) +
  geom_text_repel(data = . %>%
              filter(date %in% zoo::as.yearqtr(c(max(date), "2017 Q2", "2020 Q4", "2022 Q4", "2024 Q3"))),
            aes(x = date, y = value, label = round(value, 1), color = variable))

2017T2-2024T2

Salaires

data1 <- `les-indices-de-salaire-de-base` |>
  filter(secteur == "ENS",
         sheets == "Sal. mens. ensemble") |>
  select(TIME_PERIOD, value) |>
  
  mutate(variable = "Salaire mensuel du secteur privé") |>
  mutate(date = zoo::as.yearqtr(TIME_PERIOD, format = "%YT%q")) |>
  filter(date >=  zoo::as.yearqtr("2017 Q2"),
         date <=  zoo::as.yearqtr("2024 Q2")) |>
  mutate(value = 100*value/value[1]) |>
  ungroup()
data1 |>
  ggplot() + geom_line(aes(x = date, y = value, color = variable)) + 
  theme_minimal() + xlab("") + ylab("") +
  zoo::scale_x_yearqtr(labels = date_format("%YT%q"),
                       breaks = expand.grid(2017:2100, c(2, 4)) |>
                         mutate(breaks = zoo::as.yearqtr(paste0(Var1, "Q", Var2))) |>
                         pull(breaks)) +
  scale_y_log10(breaks = seq(100, 200, 1)) +
  theme(legend.position = c(0.2, 0.7),
        legend.title = element_blank(),
        axis.text.x = element_text(angle = 45, vjust = 1, hjust = 1)) +
  geom_text(data = . %>% 
              filter(quarter(as.Date(date)) %in% c(2, 4)),
            aes(x = date, y = value, label = round(value, 1)))

Prix

`IPCH-IPC-2025-ensemble` <- read_parquet(here::here("data", "insee", "IPCH-IPC-2025-ensemble.parquet"))
data2 <- `IPCH-IPC-2025-ensemble` |>
  filter(date >= as.Date("2017-06-01"),
         month(date) %in% c(3, 6, 9, 12),
         date <= as.Date("2024-06-01")) |>
  group_by(INDICATEUR) |>
  mutate(value = 100*OBS_VALUE/OBS_VALUE[1]) |>
  transmute(TIME_PERIOD = paste0(year(date), "T", month(date)/3),
            value = 100*OBS_VALUE/OBS_VALUE[1],
            VARIABLE = INDICATEUR) |>
  mutate(variable = ifelse(VARIABLE == "IPC", "Prix Insee (IPC)", "Prix Eurostat (IPCH)")) |>
  mutate(date = zoo::as.yearqtr(TIME_PERIOD, format = "%YT%q")) |>
  ungroup()
data2 |>
  ggplot() + geom_line(aes(x = date, y = value, color = variable)) + 
  theme_minimal() + xlab("") + ylab("") +
  zoo::scale_x_yearqtr(labels = date_format("%YT%q"),
                       breaks = expand.grid(2017:2100, c(2, 4)) |>
                         mutate(breaks = zoo::as.yearqtr(paste0(Var1, "Q", Var2))) |>
                         pull(breaks)) +
  scale_y_log10(breaks = seq(100, 200, 1)) +
  theme(legend.position = c(0.3, 0.9),
        legend.title = element_blank(),
        axis.text.x = element_text(angle = 45, vjust = 1, hjust = 1)) +
  geom_text(data = . %>% 
              filter(quarter(as.Date(date)) %in% c(2, 4)),
            aes(x = date, y = value, label = round(value, 1)))

Bind

data2 |>
  bind_rows(data1) |>
  ggplot() + geom_line(aes(x = date, y = value, color = variable)) + 
  theme_minimal() + xlab("") + ylab("") +
  zoo::scale_x_yearqtr(labels = date_format("%YT%q"),
                       breaks = expand.grid(2017:2100, c(2, 4)) |>
                         mutate(breaks = zoo::as.yearqtr(paste0(Var1, "Q", Var2))) |>
                         pull(breaks)) +
  scale_y_log10(breaks = seq(100, 200, 2)) +
  theme(legend.position = c(0.3, 0.9),
        legend.title = element_blank(),
        axis.text.x = element_text(angle = 45, vjust = 1, hjust = 1)) +
  geom_text(data = . %>%
              filter(date %in% zoo::as.yearqtr(c(max(date), "2017 Q2", "2020 Q4", "2022 Q4", "2024 Q2"))),
            aes(x = date, y = value, label = round(value, 1)))

2017T4-

Salaires

data1 <- `les-indices-de-salaire-de-base` |>
  filter(secteur == "ENS",
         sheets == "Sal. mens. ensemble") |>
  select(TIME_PERIOD, value) |>
  
  mutate(variable = "Salaire mensuel du secteur privé") |>
  mutate(date = zoo::as.yearqtr(TIME_PERIOD, format = "%YT%q")) |>
  filter(date >=  zoo::as.yearqtr("2017 Q4")) |>
  mutate(value = 100*value/value[1]) |>
  ungroup()
data1 |>
  ggplot() + geom_line(aes(x = date, y = value, color = variable)) + 
  theme_minimal() + xlab("") + ylab("") +
  zoo::scale_x_yearqtr(labels = date_format("%YT%q"),
                       breaks = expand.grid(2017:2100, c(2, 4)) |>
                         mutate(breaks = zoo::as.yearqtr(paste0(Var1, "Q", Var2))) |>
                         pull(breaks)) +
  scale_y_log10(breaks = seq(100, 200, 1)) +
  theme(legend.position = c(0.2, 0.7),
        legend.title = element_blank(),
        axis.text.x = element_text(angle = 45, vjust = 1, hjust = 1)) +
  geom_text(data = . %>% 
              filter(quarter(as.Date(date)) %in% c(2, 4)),
            aes(x = date, y = value, label = round(value, 1)))

Prix

`IPCH-IPC-2025-ensemble` <- read_parquet(here::here("data", "insee", "IPCH-IPC-2025-ensemble.parquet"))
data2 <- `IPCH-IPC-2025-ensemble` |>
  filter(date >= as.Date("2017-12-01"),
         month(date) %in% c(3, 6, 9, 12)) |>
  group_by(INDICATEUR) |>
  mutate(value = 100*OBS_VALUE/OBS_VALUE[1]) |>
  transmute(TIME_PERIOD = paste0(year(date), "T", month(date)/3),
            value = 100*OBS_VALUE/OBS_VALUE[1],
            VARIABLE = INDICATEUR) |>
  mutate(variable = ifelse(VARIABLE == "IPC", "Prix Insee (IPC)", "Prix Eurostat (IPCH)")) |>
  mutate(date = zoo::as.yearqtr(TIME_PERIOD, format = "%YT%q")) |>
  ungroup()
data2 |>
  ggplot() + geom_line(aes(x = date, y = value, color = variable)) + 
  theme_minimal() + xlab("") + ylab("") +
  zoo::scale_x_yearqtr(labels = date_format("%YT%q"),
                       breaks = expand.grid(2017:2100, c(2, 4)) |>
                         mutate(breaks = zoo::as.yearqtr(paste0(Var1, "Q", Var2))) |>
                         pull(breaks)) +
  scale_y_log10(breaks = seq(100, 200, 1)) +
  theme(legend.position = c(0.3, 0.9),
        legend.title = element_blank(),
        axis.text.x = element_text(angle = 45, vjust = 1, hjust = 1)) +
  geom_text(data = . %>% 
              filter(quarter(as.Date(date)) %in% c(2, 4)),
            aes(x = date, y = value, label = round(value, 1)))

Bind

data2 |>
  bind_rows(data1) |>
  ggplot() + geom_line(aes(x = date, y = value, color = variable)) + 
  theme_minimal() + xlab("") + ylab("") +
  zoo::scale_x_yearqtr(labels = date_format("%YT%q"),
                       breaks = expand.grid(2017:2100, c(2, 4)) |>
                         mutate(breaks = zoo::as.yearqtr(paste0(Var1, "Q", Var2))) |>
                         pull(breaks)) +
  scale_y_log10(breaks = seq(100, 200, 2)) +
  theme(legend.position = c(0.3, 0.9),
        legend.title = element_blank(),
        axis.text.x = element_text(angle = 45, vjust = 1, hjust = 1)) +
  geom_text(data = . %>%
              filter(date %in% zoo::as.yearqtr(c("2017 Q2", "2020 Q4", "2022 Q4", "2023 Q4"))),
            aes(x = date, y = value, label = round(value, 1)))

2019T4-

Salaires

data1 <- `les-indices-de-salaire-de-base` |>
  filter(secteur == "ENS",
         sheets == "Sal. mens. ensemble") |>
  select(TIME_PERIOD, value) |>
  
  mutate(variable = "Salaire mensuel du secteur privé") |>
  mutate(date = zoo::as.yearqtr(TIME_PERIOD, format = "%YT%q")) |>
  filter(date >=  zoo::as.yearqtr("2019 Q4")) |>
  mutate(value = 100*value/value[1])
data1 |>
  ggplot() + geom_line(aes(x = date, y = value, color = variable)) + 
  theme_minimal() + xlab("") + ylab("") +
  zoo::scale_x_yearqtr(labels = date_format("%YT%q"), n = 12) +
  scale_y_log10(breaks = seq(100, 200, 1)) +
  theme(legend.position = c(0.2, 0.7),
        legend.title = element_blank(),
        axis.text.x = element_text(angle = 45, vjust = 1, hjust = 1)) +
  geom_text(aes(x = date, y = value, label = round(value, 1)))

Prix

`IPCH-IPC-2025-ensemble` <- read_parquet(here::here("data", "insee", "IPCH-IPC-2025-ensemble.parquet"))

data2 <- `IPCH-IPC-2025-ensemble` |>
  filter(date >= as.Date("2019-12-01"),
         month(date) %in% c(3, 6, 9, 12)) |>
  group_by(INDICATEUR) |>
  mutate(value = 100*OBS_VALUE/OBS_VALUE[1]) |>
  transmute(TIME_PERIOD = paste0(year(date), "T", month(date)/3),
            value = 100*OBS_VALUE/OBS_VALUE[1],
            VARIABLE = INDICATEUR) |>
  mutate(variable = ifelse(VARIABLE == "IPC", "Prix Insee (IPC)", "Prix Eurostat (IPCH)")) |>
  mutate(date = zoo::as.yearqtr(TIME_PERIOD, format = "%YT%q"))

data2 |>
  ggplot() + geom_line(aes(x = date, y = value, color = variable)) + 
  theme_minimal() + xlab("") + ylab("") +
  zoo::scale_x_yearqtr(labels = date_format("%YT%q"), n = 12) +
  scale_y_log10(breaks = seq(100, 200, 1)) +
  theme(legend.position = c(0.3, 0.9),
        legend.title = element_blank(),
        axis.text.x = element_text(angle = 45, vjust = 1, hjust = 1)) +
  geom_text(aes(x = date, y = value, label = round(value, 1)))

Bind

data2 |>
  bind_rows(data1) |>
  ggplot() + geom_line(aes(x = date, y = value, color = variable)) + 
  theme_minimal() + xlab("") + ylab("") +
  zoo::scale_x_yearqtr(labels = date_format("%YT%q"), n = 24) +
  scale_y_log10(breaks = seq(100, 200, 1)) +
  theme(legend.position = c(0.3, 0.9),
        legend.title = element_blank(),
        axis.text.x = element_text(angle = 45, vjust = 1, hjust = 1)) +
  geom_text(data = . %>% filter(date == max(date)), aes(x = date, y = value, label = round(value, 1)))

2020T4-

Salaires

data1 <- `les-indices-de-salaire-de-base` |>
  filter(secteur == "ENS",
         sheets == "Sal. mens. ensemble") |>
  select(TIME_PERIOD, value) |>
  
  mutate(variable = "Salaire mensuel du secteur privé") |>
  mutate(date = zoo::as.yearqtr(TIME_PERIOD, format = "%YT%q")) |>
  filter(date >=  zoo::as.yearqtr("2020 Q4")) |>
  mutate(value = 100*value/value[1])
data1 |>
  ggplot() + geom_line(aes(x = date, y = value, color = variable)) + 
  theme_minimal() + xlab("") + ylab("") +
  zoo::scale_x_yearqtr(labels = date_format("%YT%q"), n = 12) +
  scale_y_log10(breaks = seq(100, 200, 1)) +
  theme(legend.position = c(0.2, 0.7),
        legend.title = element_blank(),
        axis.text.x = element_text(angle = 45, vjust = 1, hjust = 1)) +
  geom_text(aes(x = date, y = value, label = round(value, 1)))

Prix

`IPCH-IPC-2025-ensemble` <- read_parquet(here::here("data", "insee", "IPCH-IPC-2025-ensemble.parquet"))
data2 <- `IPCH-IPC-2025-ensemble` |>
  filter(date >= as.Date("2020-12-01"),
         month(date) %in% c(3, 6, 9, 12)) |>
  group_by(INDICATEUR) |>
  mutate(value = 100*OBS_VALUE/OBS_VALUE[1]) |>
  transmute(TIME_PERIOD = paste0(year(date), "T", month(date)/3),
            value = 100*OBS_VALUE/OBS_VALUE[1],
            VARIABLE = INDICATEUR) |>
  mutate(variable = ifelse(VARIABLE == "IPC", "Prix Insee (IPC)", "Prix Eurostat (IPCH)")) |>
  mutate(date = zoo::as.yearqtr(TIME_PERIOD, format = "%YT%q"))
data2 |>
  ggplot() + geom_line(aes(x = date, y = value, color = variable)) + 
  theme_minimal() + xlab("") + ylab("") +
  zoo::scale_x_yearqtr(labels = date_format("%YT%q"), n = 12) +
  scale_y_log10(breaks = seq(100, 200, 1)) +
  theme(legend.position = c(0.3, 0.9),
        legend.title = element_blank(),
        axis.text.x = element_text(angle = 45, vjust = 1, hjust = 1)) +
  geom_text(aes(x = date, y = value, label = round(value, 1)))

Bind

data_bind <- data2 |>
  bind_rows(data1) |>
  ungroup() |>
  select(date, value, variable) |>
  arrange(date)

# write_excel_csv(data_bind, file = "alter_eco_20230213.csv")

data_bind |>
  ggplot() + geom_line(aes(x = date, y = value, color = variable)) + 
  theme_minimal() + xlab("") + ylab("Salaire de base") +
  zoo::scale_x_yearqtr(labels = date_format("%YT%q"),
                       breaks = seq(min(data_bind$date), max(data_bind$date), by = 0.25)) +
  scale_y_log10(breaks = seq(100, 200, 1)) +
  theme(legend.position = c(0.3, 0.9),
        legend.title = element_blank(),
        axis.text.x = element_text(angle = 45, vjust = 1, hjust = 1)) +
  geom_label(data = . %>%
              filter(date == max(date)),
            aes(x = date, y = value, label = round(value, 1)))

2021T1-

Salaires

data1 <- `les-indices-de-salaire-de-base` |>
  filter(secteur == "ENS",
         sheets == "Sal. mens. ensemble") |>
  select(TIME_PERIOD, value) |>
  
  mutate(variable = "Salaire mensuel du secteur privé") |>
  mutate(date = zoo::as.yearqtr(TIME_PERIOD, format = "%YT%q")) |>
  filter(date >=  zoo::as.yearqtr("2021 Q1")) |>
  mutate(value = 100*value/value[1])
data1 |>
  ggplot() + geom_line(aes(x = date, y = value, color = variable)) + 
  theme_minimal() + xlab("") + ylab("") +
  zoo::scale_x_yearqtr(labels = date_format("%YT%q"), n = 12) +
  scale_y_log10(breaks = seq(100, 200, 1)) +
  theme(legend.position = c(0.2, 0.7),
        legend.title = element_blank(),
        axis.text.x = element_text(angle = 45, vjust = 1, hjust = 1)) +
  geom_text(aes(x = date, y = value, label = round(value, 1)))

Prix

`IPCH-IPC-2025-ensemble` <- read_parquet(here::here("data", "insee", "IPCH-IPC-2025-ensemble.parquet"))
data2 <- `IPCH-IPC-2025-ensemble` |>
  filter(date >= as.Date("2021-03-01"),
         month(date) %in% c(3, 6, 9, 12)) |>
  group_by(INDICATEUR) |>
  mutate(value = 100*OBS_VALUE/OBS_VALUE[1]) |>
  transmute(TIME_PERIOD = paste0(year(date), "T", month(date)/3),
            value = 100*OBS_VALUE/OBS_VALUE[1],
            VARIABLE = INDICATEUR) |>
  mutate(variable = ifelse(VARIABLE == "IPC", "Prix Insee (IPC)", "Prix Eurostat (IPCH)")) |>
  mutate(date = zoo::as.yearqtr(TIME_PERIOD, format = "%YT%q"))
data2 |>
  ggplot() + geom_line(aes(x = date, y = value, color = variable)) + 
  theme_minimal() + xlab("") + ylab("") +
  zoo::scale_x_yearqtr(labels = date_format("%YT%q"), n = 12) +
  scale_y_log10(breaks = seq(100, 200, 1)) +
  theme(legend.position = c(0.3, 0.9),
        legend.title = element_blank(),
        axis.text.x = element_text(angle = 45, vjust = 1, hjust = 1)) +
  geom_text(aes(x = date, y = value, label = round(value, 1)))

Bind

data_bind <- data2 |>
  bind_rows(data1) |>
  ungroup() |>
  select(date, value, variable) |>
  arrange(date)

# write_excel_csv(data_bind, file = "alter_eco_20230213.csv")

data_bind |>
  ggplot() + geom_line(aes(x = date, y = value, color = variable)) + 
  theme_minimal() + xlab("") + ylab("") +
  zoo::scale_x_yearqtr(labels = date_format("%YT%q"), n = 12) +
  scale_y_log10(breaks = seq(100, 200, 1)) +
  theme(legend.position = c(0.3, 0.9),
        legend.title = element_blank(),
        axis.text.x = element_text(angle = 45, vjust = 1, hjust = 1)) +
  geom_text(data = . %>% filter(date == max(date)), aes(x = date, y = value, label = round(value, 1)))

2021T2-

Salaires

data1 <- `les-indices-de-salaire-de-base` |>
  filter(secteur == "ENS",
         sheets == "Sal. mens. ensemble") |>
  select(TIME_PERIOD, value) |>
  
  mutate(variable = "Salaire mensuel du secteur privé") |>
  mutate(date = zoo::as.yearqtr(TIME_PERIOD, format = "%YT%q")) |>
  filter(date >=  zoo::as.yearqtr("2021 Q2")) |>
  mutate(value = 100*value/value[1])
data1 |>
  ggplot() + geom_line(aes(x = date, y = value, color = variable)) + 
  theme_minimal() + xlab("") + ylab("") +
  zoo::scale_x_yearqtr(labels = date_format("%YT%q"), n = 12) +
  scale_y_log10(breaks = seq(100, 200, 1)) +
  theme(legend.position = c(0.2, 0.7),
        legend.title = element_blank(),
        axis.text.x = element_text(angle = 45, vjust = 1, hjust = 1)) +
  geom_text(aes(x = date, y = value, label = round(value, 1)))

Prix

`IPCH-IPC-2025-ensemble` <- read_parquet(here::here("data", "insee", "IPCH-IPC-2025-ensemble.parquet"))
data2 <- `IPCH-IPC-2025-ensemble` |>
  filter(date >= as.Date("2021-06-01"),
         month(date) %in% c(3, 6, 9, 12)) |>
  group_by(INDICATEUR) |>
  mutate(value = 100*OBS_VALUE/OBS_VALUE[1]) |>
  transmute(TIME_PERIOD = paste0(year(date), "T", month(date)/3),
            value = 100*OBS_VALUE/OBS_VALUE[1],
            VARIABLE = INDICATEUR) |>
  mutate(variable = ifelse(VARIABLE == "IPC", "Prix Insee (IPC)", "Prix Eurostat (IPCH)")) |>
  mutate(date = zoo::as.yearqtr(TIME_PERIOD, format = "%YT%q"))
data2 |>
  ggplot() + geom_line(aes(x = date, y = value, color = variable)) + 
  theme_minimal() + xlab("") + ylab("") +
  zoo::scale_x_yearqtr(labels = date_format("%YT%q"), n = 12) +
  scale_y_log10(breaks = seq(100, 200, 1)) +
  theme(legend.position = c(0.3, 0.9),
        legend.title = element_blank(),
        axis.text.x = element_text(angle = 45, vjust = 1, hjust = 1)) +
  geom_text(aes(x = date, y = value, label = round(value, 1)))

Bind

data_bind <- data2 |>
  bind_rows(data1) |>
  ungroup() |>
  select(date, value, variable) |>
  arrange(date)

write_excel_csv(data_bind, file = "nathalie_silbert_20240923.csv")

data_bind |>
  ggplot() + geom_line(aes(x = date, y = value, color = variable)) + 
  theme_minimal() + xlab("") + ylab("") +
  zoo::scale_x_yearqtr(labels = date_format("%YT%q"), n = 12) +
  scale_y_log10(breaks = seq(100, 200, 1)) +
  theme(legend.position = c(0.3, 0.9),
        legend.title = element_blank(),
        axis.text.x = element_text(angle = 45, vjust = 1, hjust = 1)) +
  geom_text(data = . %>% filter(date == max(date)), aes(x = date, y = value, label = round(value, 1)))

2021T4-

Salaires

data1 <- `les-indices-de-salaire-de-base` |>
  filter(secteur == "ENS",
         sheets == "Sal. mens. ensemble") |>
  select(TIME_PERIOD, value) |>
  
  mutate(variable = "Salaire mensuel du secteur privé") |>
  mutate(date = zoo::as.yearqtr(TIME_PERIOD, format = "%YT%q")) |>
  filter(date >=  zoo::as.yearqtr("2021 Q4")) |>
  mutate(value = 100*value/value[1])


data1 |>
  ggplot() + geom_line(aes(x = date, y = value, color = variable)) + 
  theme_minimal() + xlab("") + ylab("") +
  zoo::scale_x_yearqtr(labels = date_format("%YT%q"), n = 12) +
  scale_y_log10(breaks = seq(100, 200, 1)) +
  theme(legend.position = c(0.2, 0.7),
        legend.title = element_blank(),
        axis.text.x = element_text(angle = 45, vjust = 1, hjust = 1)) +
  geom_text(aes(x = date, y = value, label = round(value, 1)))

Prix

`IPCH-IPC-2025-ensemble` <- read_parquet(here::here("data", "insee", "IPCH-IPC-2025-ensemble.parquet"))
data2 <- `IPCH-IPC-2025-ensemble` |>
  filter(date >= as.Date("2021-12-01"),
         month(date) %in% c(3, 6, 9, 12)) |>
  group_by(INDICATEUR) |>
  mutate(value = 100*OBS_VALUE/OBS_VALUE[1]) |>
  transmute(TIME_PERIOD = paste0(year(date), "T", month(date)/3),
            value = 100*OBS_VALUE/OBS_VALUE[1],
            VARIABLE = INDICATEUR) |>
  mutate(variable = ifelse(VARIABLE == "IPC", "Prix Insee (IPC)", "Prix Eurostat (IPCH)")) |>
  mutate(date = zoo::as.yearqtr(TIME_PERIOD, format = "%YT%q"))
data2 |>
  ggplot() + geom_line(aes(x = date, y = value, color = variable)) + 
  theme_minimal() + xlab("") + ylab("") +
  zoo::scale_x_yearqtr(labels = date_format("%YT%q"), n = 12) +
  scale_y_log10(breaks = seq(100, 200, 1)) +
  theme(legend.position = c(0.3, 0.9),
        legend.title = element_blank(),
        axis.text.x = element_text(angle = 45, vjust = 1, hjust = 1)) +
  geom_text(aes(x = date, y = value, label = round(value, 1)))

Bind

data_bind <- data2 |>
  bind_rows(data1) |>
  ungroup() |>
  select(date, value, variable) |>
  arrange(date)

# write_excel_csv(data_bind, file = "alter_eco_20230213.csv")

data_bind |>
  ggplot() + geom_line(aes(x = date, y = value, color = variable)) + 
  theme_minimal() + xlab("") + ylab("") +
  zoo::scale_x_yearqtr(labels = date_format("%YT%q"),
                       breaks = expand.grid(2021:2100, c(1, 2, 3, 4)) |>
                         mutate(breaks = zoo::as.yearqtr(paste0(Var1, "Q", Var2))) |>
                         pull(breaks)) +
  scale_y_log10(breaks = seq(100, 200, 1)) +
  theme(legend.position = c(0.3, 0.9),
        legend.title = element_blank(),
        axis.text.x = element_text(angle = 45, vjust = 1, hjust = 1)) +
  geom_text(data = . %>% filter(date == max(date)), aes(x = date, y = value, label = round(value, 1)))

2022T1-

Salaires

data1 <- `les-indices-de-salaire-de-base` |>
  filter(secteur == "ENS",
         sheets == "Sal. mens. ensemble") |>
  select(TIME_PERIOD, value) |>
  
  mutate(variable = "Salaire mensuel du secteur privé") |>
  mutate(date = zoo::as.yearqtr(TIME_PERIOD, format = "%YT%q")) |>
  filter(date >=  zoo::as.yearqtr("2022 Q1")) |>
  mutate(value = 100*value/value[1])


data1 |>
  ggplot() + geom_line(aes(x = date, y = value, color = variable)) + 
  theme_minimal() + xlab("") + ylab("") +
  zoo::scale_x_yearqtr(labels = date_format("%YT%q"), n = 12) +
  scale_y_log10(breaks = seq(100, 200, 1)) +
  theme(legend.position = c(0.2, 0.7),
        legend.title = element_blank(),
        axis.text.x = element_text(angle = 45, vjust = 1, hjust = 1)) +
  geom_text(aes(x = date, y = value, label = round(value, 1)))

Prix

`IPCH-IPC-2025-ensemble` <- read_parquet(here::here("data", "insee", "IPCH-IPC-2025-ensemble.parquet"))
data2 <- `IPCH-IPC-2025-ensemble` |>
  filter(date >= as.Date("2022-03-01"),
         month(date) %in% c(3, 6, 9, 12)) |>
  group_by(INDICATEUR) |>
  mutate(value = 100*OBS_VALUE/OBS_VALUE[1]) |>
  transmute(TIME_PERIOD = paste0(year(date), "T", month(date)/3),
            value = 100*OBS_VALUE/OBS_VALUE[1],
            VARIABLE = INDICATEUR) |>
  mutate(variable = ifelse(VARIABLE == "IPC", "Prix Insee (IPC)", "Prix Eurostat (IPCH)")) |>
  mutate(date = zoo::as.yearqtr(TIME_PERIOD, format = "%YT%q"))
data2 |>
  ggplot() + geom_line(aes(x = date, y = value, color = variable)) + 
  theme_minimal() + xlab("") + ylab("") +
  zoo::scale_x_yearqtr(labels = date_format("%YT%q"), n = 12) +
  scale_y_log10(breaks = seq(100, 200, 1)) +
  theme(legend.position = c(0.3, 0.9),
        legend.title = element_blank(),
        axis.text.x = element_text(angle = 45, vjust = 1, hjust = 1)) +
  geom_text(aes(x = date, y = value, label = round(value, 1)))

Bind

data_bind <- data2 |>
  bind_rows(data1) |>
  ungroup() |>
  select(date, value, variable) |>
  arrange(date)

# write_excel_csv(data_bind, file = "alter_eco_20230213.csv")

data_bind |>
  ggplot() + geom_line(aes(x = date, y = value, color = variable)) + 
  theme_minimal() + xlab("") + ylab("") +
  zoo::scale_x_yearqtr(labels = date_format("%YT%q"),
                       breaks = expand.grid(2021:2100, c(1, 2, 3, 4)) |>
                         mutate(breaks = zoo::as.yearqtr(paste0(Var1, "Q", Var2))) |>
                         pull(breaks)) +
  scale_y_log10(breaks = seq(100, 200, 1)) +
  theme(legend.position = c(0.3, 0.9),
        legend.title = element_blank(),
        axis.text.x = element_text(angle = 45, vjust = 1, hjust = 1)) +
  geom_text(data = . %>% filter(date == max(date)), aes(x = date, y = value, label = round(value, 1)))

Salaires, Ensemble

Tous

`les-indices-de-salaire-de-base` |>
  filter(secteur == "ENS") |>
  mutate(date = zoo::as.yearqtr(TIME_PERIOD, format = "%YT%q")) |>
  ggplot() + geom_line(aes(x = date, y = value, color = sheets)) + 
  theme_minimal() + xlab("") + ylab("") +
  zoo::scale_x_yearqtr(labels = date_format("%YT%q"), n = 24) +
  scale_y_log10(breaks = seq(100, 200, 2)) +
  theme(legend.position = c(0.2, 0.7),
        legend.title = element_blank(),
        axis.text.x = element_text(angle = 45, vjust = 1, hjust = 1))

2019T4-

`les-indices-de-salaire-de-base` |>
  filter(secteur == "ENS") |>
  mutate(date = zoo::as.yearqtr(TIME_PERIOD, format = "%YT%q")) |>
  filter(date >=  zoo::as.yearqtr("2019 Q4")) |>
  group_by(sheets) |>
  mutate(value = 100*value/value[1]) |>
  ggplot() + geom_line(aes(x = date, y = value, color = sheets)) + 
  theme_minimal() + xlab("") + ylab("") +
  zoo::scale_x_yearqtr(labels = date_format("%YT%q"), n = 24) +
  scale_y_log10(breaks = seq(100, 200, 1)) +
  theme(legend.position = c(0.2, 0.7),
        legend.title = element_blank(),
        axis.text.x = element_text(angle = 45, vjust = 1, hjust = 1))

2021T3-

`les-indices-de-salaire-de-base` |>
  filter(secteur == "ENS") |>
  mutate(date = zoo::as.yearqtr(TIME_PERIOD, format = "%YT%q")) |>
  filter(date >=  zoo::as.yearqtr("2021 Q3")) |>
  group_by(sheets) |>
  mutate(value = 100*value/value[1]) |>
  ggplot() + geom_line(aes(x = date, y = value, color = sheets)) + 
  theme_minimal() + xlab("") + ylab("") +
  zoo::scale_x_yearqtr(labels = date_format("%YT%q"), n = 12) +
  scale_y_log10(breaks = seq(100, 200, 1)) +
  theme(legend.position = c(0.2, 0.7),
        legend.title = element_blank(),
        axis.text.x = element_text(angle = 45, vjust = 1, hjust = 1))

2020T4-

`les-indices-de-salaire-de-base` |>
  filter(secteur == "ENS") |>
  mutate(date = zoo::as.yearqtr(TIME_PERIOD, format = "%YT%q")) |>
  filter(date >=  zoo::as.yearqtr("2020 Q4")) |>
  group_by(sheets) |>
  mutate(value = 100*value/value[1]) |>
  ggplot() + geom_line(aes(x = date, y = value, color = sheets)) + 
  theme_minimal() + xlab("") + ylab("") +
  zoo::scale_x_yearqtr(labels = date_format("%YT%q"), n = 12) +
  scale_y_log10(breaks = seq(100, 200, 1)) +
  theme(legend.position = c(0.2, 0.7),
        legend.title = element_blank(),
        axis.text.x = element_text(angle = 45, vjust = 1, hjust = 1))

Salaires des cadres

Tous

`les-indices-de-salaire-de-base` |>
  filter(secteur %in% c("ENS", "ET", "EU", "EV"),
         sheets == "Sal. mens. cadres") |>
  mutate(date = zoo::as.yearqtr(TIME_PERIOD, format = "%YT%q")) |>
  ggplot() + geom_line(aes(x = date, y = value, color = Secteur)) + 
  theme_minimal() + xlab("") + ylab("") +
  zoo::scale_x_yearqtr(labels = date_format("%YT%q"), n = 24) +
  scale_y_log10(breaks = seq(100, 200, 2)) +
  theme(legend.position = c(0.35, 0.8),
        legend.title = element_blank(),
        axis.text.x = element_text(angle = 45, vjust = 1, hjust = 1))

2017T1-

Salaires

data1 <- `les-indices-de-salaire-de-base` |>
  filter(secteur == "ENS",
         sheets == "Sal. mens. cadres") |>
  select(TIME_PERIOD, value) |>
  
  mutate(variable = "Salaire mensuel des cadres du secteur privé") |>
  mutate(date = zoo::as.yearqtr(TIME_PERIOD, format = "%YT%q")) |>
  filter(date >=  zoo::as.yearqtr("2017 Q1")) |>
  mutate(value = 100*value/value[1])
data1 |>
  ggplot() + geom_line(aes(x = date, y = value, color = variable)) + 
  theme_minimal() + xlab("") + ylab("") +
  zoo::scale_x_yearqtr(labels = date_format("%YT%q"), n = 12) +
  scale_y_log10(breaks = seq(100, 200, 1)) +
  theme(legend.position = c(0.2, 0.7),
        legend.title = element_blank(),
        axis.text.x = element_text(angle = 45, vjust = 1, hjust = 1)) +
  geom_text(aes(x = date, y = value, label = round(value, 1)))

Prix

`IPCH-IPC-2025-ensemble` <- read_parquet(here::here("data", "insee", "IPCH-IPC-2025-ensemble.parquet"))
data2 <- `IPCH-IPC-2025-ensemble` |>
  filter(date >= as.Date("2017-03-01"),
         month(date) %in% c(3, 6, 9, 12)) |>
  group_by(INDICATEUR) |>
  mutate(value = 100*OBS_VALUE/OBS_VALUE[1]) |>
  transmute(TIME_PERIOD = paste0(year(date), "T", month(date)/3),
            value = 100*OBS_VALUE/OBS_VALUE[1],
            VARIABLE = INDICATEUR) |>
  mutate(variable = ifelse(VARIABLE == "IPC", "Prix Insee (IPC)", "Prix Eurostat (IPCH)")) |>
  mutate(date = zoo::as.yearqtr(TIME_PERIOD, format = "%YT%q"))
data2 |>
  ggplot() + geom_line(aes(x = date, y = value, color = variable)) + 
  theme_minimal() + xlab("") + ylab("") +
  zoo::scale_x_yearqtr(labels = date_format("%YT%q"), n = 12) +
  scale_y_log10(breaks = seq(100, 200, 1)) +
  theme(legend.position = c(0.3, 0.9),
        legend.title = element_blank(),
        axis.text.x = element_text(angle = 45, vjust = 1, hjust = 1)) +
  geom_text(aes(x = date, y = value, label = round(value, 1)))

Bind

data_bind <- data2 |>
  bind_rows(data1) |>
  ungroup() |>
  select(date, value, variable) |>
  arrange(date)

# write_excel_csv(data_bind, file = "alter_eco_20230213.csv")

data_bind |>
  ggplot() + geom_line(aes(x = date, y = value, color = variable)) + 
  theme_minimal() + xlab("") + ylab("") +
  zoo::scale_x_yearqtr(labels = date_format("%YT%q"),
                       breaks = seq(min(data_bind$date), max(data_bind$date), by = 0.25)) +
  scale_y_log10(breaks = seq(100, 200, 1)) +
  theme(legend.position = c(0.3, 0.9),
        legend.title = element_blank(),
        axis.text.x = element_text(angle = 45, vjust = 1, hjust = 1)) +
  geom_label_repel(data = . %>%
              filter(date == max(date)),
            aes(x = date, y = value, label = round(value, 1), color = variable))

2019T4-

`les-indices-de-salaire-de-base` |>
  filter(secteur %in% c("ENS", "ET", "EU", "EV"),
         sheets == "Sal. mens. cadres") |>
  mutate(date = zoo::as.yearqtr(TIME_PERIOD, format = "%YT%q")) |>
  filter(date >=  zoo::as.yearqtr("2019 Q4")) |>
  group_by(Secteur) |>
  arrange(date) |>
  mutate(value = 100*value/value[1]) |>
  ggplot() + geom_line(aes(x = date, y = value, color = Secteur)) + 
  theme_minimal() + xlab("") + ylab("") +
  zoo::scale_x_yearqtr(labels = date_format("%YT%q"), n = 24) +
  scale_y_log10(breaks = seq(100, 200, 1)) +
  theme(legend.position = c(0.35, 0.8),
        legend.title = element_blank(),
        axis.text.x = element_text(angle = 45, vjust = 1, hjust = 1))

2020T4-

Salaires

data1 <- `les-indices-de-salaire-de-base` |>
  filter(secteur == "ENS",
         sheets == "Sal. mens. cadres") |>
  select(TIME_PERIOD, value) |>
  
  mutate(variable = "Salaire mensuel des cadres du secteur privé") |>
  mutate(date = zoo::as.yearqtr(TIME_PERIOD, format = "%YT%q")) |>
  filter(date >=  zoo::as.yearqtr("2020 Q4")) |>
  mutate(value = 100*value/value[1])
data1 |>
  ggplot() + geom_line(aes(x = date, y = value, color = variable)) + 
  theme_minimal() + xlab("") + ylab("") +
  zoo::scale_x_yearqtr(labels = date_format("%YT%q"), n = 12) +
  scale_y_log10(breaks = seq(100, 200, 1)) +
  theme(legend.position = c(0.2, 0.7),
        legend.title = element_blank(),
        axis.text.x = element_text(angle = 45, vjust = 1, hjust = 1)) +
  geom_text(aes(x = date, y = value, label = round(value, 1)))

Prix

`IPCH-IPC-2025-ensemble` <- read_parquet(here::here("data", "insee", "IPCH-IPC-2025-ensemble.parquet"))
data2 <- `IPCH-IPC-2025-ensemble` |>
  filter(date >= as.Date("2020-12-01"),
         month(date) %in% c(3, 6, 9, 12)) |>
  group_by(INDICATEUR) |>
  mutate(value = 100*OBS_VALUE/OBS_VALUE[1]) |>
  transmute(TIME_PERIOD = paste0(year(date), "T", month(date)/3),
            value = 100*OBS_VALUE/OBS_VALUE[1],
            VARIABLE = INDICATEUR) |>
  mutate(variable = ifelse(VARIABLE == "IPC", "Prix Insee (IPC)", "Prix Eurostat (IPCH)")) |>
  mutate(date = zoo::as.yearqtr(TIME_PERIOD, format = "%YT%q"))
data2 |>
  ggplot() + geom_line(aes(x = date, y = value, color = variable)) + 
  theme_minimal() + xlab("") + ylab("") +
  zoo::scale_x_yearqtr(labels = date_format("%YT%q"), n = 12) +
  scale_y_log10(breaks = seq(100, 200, 1)) +
  theme(legend.position = c(0.3, 0.9),
        legend.title = element_blank(),
        axis.text.x = element_text(angle = 45, vjust = 1, hjust = 1)) +
  geom_text(aes(x = date, y = value, label = round(value, 1)))

Bind

data_bind <- data2 |>
  bind_rows(data1) |>
  ungroup() |>
  select(date, value, variable) |>
  arrange(date)

# write_excel_csv(data_bind, file = "alter_eco_20230213.csv")

data_bind |>
  ggplot() + geom_line(aes(x = date, y = value, color = variable)) + 
  theme_minimal() + xlab("") + ylab("") +
  zoo::scale_x_yearqtr(labels = date_format("%YT%q"),
                       breaks = seq(min(data_bind$date), max(data_bind$date), by = 0.25)) +
  scale_y_log10(breaks = seq(100, 200, 1)) +
  theme(legend.position = c(0.3, 0.9),
        legend.title = element_blank(),
        axis.text.x = element_text(angle = 45, vjust = 1, hjust = 1)) +
  geom_label(data = . %>%
              filter(date == max(date)),
            aes(x = date, y = value, label = round(value, 1)))

2021T3-

Secteurs

`les-indices-de-salaire-de-base` |>
  filter(secteur %in% c("ENS", "ET", "EU", "EV"),
         sheets == "Sal. mens. cadres") |>
  mutate(date = zoo::as.yearqtr(TIME_PERIOD, format = "%YT%q")) |>
  filter(date >=  zoo::as.yearqtr("2021 Q3")) |>
  group_by(Secteur) |>
  arrange(date) |>
  mutate(value = 100*value/value[1]) |>
  ggplot() + geom_line(aes(x = date, y = value, color = Secteur)) + 
  theme_minimal() + xlab("") + ylab("") +
  zoo::scale_x_yearqtr(labels = date_format("%YT%q"), n = 12) +
  scale_y_log10(breaks = seq(100, 200, 1)) +
  theme(legend.position = c(0.35, 0.8),
        legend.title = element_blank(),
        axis.text.x = element_text(angle = 45, vjust = 1, hjust = 1))

Salaires des ouvriers

Tous

`les-indices-de-salaire-de-base` |>
  filter(secteur %in% c("ENS", "ET", "EU", "EV"),
         sheets == "Sal. mens. ouv.") |>
  mutate(date = zoo::as.yearqtr(TIME_PERIOD, format = "%YT%q")) |>
  ggplot() + geom_line(aes(x = date, y = value, color = Secteur)) + 
  theme_minimal() + xlab("") + ylab("") +
  zoo::scale_x_yearqtr(labels = date_format("%YT%q"), n = 24) +
  scale_y_log10(breaks = seq(100, 200, 2)) +
  theme(legend.position = c(0.35, 0.8),
        legend.title = element_blank(),
        axis.text.x = element_text(angle = 45, vjust = 1, hjust = 1))

2019T4-

`les-indices-de-salaire-de-base` |>
  filter(secteur %in% c("ENS", "ET", "EU", "EV"),
         sheets == "Sal. mens. ouv.") |>
  mutate(date = zoo::as.yearqtr(TIME_PERIOD, format = "%YT%q")) |>
  filter(date >=  zoo::as.yearqtr("2019 Q4")) |>
  group_by(Secteur) |>
  arrange(date) |>
  mutate(value = 100*value/value[1]) |>
  ggplot() + geom_line(aes(x = date, y = value, color = Secteur)) + 
  theme_minimal() + xlab("") + ylab("") +
  zoo::scale_x_yearqtr(labels = date_format("%YT%q"), n = 24) +
  scale_y_log10(breaks = seq(100, 200, 1)) +
  theme(legend.position = c(0.35, 0.8),
        legend.title = element_blank(),
        axis.text.x = element_text(angle = 45, vjust = 1, hjust = 1))

2021T3-

`les-indices-de-salaire-de-base` |>
  filter(secteur %in% c("ENS", "ET", "EU", "EV"),
         sheets == "Sal. mens. ouv.") |>
  mutate(date = zoo::as.yearqtr(TIME_PERIOD, format = "%YT%q")) |>
  filter(date >=  zoo::as.yearqtr("2021 Q3")) |>
  group_by(Secteur) |>
  arrange(date) |>
  mutate(value = 100*value/value[1]) |>
  ggplot() + geom_line(aes(x = date, y = value, color = Secteur)) + 
  theme_minimal() + xlab("") + ylab("") +
  zoo::scale_x_yearqtr(labels = date_format("%YT%q"), n = 12) +
  scale_y_log10(breaks = seq(100, 200, 1)) +
  theme(legend.position = c(0.35, 0.8),
        legend.title = element_blank(),
        axis.text.x = element_text(angle = 45, vjust = 1, hjust = 1))

Related Data

Évolution du pouvoir d’achat depuis 2017

source	dataset	Title	Updated
dares	les-indices-de-salaire-de-base	Les indices de salaire de base - les-indices-de-salaire-de-base	2026-09-20
eurostat	nama_10_pc	Main GDP aggregates per capita - nama_10_pc	2026-09-20
eurostat	nasq_10_nf_tr	Non-financial transactions - nasq_10_nf_tr	2026-09-09
insee	CNA-2020-CONSO-MEN	Consommation des ménages - CNA-2020-CONSO-MEN	2026-09-04
insee	INDICES_LOYERS	Indices des loyers d’habitation (ILH) - INDICES_LOYERS	2026-09-20
insee	IPEA-2021	Indice des prix d’entretien-amélioration des bâtiments - Base 2021 - IPEA-2021	2026-09-20
insee	IRL	Indice pour la révision d’un loyer d’habitation - IRL	2026-09-20
insee	SALAIRES-ANNUELS	Salaires annuels - SALAIRES-ANNUELS	2026-09-20
insee	SMIC-COTISATIONS	Taux de cotisations et salaires minima garantis - SMIC-COTISATIONS	2026-09-20
insee	T_2101	2.101 – Revenu disponible brut des ménages et évolution du pouvoir d’achat par personne, par ménage et par unité de consommation (En milliards euros et %) - t_2101	2026-09-22
insee	T_2103	2.103 – Dépense de consommation finale pré-engagée des ménages à prix courants (En milliards d’euros et %) - t_2103	2026-09-22
insee	T_7401	7.401 – Compte des ménages (S14) (En milliards d’euros) - T_7401	2026-09-08
insee	econ-gen-revenu-dispo-pouv-achat-2	Revenu disponible brut et pouvoir d’achat - Données annuelles - econ-gen-revenu-dispo-pouv-achat-2	2026-08-02
insee	t_men_val	Revenu, pouvoir d’achat et comptes des ménages - Valeurs aux prix courants - t_men_val	2026-09-03
insee	t_pib_vol	PIB et ses composantes - Equilibre emplois-ressources - volumes aux prix de l’année précédente chaînés - t_pib_vol	2026-09-03
insee	t_pouvachat_val	Pouvoir d’achat et ratios des comptes des ménages - t_pouvachat_val	2026-09-03
insee	t_recapAgent_val	Récapitulatif des séries des comptes d’agents - t_recapAgent_val	2026-09-03
insee	t_salaire_val	Salaire moyen par tête - SMPT (données CVS) - t_salaire_val	2026-09-03
ipp	indicefp	Point d’indice de la fonction publique - indicefp	2026-09-20
ipp	revalorisation_pension	Revalorisation du Régime général de la caisse nationale d’assurance vieillesse (CNAV) - revalorisation_pension	2026-09-20
oecd	PRICES_ALL	Consumer price indices (CPIs) - PRICES_ALL	2026-09-18

Analyse des Données de Salaires

source	dataset	Title	Updated
dares	les-indices-de-salaire-de-base	Les indices de salaire de base - les-indices-de-salaire-de-base	2026-09-20
insee	INDICE-TRAITEMENT-FP	Indice de traitement brut dans la fonction publique de l’État - INDICE-TRAITEMENT-FP	2026-09-20
insee	SALAIRES-ACEMO	Indices trimestriels de salaires dans le secteur privé - Résultats par secteur d’activité - SALAIRES-ACEMO	2026-09-20
insee	SALAIRES-ACEMO-2017	Indices trimestriels de salaires dans le secteur privé - SALAIRES-ACEMO-2017	2026-09-20
insee	SALAIRES-ANNUELS	Salaires annuels - SALAIRES-ANNUELS	2026-09-20
insee	SMIC-COTISATIONS	Taux de cotisations et salaires minima garantis - SMIC-COTISATIONS	2026-09-20
insee	T_2101	2.101 – Revenu disponible brut des ménages et évolution du pouvoir d’achat par personne, par ménage et par unité de consommation (En milliards euros et %) - t_2101	2026-09-22
insee	t_salaire_val	Salaire moyen par tête - SMPT (données CVS) - t_salaire_val	2026-09-03