Point d’indice de la fonction publique - indicefp

Données - IPP

Auteur

Info

source	dataset	Title	Updated
ipp	indicefp	Point d'indice de la fonction publique	2026-08-11
insee	IPCH-2025	Indices des prix à la consommation harmonisés - Base 2025	2026-08-16
insee	IPC-2025	Indice des prix à la consommation - Base 2025	2026-08-17
insee	INDICE-TRAITEMENT-FP	Indice de traitement brut dans la fonction publique de l'État	2026-08-16

Indice Fonction Publique

All

Valeur

Code

indicefp |>
  select(date, point_indice_en_euros) |>
  gather(variable, value, -date) |>
  group_by(variable) |>
  complete(date = seq.Date(min(date), max(date), by = "day")) |>
  fill(value) |>
  ggplot() + geom_line(aes(x = date, y = value)) +
  scale_color_manual(values = viridis(8)[1:7]) +
  theme_minimal() +
  scale_x_date(breaks = seq(1920, 2100, 5) |> paste0("-01-01") |> as.Date(),
               labels = date_format("%Y")) +
  theme(legend.position = c(0.3, 0.8),
        legend.title = element_blank()) +
  scale_y_log10(breaks = seq(0, 200, 5),
                     labels = dollar_format(accuracy = 1, suffix = " € / point", prefix = "")) + 
  ylab("Point Indice Fonction Publique (euros)") + xlab("")

Base 100

Code

indicefp |>
  select(date, point_indice_en_euros) |>
  gather(variable, value, -date) |>
  group_by(variable) |>
  complete(date = seq.Date(min(date), max(date), by = "day")) |>
  fill(value) |>
  mutate(value = 100*value/value[1]) |>
  ggplot() + geom_line(aes(x = date, y = value)) +
  theme_minimal() +
  scale_x_date(breaks = seq(1920, 2100, 5) |> paste0("-01-01") |> as.Date(),
               labels = date_format("%Y")) +
  theme(legend.position = c(0.3, 0.8),
        legend.title = element_blank()) +
  scale_y_log10(breaks = seq(0, 3000, 100)) +
  ylab("Point Indice Fonction Publique") + xlab("")

1970-1990

Valeur

Code

indicefp |>
  select(date, point_indice_en_euros) |>
  gather(variable, value, -date) |>
  group_by(variable) |>
  complete(date = seq.Date(min(date), max(date), by = "day")) |>
  fill(value) |>
  filter(date >= as.Date("1970-01-01"),
         date <= as.Date("1990-01-01")) |>
  ggplot() + geom_line(aes(x = date, y = value)) +
  theme_minimal() + ylab("Point Indice Fonction Publique (euros)") + xlab("") +
  scale_x_date(breaks = seq(1920, 2100, 2) |> paste0("-01-01") |> as.Date(),
               labels = date_format("%Y")) +
  scale_y_log10(breaks = seq(0, 200, 4),
                     labels = dollar_format(accuracy = 1, suffix = " € / point", prefix = ""))

Base 100

Code

indicefp |>
  select(date, point_indice_en_euros) |>
  gather(variable, value, -date) |>
  group_by(variable) |>
  complete(date = seq.Date(min(date), max(date), by = "day")) |>
  fill(value) |>
  filter(date >= as.Date("1970-01-01"),
         date <= as.Date("1990-01-01")) |>
  mutate(value = 100*value/value[1]) |>
  ggplot() + geom_line(aes(x = date, y = value)) +
  theme_minimal() + ylab("Point Indice Fonction Publique") + xlab("") +
  scale_x_date(breaks = seq(1920, 2100, 2) |> paste0("-01-01") |> as.Date(),
               labels = date_format("%Y")) +
  scale_y_log10(breaks = seq(0, 1000, 20))

1986-

Valeur

Code

indicefp |>
  arrange(desc(date)) |>
  # Add today's date to be equal ------
  slice(1, 1:n()) |>
  mutate(date = ifelse(row_number() == 1, Sys.Date(), date)) |>
  mutate(date = as.Date(date)) |>
  select(date, point_indice_en_euros) |>
  gather(variable, value, -date) |>
  group_by(variable) |>
  complete(date = seq.Date(min(date), max(date), by = "day")) |>
  fill(value) |>
  filter(date >= as.Date("1986-01-01")) |>
  ggplot() + geom_line(aes(x = date, y = value)) +
  theme_minimal() + ylab("Point Indice Fonction Publique (euros)") + xlab("") +
  scale_x_date(breaks = seq(1920, 2100, 5) |> paste0("-01-01") |> as.Date(),
               labels = date_format("%Y")) +
  scale_y_log10(breaks = seq(0, 200, 1),
                     labels = dollar_format(accuracy = 1, suffix = " € / point", prefix = ""))

Base 100

Code

indicefp |>
  select(date, point_indice_en_euros) |>
  gather(variable, value, -date) |>
  group_by(variable) |>
  complete(date = seq.Date(min(date), max(date), by = "day")) |>
  fill(value) |>
  filter(date >= as.Date("1986-01-01")) |>
  mutate(value = 100*value/value[1]) |>
  ggplot() + geom_line(aes(x = date, y = value)) +
  theme_minimal() + ylab("Point Indice Fonction Publique") + xlab("") +
  scale_x_date(breaks = seq(1920, 2100, 5) |> paste0("-01-01") |> as.Date(),
               labels = date_format("%Y")) +
  scale_y_log10(breaks = seq(0, 200, 5))

1990-

Valeur

Code

indicefp |>
  select(date, point_indice_en_euros) |>
  gather(variable, value, -date) |>
  group_by(variable) |>
  complete(date = seq.Date(min(date), max(date), by = "day")) |>
  fill(value) |>
  filter(date >= as.Date("1990-01-01")) |>
  ggplot() + geom_line(aes(x = date, y = value)) +
  theme_minimal() + ylab("Point Indice Fonction Publique (euros)") + xlab("") +
  scale_x_date(breaks = seq(1920, 2100, 2) |> paste0("-01-01") |> as.Date(),
               labels = date_format("%Y")) +
  scale_y_log10(breaks = seq(0, 200, 1),
                     labels = dollar_format(accuracy = 1, suffix = " € / point", prefix = ""))

Base 100

Code

indicefp |>
  select(date, point_indice_en_euros) |>
  gather(variable, value, -date) |>
  group_by(variable) |>
  complete(date = seq.Date(min(date), max(date), by = "day")) |>
  fill(value) |>
  filter(date >= as.Date("1990-01-01")) |>
  mutate(value = 100*value/value[1]) |>
  ggplot() + geom_line(aes(x = date, y = value)) +
  theme_minimal() + ylab("Point Indice Fonction Publique") + xlab("") +
  scale_x_date(breaks = seq(1920, 2100, 2) |> paste0("-01-01") |> as.Date(),
               labels = date_format("%Y")) +
  scale_y_log10(breaks = seq(0, 200, 5))

1992-

Valeur

Code

indicefp |>
  select(date, point_indice_en_euros) |>
  gather(variable, value, -date) |>
  group_by(variable) |>
  complete(date = seq.Date(min(date), max(date), by = "day")) |>
  fill(value) |>
  filter(date >= as.Date("1992-01-01")) |>
  ggplot() + geom_line(aes(x = date, y = value)) +
  theme_minimal() + ylab("Point Indice Fonction Publique (euros)") + xlab("") +
  scale_x_date(breaks = seq(1920, 2100, 2) |> paste0("-01-01") |> as.Date(),
               labels = date_format("%Y")) +
  scale_y_log10(breaks = seq(0, 200, 1),
                     labels = dollar_format(accuracy = 1, suffix = " € / point", prefix = ""))

Base 100

Code

indicefp |>
  select(date, point_indice_en_euros) |>
  gather(variable, value, -date) |>
  group_by(variable) |>
  complete(date = seq.Date(min(date), max(date), by = "day")) |>
  fill(value) |>
  filter(date >= as.Date("1992-01-01")) |>
  mutate(value = 100*value/value[1]) |>
  ggplot() + geom_line(aes(x = date, y = value)) +
  theme_minimal() + ylab("Point Indice Fonction Publique (euros)") + xlab("") +
  scale_x_date(breaks = seq(1920, 2100, 2) |> paste0("-01-01") |> as.Date(),
               labels = date_format("%Y")) +
  scale_y_log10(breaks = seq(0, 200, 5))

1996-

Valeur

Code

indicefp |>
  select(date, point_indice_en_euros) |>
  gather(variable, value, -date) |>
  group_by(variable) |>
  complete(date = seq.Date(min(date), max(date), by = "day")) |>
  fill(value) |>
  filter(date >= as.Date("1996-01-01")) |>
  ggplot() + geom_line(aes(x = date, y = value)) +
  theme_minimal() + ylab("Point Indice Fonction Publique (euros)") + xlab("") +
  scale_x_date(breaks = seq(1920, 2100, 2) |> paste0("-01-01") |> as.Date(),
               labels = date_format("%Y")) +
  scale_y_log10(breaks = seq(0, 200, 1),
                     labels = dollar_format(accuracy = 1, suffix = " € / point", prefix = ""))

Base 100

Code

indicefp |>
  select(date, point_indice_en_euros) |>
  gather(variable, value, -date) |>
  group_by(variable) |>
  complete(date = seq.Date(min(date), max(date), by = "day")) |>
  fill(value) |>
  filter(date >= as.Date("1996-01-01")) |>
  mutate(value = 100*value/value[1]) |>
  ggplot() + geom_line(aes(x = date, y = value)) +
  theme_minimal() + ylab("Point Indice Fonction Publique (euros)") + xlab("") +
  scale_x_date(breaks = seq(1920, 2100, 2) |> paste0("-01-01") |> as.Date(),
               labels = date_format("%Y")) +
  scale_y_log10(breaks = seq(0, 200, 5))

inflation

Table

Code

cpi |>
  print_table_conditional()

Graph

All

Code

cpi |>
  mutate(cpi = 100*cpi/cpi[date == as.Date("1951-01-01")]) |>
  ggplot() + geom_line(aes(x = date, y = cpi)) +
  scale_x_date(breaks = seq(1950, 2100, 5) |> paste0("-01-01") |> as.Date(),
               labels = date_format("%Y")) +
  scale_y_log10(breaks = c(100, 200, 300, 500, 800, 1000, 1200, 1500, 2000)) +
  theme_minimal() + xlab("") + ylab("")

1990-

Code

cpi |>
  filter(date >= as.Date("1990-01-01")) |>
  mutate(cpi = 100*cpi/cpi[date == as.Date("1990-01-01")]) |>
  ggplot() + geom_line(aes(x = date, y = cpi)) +
  scale_x_date(breaks = seq(1950, 2100, 5) |> paste0("-01-01") |> as.Date(),
               labels = date_format("%Y")) +
  scale_y_log10(breaks = seq(100, 200, 5)) +
  theme_minimal() + xlab("") + ylab("Inflation (100 = 1990)")

1995-

Code

cpi |>
  filter(date >= as.Date("1995-01-01")) |>
  mutate(cpi = 100*cpi/cpi[date == as.Date("1995-01-01")]) |>
  ggplot() + geom_line(aes(x = date, y = cpi)) +
  scale_x_date(breaks = seq(1950, 2100, 5) |> paste0("-01-01") |> as.Date(),
               labels = date_format("%Y")) +
  scale_y_log10(breaks = seq(100, 200, 5)) +
  theme_minimal() + xlab("") + ylab("Inflation (100 = 1995)")

1996-

Code

cpi |>
  filter(date >= as.Date("1996-01-01")) |>
  mutate(cpi = 100*cpi/cpi[date == as.Date("1996-01-01")]) |>
  ggplot() + geom_line(aes(x = date, y = cpi)) +
  scale_x_date(breaks = seq(1950, 2100, 2) |> paste0("-01-01") |> as.Date(),
               labels = date_format("%Y")) +
  scale_y_log10(breaks = seq(100, 200, 5)) +
  theme_minimal() + xlab("") + ylab("Inflation (100 = 1996)")

2000-

Code

cpi |>
  filter(date >= as.Date("2000-01-01")) |>
  mutate(cpi = 100*cpi/cpi[date == as.Date("2000-01-01")]) |>
  ggplot() + geom_line(aes(x = date, y = cpi)) +
  scale_x_date(breaks = seq(1950, 2100, 2) |> paste0("-01-01") |> as.Date(),
               labels = date_format("%Y")) +
  scale_y_log10(breaks = seq(100, 200, 5)) +
  theme_minimal() + xlab("") + ylab("Inflation (100 = 2000)")

2012-

Code

cpi |>
  filter(date >= as.Date("2012-01-01")) |>
  mutate(cpi = 100*cpi/cpi[date == as.Date("2012-01-01")]) |>
  ggplot() + geom_line(aes(x = date, y = cpi)) +
  scale_x_date(breaks = seq(1950, 2100, 2) |> paste0("-01-01") |> as.Date(),
               labels = date_format("%Y")) +
  scale_y_log10(breaks = seq(100, 200, 1)) +
  theme_minimal() + xlab("") + ylab("Inflation (100 = 2012)")

Indice Fonction Publique Réel

1970-

Annuel

Code

indicefp |>
  select(date, point_indice_en_euros) |>
  
  arrange(desc(date)) |>
  gather(variable, value, -date) |>
  group_by(variable) |>
  complete(date = seq.Date(min(date), max(date), by = "day")) |>
  fill(value) |>
  filter(month(date) == 1,
         day(date) == 1) |>
  left_join(cpi, by = "date") |>
  mutate(value = value/cpi,
         value = 100*value/value[1]) |>
  ggplot() + geom_line(aes(x = date, y = value)) + theme_minimal() +
  scale_x_date(breaks = seq(1920, 2100, 5) |> paste0("-01-01") |> as.Date(),
               labels = date_format("%Y")) +
  scale_y_log10(breaks = seq(0, 200, 2)) + 
  ylab("Point Indice Fonction Publique (euros constants)") + xlab("")

1965-

Annuel

Code

indicefp |>
  select(date, point_indice_en_euros) |>
  
  arrange(desc(date)) |>
  gather(variable, value, -date) |>
  group_by(variable) |>
  complete(date = seq.Date(min(date), max(date), by = "day")) |>
  fill(value) |>
  filter(date >= as.Date("1965-01-01")) |>
  filter(month(date) == 1,
         day(date) == 1) |>
  left_join(cpi, by = "date") |>
  mutate(value = value/cpi,
         value = 100*value/value[1]) |>
  ggplot() + geom_line(aes(x = date, y = value)) + theme_minimal() +
  scale_x_date(breaks = seq(1920, 2100, 5) |> paste0("-01-01") |> as.Date(),
               labels = date_format("%Y")) +
  scale_y_log10(breaks = seq(0, 200, 2)) + 
  ylab("Point Indice Fonction Publique (euros constants)") + xlab("")

1996-

Annuel

Code

indicefp |>
  select(date, point_indice_en_euros) |>
  
  arrange(desc(date)) |>
  gather(variable, value, -date) |>
  group_by(variable) |>
  complete(date = seq.Date(min(date), max(date), by = "day")) |>
  fill(value) |>
  filter(month(date) == 1,
         day(date) == 1) |>
  left_join(cpi, by = "date") |>
  filter(date >= as.Date("1996-01-01")) |>
  mutate(value = value/cpi,
         value = 100*value/value[1]) |>
  ggplot() + geom_line(aes(x = date, y = value)) + theme_minimal() +
  scale_x_date(breaks = seq(1920, 2100, 2) |> paste0("-01-01") |> as.Date(),
               labels = date_format("%Y")) +
  scale_y_log10(breaks = seq(0, 200, 2)) + 
  ylab("Point Indice Fonction Publique (euros constants)") + xlab("")

CPI

Code

indicefp |>
  select(date, point_indice_en_euros) |>
  
  arrange(desc(date)) |>
  gather(variable, value, -date) |>
  group_by(variable) |>
  complete(date = seq.Date(min(date), max(date), by = "day")) |>
  fill(value) |>
  filter(month(date) == 1,
         day(date) == 1) |>
  left_join(cpi2, by = "date") |>
  filter(date >= as.Date("1996-01-01")) |>
  mutate(value = value/cpi,
         value = 100*value/value[1]) |>
  ggplot() + geom_line(aes(x = date, y = value)) + theme_minimal() +
  scale_x_date(breaks = seq(1920, 2100, 2) |> paste0("-01-01") |> as.Date(),
               labels = date_format("%Y")) +
  scale_y_log10(breaks = seq(0, 200, 2)) + 
  ylab("Point Indice Fonction Publique (€ constants)") + xlab("")

CPIH

All

Code

indicefp |>
  select(date, point_indice_en_euros) |>
  arrange(desc(date)) |>
  gather(variable, value, -date) |>
  group_by(variable) |>
  complete(date = seq.Date(min(date), max(date), by = "day")) |>
  fill(value) |>
  filter(month(date) == 1,
         day(date) == 1) |>
  left_join(cpi2, by = "date") |>
  filter(date >= as.Date("1997-01-01")) |>
  mutate(value = value/cpih,
         value = 100*value/value[1]) |>
  ggplot() + geom_line(aes(x = date, y = value)) + theme_minimal() +
  scale_x_date(breaks = seq(1920, 2100, 2) |> paste0("-01-01") |> as.Date(),
               labels = date_format("%Y")) +
  scale_y_log10(breaks = seq(0, 200, 2)) + 
  ylab("Point Indice Fonction Publique (€ constants)") + xlab("")

in %

Code

data_alter_eco <- indicefp |>
  select(date, point_indice_en_euros) |>
  gather(variable, value, -date) |>
  group_by(variable) |>
  complete(date = seq.Date(min(date), max(date), by = "day")) |>
  fill(value) |>
  ungroup() |>
  filter(month(date) == 1,
         day(date) == 1) |>
  left_join(cpi2, by = "date") |>
  filter(date >= as.Date("1996-01-01")) |>
  transmute(date, 
            value_cpih = value/cpih,
            value_cpi = value/cpi) |>
  transmute(date,
            `Valeur du Point d'Indice de la Fonction Publique par rapport à 1996 (IPCH, Eurostat)` = value_cpih/value_cpih[1],
            `Valeur du Point d'Indice de la Fonction Publique par rapport à 1996 (IPC, INSEE)` = value_cpi/value_cpi[1])

write_csv(data_alter_eco, file = "data_alter_eco.csv")

data_alter_eco |>
  gather(variable, value, -date) |>
  ggplot() + geom_line(aes(x = date, y = value, color = variable)) + theme_minimal() +
  scale_x_date(breaks = seq(1920, 2100, 2) |> paste0("-01-01") |> as.Date(),
               labels = date_format("%Y")) +
  scale_color_manual(values = viridis(3)[1:2]) +
  theme(legend.position = c(0.43, 0.1),
        legend.title = element_blank()) +
  scale_y_log10(breaks = seq(1, 0.02, -0.02),
                labels = percent(seq(1, 0.02, -0.02)-1, acc = 1)) + 
  ylab("Valeur du Point Indice Fonction Publique vs. 1996") + xlab("")

in %

Code

indicefp |>
  select(date, point_indice_en_euros) |>
  gather(variable, value, -date) |>
  group_by(variable) |>
  complete(date = seq.Date(min(date), max(date), by = "day")) |>
  fill(value) |>
  ungroup() |>
  filter(month(date) == 1,
         day(date) == 1) |>
  left_join(cpi2, by = "date") |>
  filter(date >= as.Date("1997-01-01")) |>
  transmute(date, 
            value_cpih = value/cpih,
            value_cpi = value/cpi) |>
  transmute(date,
            `Valeur réelle du Point d'Indice par rapport à 1996` = value_cpih/value_cpih[1]) |>
  gather(variable, value, -date) |>
  ggplot() + geom_line(aes(x = date, y = value)) + theme_minimal() +
  scale_x_date(breaks = seq(1920, 2100, 2) |> paste0("-01-01") |> as.Date(),
               labels = date_format("%Y")) +
  scale_color_manual(values = viridis(3)[1:2]) +
  theme(legend.position = c(0.43, 0.1),
        legend.title = element_blank()) +
  scale_y_log10(breaks = seq(1, 0.02, -0.02),
                labels = percent(seq(1, 0.02, -0.02)-1, acc = 1)) + 
  ylab("Valeur du Point Indice vs. 1996") + xlab("") + 
  labs(title = "Valeur réelle du Point Indice de la Fonction Publique par rapport à 1997",
              subtitle = "(Indice des prix utilisé: Indice des Prix à la Consommation Harmonisé (IPCH))")

1996-

1997-

Mensuel

Code

net_brut_mensuel <- read_parquet(here::here("data", "insee", "INDICE-TRAITEMENT-FP-net-brut-mensuel.parquet"))
indicefp |>
  select(date, point_indice_en_euros) |>
  arrange(desc(date)) |>
  gather(variable, value, -date) |>
  group_by(variable) |>
  complete(date = seq.Date(min(date), max(date), by = "day")) |>
  fill(value) |>
  ungroup() |>
  left_join(cpi2_m, by = "date") |>
  filter(day(date) == 1) |>
  left_join(net_brut_mensuel, by = "date") |>
  filter(date >= as.Date("1997-01-01")) |>
  transmute(date,
            `Valeur du Point d'Indice de la Fonction Publique vs. 1997 (IPCH, Eurostat)` = (value/value[1])*(cpih[1]/cpih)*net_brut,
            `Valeur du Point d'Indice de la Fonction Publique vs. 1997 (IPC, INSEE)` = (value/value[1])*(cpi[1]/cpi)*net_brut) |>
  gather(variable, OBS_VALUE, -date) |>
  ggplot() + geom_line(aes(x = date, y = OBS_VALUE, color = variable)) + theme_minimal() +
  scale_x_date(breaks = c(seq(1997, 2100, 5), seq(1999, 2100, 5)) |> paste0("-01-01") |> as.Date(),
               labels = date_format("%Y")) +
  theme(legend.position = c(0.43, 0.12),
        legend.title = element_blank()) +
  scale_y_log10(breaks = seq(1, 0.02, -0.02),
                labels = percent(seq(1, 0.02, -0.02)-1, acc = 1)) + 
  ylab("Valeur du Point Indice Fonction Publique vs. 1997") + xlab("") +
  geom_text(data = . %>% filter((year(date) %in% seq(1999, 2019, 5) & month(date) == 1) | date == max(date)),
            aes(x = date, y = OBS_VALUE, label = percent(OBS_VALUE-1, acc = 0.1)), 
                  fontface ="bold", color = "black", size = 3)

1999-

Code

indicefp |>
  select(date, point_indice_en_euros) |>
  gather(variable, value, -date) |>
  group_by(variable) |>
  complete(date = seq.Date(min(date), max(date), by = "day")) |>
  fill(value) |>
  ungroup() |>
  filter(month(date) == 1,
         day(date) == 1) |>
  left_join(cpi2, by = "date") |>
  filter(date >= as.Date("1999-01-01")) |>
  transmute(date, 
            value_cpih = value/cpih,
            value_cpi = value/cpi) |>
  transmute(date,
            `Valeur du Point d'Indice de la Fonction Publique vs. 1999 (IPCH, Eurostat)` = value_cpih/value_cpih[1]-1,
            `Valeur du Point d'Indice de la Fonction Publique vs. 1999 (IPC, INSEE)` = value_cpi/value_cpi[1]-1) |>
  gather(variable, OBS_VALUE, -date) |>
  ggplot() + geom_line(aes(x = date, y = OBS_VALUE, color = variable)) + theme_minimal() +
  scale_x_date(breaks = seq(1999, 2100, 5) |> paste0("-01-01") |> as.Date(),
               labels = date_format("%Y")) +
  theme(legend.position = c(0.43, 0.12),
        legend.title = element_blank()) +
  scale_y_continuous(breaks = 0.01*seq(-200, 200, 2),
                     labels = percent_format(acc = 1)) + 
  ylab("Valeur du Point Indice Fonction Publique vs. 1999") + xlab("") +
  geom_text(data = . %>% filter(year(date) %in% seq(1999, 2040, 5)),aes(x = date, y = OBS_VALUE, label = percent(OBS_VALUE, acc = 0.1)), 
                  fontface ="plain", color = "black", size = 3)

2015- (avec correction net brut)

Annuel

Code

net_brut_annuel <- read_parquet(here::here("data", "insee", "INDICE-TRAITEMENT-FP-net-brut-annuel.parquet"))
indicefp |>
  select(date, point_indice_en_euros) |>
  arrange(desc(date)) |>
  gather(variable, value, -date) |>
  group_by(variable) |>
  complete(date = seq.Date(min(date), max(date), by = "day")) |>
  fill(value) |>
  ungroup() |>
  filter(month(date) == 1,
         day(date) == 1) |>
  left_join(cpi2, by = "date") |>
  left_join(net_brut_annuel, by = "date") |>
  filter(date >= as.Date("2015-01-01")) |>
  transmute(date,
            `Valeur du Point d'Indice de la Fonction Publique vs. 1999 (IPCH, Eurostat)` = (value/value[1])*(cpih[1]/cpih)*net_brut-1,
            `Valeur du Point d'Indice de la Fonction Publique vs. 1999 (IPC, INSEE)` = (value/value[1])*(cpi[1]/cpi)*net_brut-1) |>
  gather(variable, OBS_VALUE, -date) |>
  ggplot() + geom_line(aes(x = date, y = OBS_VALUE, color = variable)) + theme_minimal() +
  scale_x_date(breaks = seq(1999, 2100, 5) |> paste0("-01-01") |> as.Date(),
               labels = date_format("%Y")) +
  theme(legend.position = c(0.43, 0.12),
        legend.title = element_blank()) +
  scale_y_continuous(breaks = 0.01*seq(-200, 200, 2),
                     labels = percent_format(acc = 1)) + 
  ylab("Valeur du Point Indice Fonction Publique vs. 1999") + xlab("") +
  geom_text(data = . %>% filter(year(date) %in% seq(1999, 2040, 5)),
            aes(x = date, y = OBS_VALUE, label = percent(OBS_VALUE, acc = 1)), 
                  fontface ="plain", color = "black", size = 3)

Mensuel

Code

net_brut_mensuel <- read_parquet(here::here("data", "insee", "INDICE-TRAITEMENT-FP-net-brut-mensuel.parquet"))
indicefp |>
  select(date, point_indice_en_euros) |>
  arrange(desc(date)) |>
  gather(variable, value, -date) |>
  group_by(variable) |>
  complete(date = seq.Date(min(date), max(date), by = "day")) |>
  fill(value) |>
  ungroup() |>
  left_join(cpi2_m, by = "date") |>
  filter(day(date) == 1) |>
  left_join(net_brut_mensuel, by = "date") |>
  filter(date >= as.Date("2015-01-01")) |>
  transmute(date,
            `Valeur du Point d'Indice de la Fonction Publique vs. 2015 (IPCH, Eurostat)` = (value/value[1])*(cpih[1]/cpih)*(net_brut/net_brut[1]),
            `Valeur du Point d'Indice de la Fonction Publique vs. 2015 (IPC, INSEE)` = (value/value[1])*(cpi[1]/cpi)*(net_brut/net_brut[1])) |>
  gather(variable, OBS_VALUE, -date) |>
  ggplot() + geom_line(aes(x = date, y = OBS_VALUE, color = variable)) + theme_minimal() +
  scale_x_date(breaks = c(seq(1999, 2100, 1), seq(1997, 2100, 5)) |> paste0("-01-01") |> as.Date(),
               labels = date_format("%Y")) +
  theme(legend.position = c(0.43, 0.12),
        legend.title = element_blank()) +
  scale_y_log10(breaks = seq(1, 0.02, -0.02),
                labels = percent(seq(1, 0.02, -0.02)-1, acc = 1)) + 
  ylab("Valeur du Point Indice Fonction Publique vs. 2015") + xlab("") +
  geom_label(data = . %>% filter(date == max(date)),
            aes(x = date, y = OBS_VALUE, label = percent(OBS_VALUE-1, acc = 0.1), color = variable), 
                  fontface ="bold", size = 4)

2021T2-

Code

invisible(Sys.setlocale("LC_TIME", "fr_FR.UTF-8"))
net_brut_mensuel <- read_parquet(here::here("data", "insee", "INDICE-TRAITEMENT-FP-net-brut-mensuel.parquet"))
indicefp |>
  select(date, point_indice_en_euros) |>
  arrange(desc(date)) |>
  gather(variable, value, -date) |>
  group_by(variable) |>
  complete(date = seq.Date(min(date), max(date), by = "day")) |>
  fill(value) |>
  ungroup() |>
  left_join(cpi2_m, by = "date") |>
  filter(day(date) == 1) |>
  left_join(net_brut_mensuel, by = "date") |>
  filter(date >= as.Date("2021-04-01")) |>
  transmute(date,
            `Valeur du Point d'Indice de la Fonction Publique vs. Avril 2021 (IPCH, Eurostat)` = (value/value[1])*(cpih[1]/cpih)*(net_brut/net_brut[1]),
            `Valeur du Point d'Indice de la Fonction Publique vs. Avril 2021 (IPC, INSEE)` = (value/value[1])*(cpi[1]/cpi)*(net_brut/net_brut[1])) |>
  gather(variable, OBS_VALUE, -date) |>
  filter(!is.na(OBS_VALUE)) |>
  ggplot() + geom_line(aes(x = date, y = OBS_VALUE, color = variable)) + theme_minimal() +
  scale_x_date(breaks = "3 months",
               labels = date_format("%B %Y")) +
  theme(legend.position = c(0.43, 0.12),
        legend.title = element_blank(),
        axis.text.x = element_text(angle = 45, vjust = 1, hjust = 1)) +
  scale_y_log10(breaks = seq(1, 0.02, -0.01),
                labels = percent(seq(1, 0.02, -0.01)-1, acc = 1)) + 
  ylab("Valeur du Point Indice Fonction Publique vs. Avril 2021") + xlab("") +
  geom_label(data = . %>% filter(date == max(date)),
            aes(x = date, y = OBS_VALUE, label = percent(OBS_VALUE-1, acc = 0.1), color = variable), 
                  fontface ="bold")

2021T4-

Code

net_brut_mensuel <- read_parquet(here::here("data", "insee", "INDICE-TRAITEMENT-FP-net-brut-mensuel.parquet"))
indicefp |>
  select(date, point_indice_en_euros) |>
  arrange(desc(date)) |>
  gather(variable, value, -date) |>
  group_by(variable) |>
  complete(date = seq.Date(min(date), max(date), by = "day")) |>
  fill(value) |>
  ungroup() |>
  left_join(cpi2_m, by = "date") |>
  filter(day(date) == 1) |>
  left_join(net_brut_mensuel, by = "date") |>
  filter(date >= as.Date("2021-10-01")) |>
  transmute(date,
            `Valeur du Point d'Indice de la Fonction Publique vs. 2021T4 (IPCH, Eurostat)` = (value/value[1])*(cpih[1]/cpih)*(net_brut/net_brut[1]),
            `Valeur du Point d'Indice de la Fonction Publique vs. 2015 (IPC, INSEE)` = (value/value[1])*(cpi[1]/cpi)*(net_brut/net_brut[1])) |>
  gather(variable, OBS_VALUE, -date) |>
  ggplot() + geom_line(aes(x = date, y = OBS_VALUE, color = variable)) + theme_minimal() +
  scale_x_date(breaks = "3 months",
               labels = date_format("%Y %b")) +
  theme(legend.position = c(0.43, 0.12),
        legend.title = element_blank(),
        axis.text.x = element_text(angle = 45, vjust = 1, hjust = 1)) +
  scale_y_log10(breaks = seq(1, 0.02, -0.01),
                labels = percent(seq(1, 0.02, -0.01)-1, acc = 1)) + 
  ylab("Valeur du Point Indice Fonction Publique vs. 2015") + xlab("") +
  geom_label(data = . %>% filter(date == max(date)),
            aes(x = date, y = OBS_VALUE, label = percent(OBS_VALUE-1, acc = 0.1)), 
                  fontface ="bold", color = "black", size = 3)

2012-

Mensuel

Code

net_brut_mensuel <- read_parquet(here::here("data", "insee", "INDICE-TRAITEMENT-FP-net-brut-mensuel.parquet"))
indicefp |>
  select(date, point_indice_en_euros) |>
  arrange(desc(date)) |>
  gather(variable, value, -date) |>
  group_by(variable) |>
  complete(date = seq.Date(min(date), max(date), by = "day")) |>
  fill(value) |>
  ungroup() |>
  left_join(cpi2_m, by = "date") |>
  filter(day(date) == 1) |>
  left_join(net_brut_mensuel, by = "date") |>
  filter(date >= as.Date("2012-01-01")) |>
  transmute(date,
            `Valeur du Point d'Indice de la Fonction Publique vs. 2012 (IPCH, Eurostat)` = (value/value[1])*(cpih[1]/cpih)*(net_brut/net_brut[1]),
            `Valeur du Point d'Indice de la Fonction Publique vs. 2012 (IPC, INSEE)` = (value/value[1])*(cpi[1]/cpi)*(net_brut/net_brut[1])) |>
  gather(variable, OBS_VALUE, -date) |>
  ggplot() + geom_line(aes(x = date, y = OBS_VALUE, color = variable)) + theme_minimal() +
  scale_x_date(breaks = c(seq(1999, 2100, 1), seq(1997, 2100, 5)) |> paste0("-01-01") |> as.Date(),
               labels = date_format("%Y")) +
  theme(legend.position = c(0.43, 0.12),
        legend.title = element_blank()) +
  scale_y_log10(breaks = seq(1, 0.02, -0.02),
                labels = percent(seq(1, 0.02, -0.02)-1, acc = 1)) + 
  ylab("Valeur du Point Indice Fonction Publique vs. 2012") + xlab("") +
  geom_text_repel(data = . %>% filter(year(date) %in% c(2017, 2024),
                                month(date) == 3),
            aes(x = date, y = OBS_VALUE, label = percent(OBS_VALUE-1, acc = 0.1)), 
                  fontface ="bold", color = "black", size = 3)

1999- (avec correction net brut)

Annuel

Code

net_brut_annuel <- read_parquet(here::here("data", "insee", "INDICE-TRAITEMENT-FP-net-brut-annuel.parquet"))
indicefp |>
  select(date, point_indice_en_euros) |>
  arrange(desc(date)) |>
  gather(variable, value, -date) |>
  group_by(variable) |>
  complete(date = seq.Date(min(date), max(date), by = "day")) |>
  fill(value) |>
  ungroup() |>
  filter(month(date) == 1,
         day(date) == 1) |>
  left_join(cpi2, by = "date") |>
  left_join(net_brut_annuel, by = "date") |>
  filter(date >= as.Date("1999-01-01")) |>
  transmute(date,
            `Valeur du Point d'Indice de la Fonction Publique vs. 1999 (IPCH, Eurostat)` = (value/value[1])*(cpih[1]/cpih)*net_brut-1,
            `Valeur du Point d'Indice de la Fonction Publique vs. 1999 (IPC, INSEE)` = (value/value[1])*(cpi[1]/cpi)*net_brut-1) |>
  gather(variable, OBS_VALUE, -date) |>
  ggplot() + geom_line(aes(x = date, y = OBS_VALUE, color = variable)) + theme_minimal() +
  scale_x_date(breaks = seq(1999, 2100, 5) |> paste0("-01-01") |> as.Date(),
               labels = date_format("%Y")) +
  theme(legend.position = c(0.43, 0.12),
        legend.title = element_blank()) +
  scale_y_continuous(breaks = 0.01*seq(-200, 200, 2),
                     labels = percent_format(acc = 1)) + 
  ylab("Valeur du Point Indice Fonction Publique vs. 1999") + xlab("") +
  geom_text(data = . %>% filter(year(date) %in% seq(1999, 2040, 5)),
            aes(x = date, y = OBS_VALUE, label = percent(OBS_VALUE, acc = 1)), 
                  fontface ="plain", color = "black", size = 3)

Trimestriel

Code

net_brut_trimestriel <- read_parquet(here::here("data", "insee", "INDICE-TRAITEMENT-FP-net-brut-trimestriel.parquet"))
indicefp |>
  select(date, point_indice_en_euros) |>
  arrange(desc(date)) |>
  gather(variable, value, -date) |>
  group_by(variable) |>
  complete(date = seq.Date(min(date), max(date), by = "day")) |>
  fill(value) |>
  ungroup() |>
  left_join(cpi2_m, by = "date") |>
  filter(month(date) %in% c(1, 4, 7, 10),
         day(date) == 1) |>
  left_join(net_brut_trimestriel, by = "date") |>
  filter(date >= as.Date("1999-01-01")) |>
  transmute(date,
            `Valeur du Point d'Indice de la Fonction Publique vs. 1999 (IPCH, Eurostat)` = (value/value[1])*(cpih[1]/cpih)*net_brut-1,
            `Valeur du Point d'Indice de la Fonction Publique vs. 1999 (IPC, INSEE)` = (value/value[1])*(cpi[1]/cpi)*net_brut-1) |>
  gather(variable, OBS_VALUE, -date) |>
  ggplot() + geom_line(aes(x = date, y = OBS_VALUE, color = variable)) + theme_minimal() +
  scale_x_date(breaks = seq(1999, 2100, 5) |> paste0("-01-01") |> as.Date(),
               labels = date_format("%Y")) +
  theme(legend.position = c(0.43, 0.12),
        legend.title = element_blank()) +
  scale_y_continuous(breaks = 0.01*seq(-200, 200, 2),
                     labels = percent_format(acc = 1)) + 
  ylab("Valeur du Point Indice Fonction Publique vs. 1999") + xlab("") +
  geom_text(data = . %>% filter(year(date) %in% seq(1999, 2040, 5),
                                month(date) == 1),
            aes(x = date, y = OBS_VALUE, label = percent(OBS_VALUE, acc = 1)), 
                  fontface ="plain", color = "black", size = 3)

Mensuel

Code

net_brut_mensuel <- read_parquet(here::here("data", "insee", "INDICE-TRAITEMENT-FP-net-brut-mensuel.parquet"))
indicefp |>
  select(date, point_indice_en_euros) |>
  arrange(desc(date)) |>
  gather(variable, value, -date) |>
  group_by(variable) |>
  complete(date = seq.Date(min(date), max(date), by = "day")) |>
  fill(value) |>
  ungroup() |>
  left_join(cpi2_m, by = "date") |>
  filter(day(date) == 1) |>
  left_join(net_brut_mensuel, by = "date") |>
  filter(date >= as.Date("1999-01-01")) |>
  transmute(date,
            `Valeur du Point d'Indice de la Fonction Publique vs. 1999 (IPCH, Eurostat)` = (value/value[1])*(cpih[1]/cpih)*net_brut,
            `Valeur du Point d'Indice de la Fonction Publique vs. 1999 (IPC, INSEE)` = (value/value[1])*(cpi[1]/cpi)*net_brut) |>
  gather(variable, OBS_VALUE, -date) |>
  group_by(date) |>
  arrange(desc(date)) |>
  filter(!is.na(OBS_VALUE)) |>
  filter(n() == 2) |>
  ungroup() |>
  ggplot() + geom_line(aes(x = date, y = OBS_VALUE, color = variable)) + theme_minimal() +
  scale_x_date(breaks = c(seq(1999, 2100, 2)) |> paste0("-01-01") |> as.Date(),
               labels = date_format("%Y")) +
  theme(legend.position = c(0.43, 0.12),
        legend.title = element_blank()) +
  scale_y_log10(breaks = seq(1, 0.02, -0.02),
                labels = percent(seq(1, 0.02, -0.02)-1, acc = 1)) + 
  ylab("Valeur du Point Indice Fonction Publique vs. 1999") + xlab("") +
  geom_text(data = . %>% filter((year(date) %in% seq(2009, 2019, 5) & month(date) == 1)),
            aes(x = date, y = OBS_VALUE, label = percent(OBS_VALUE-1, acc = 0.1)), 
                  fontface ="bold", color = "black", size = 3) +
  geom_label(data = . %>% filter(date == max(date)),
            aes(x = date, y = OBS_VALUE, label = percent(OBS_VALUE-1, acc = 0.1), color = variable), 
                  fontface ="bold", size = 3)

Mai 2007 (Sarkozy)

Code

net_brut_mensuel <- read_parquet(here::here("data", "insee", "INDICE-TRAITEMENT-FP-net-brut-mensuel.parquet"))
indicefp |>
  select(date, point_indice_en_euros) |>
  arrange(desc(date)) |>
  gather(variable, value, -date) |>
  group_by(variable) |>
  complete(date = seq.Date(min(date), max(date), by = "day")) |>
  fill(value) |>
  ungroup() |>
  left_join(cpi2_m, by = "date") |>
  filter(day(date) == 1) |>
  left_join(net_brut_mensuel, by = "date") |>
  filter(date >= as.Date("2007-05-01")) |>
  transmute(date,
            `Valeur du Point d'Indice de la Fonction Publique vs. mai 2007 (IPCH, Eurostat)` = (value/value[1])*(cpih[1]/cpih)*(net_brut/net_brut[1]),
            `Valeur du Point d'Indice de la Fonction Publique vs. mai 2007 (IPC, INSEE)` = (value/value[1])*(cpi[1]/cpi)*(net_brut/net_brut[1])) |>
  gather(variable, OBS_VALUE, -date) |>
  ggplot() + geom_line(aes(x = date, y = OBS_VALUE, color = variable)) + theme_minimal() +
  scale_x_date(breaks = c(seq(1999, 2100, 5), seq(1997, 2100, 5)) |> paste0("-01-01") |> as.Date(),
               labels = date_format("%Y")) +
  theme(legend.position = c(0.43, 0.12),
        legend.title = element_blank()) +
  scale_y_log10(breaks = seq(1, 0.02, -0.02),
                labels = percent(seq(1, 0.02, -0.02)-1, acc = 1)) + 
  ylab("Valeur du Point Indice Net vs. mai 2007") + xlab("") +
  geom_text(data = . %>% filter(date %in% c(max(date), as.Date("2012-05-01"), as.Date("2017-05-01"),
                                            as.Date("2022-05-01"))),
            aes(x = date, y = OBS_VALUE, label = percent(OBS_VALUE-1, acc = 0.1)), 
                  fontface ="bold", color = "black", size = 3)

2007-

Code

net_brut_mensuel <- read_parquet(here::here("data", "insee", "INDICE-TRAITEMENT-FP-net-brut-mensuel.parquet"))
indicefp |>
  select(date, point_indice_en_euros) |>
  arrange(desc(date)) |>
  gather(variable, value, -date) |>
  group_by(variable) |>
  complete(date = seq.Date(min(date), max(date), by = "day")) |>
  fill(value) |>
  ungroup() |>
  left_join(cpi2_m, by = "date") |>
  filter(day(date) == 1) |>
  left_join(net_brut_mensuel, by = "date") |>
  filter(date >= as.Date("2007-01-01")) |>
  transmute(date,
            `Valeur du Point d'Indice de la Fonction Publique vs. 2007 (IPCH, Eurostat)` = (value/value[1])*(cpih[1]/cpih)*net_brut,
            `Valeur du Point d'Indice de la Fonction Publique vs. 2007 (IPC, INSEE)` = (value/value[1])*(cpi[1]/cpi)*net_brut) |>
  gather(variable, OBS_VALUE, -date) |>
  ggplot() + geom_line(aes(x = date, y = OBS_VALUE, color = variable)) + theme_minimal() +
  scale_x_date(breaks = c(seq(1999, 2100, 5), seq(1997, 2100, 5)) |> paste0("-01-01") |> as.Date(),
               labels = date_format("%Y")) +
  theme(legend.position = c(0.43, 0.12),
        legend.title = element_blank()) +
  scale_y_log10(breaks = seq(1, 0.02, -0.02),
                labels = percent(seq(1, 0.02, -0.02)-1, acc = 1)) + 
  ylab("Valeur du Point Indice Fonction Publique vs. 2007") + xlab("") +
  geom_text_repel(data = . %>% filter(year(date) %in% seq(1999, 2040, 5),
                                month(date) == 1),
            aes(x = date, y = OBS_VALUE, label = percent(OBS_VALUE-1, acc = 0.1)), 
                  fontface ="bold", color = "black", size = 3)

2017-

Code

net_brut_mensuel <- read_parquet(here::here("data", "insee", "INDICE-TRAITEMENT-FP-net-brut-mensuel.parquet"))
indicefp |>
  select(date, point_indice_en_euros) |>
  arrange(desc(date)) |>
  gather(variable, value, -date) |>
  group_by(variable) |>
  complete(date = seq.Date(min(date), max(date), by = "day")) |>
  fill(value) |>
  ungroup() |>
  left_join(cpi2_m, by = "date") |>
  filter(day(date) == 1) |>
  left_join(net_brut_mensuel, by = "date") |>
  filter(date >= as.Date("2017-01-01")) |>
  transmute(date,
            `Valeur du Point d'Indice de la Fonction Publique vs. 2017 (IPCH, Eurostat)` = (value/value[1])*(cpih[1]/cpih)*(net_brut/net_brut[1]),
            `Valeur du Point d'Indice de la Fonction Publique vs. 2017 (IPC, INSEE)` = (value/value[1])*(cpi[1]/cpi)*(net_brut/net_brut[1])) |>
  gather(variable, OBS_VALUE, -date) |>
  ggplot() + geom_line(aes(x = date, y = OBS_VALUE, color = variable)) + theme_minimal() +
  scale_x_date(breaks = c(seq(1999, 2100, 1), seq(1997, 2100, 5)) |> paste0("-01-01") |> as.Date(),
               labels = date_format("%Y")) +
  theme(legend.position = c(0.43, 0.12),
        legend.title = element_blank()) +
  scale_y_log10(breaks = seq(1, 0.02, -0.02),
                labels = percent(seq(1, 0.02, -0.02)-1, acc = 1)) + 
  ylab("Valeur du Point Indice Net vs. 2017") + xlab("") +
  geom_text(data = . %>% filter(date %in% c(max(date), as.Date("2022-01-01"))),
            aes(x = date, y = OBS_VALUE, label = percent(OBS_VALUE-1, acc = 0.1)), 
                  fontface ="bold", color = "black", size = 3)

1997T1-

Code

net_brut_mensuel <- read_parquet(here::here("data", "insee", "INDICE-TRAITEMENT-FP-net-brut-mensuel.parquet"))

colors_fp <- c(
  "Pouvoir d'achat du Point d'Indice Net vs. 1996 (IPC, INSEE)"     = "#E07B6A",
  "Pouvoir d'achat du Point d'Indice Net vs. 1996 (IPCH, Eurostat)" = "#4ABFB0"
)

indicefp |>
  select(date, point_indice_en_euros) |>
  arrange(desc(date)) |>
  gather(variable, value, -date) |>
  group_by(variable) |>
  complete(date = seq.Date(min(date), max(date), by = "day")) |>
  fill(value) |>
  ungroup() |>
  left_join(cpi2_m, by = "date") |>
  filter(day(date) == 1) |>
  left_join(net_brut_mensuel, by = "date") |>
  filter(date >= as.Date("1996-02-01")) |>
  transmute(
    date,
    `Pouvoir d'achat du Point d'Indice Net vs. 1996 (IPCH, Eurostat)` = (value/value[1])*(cpih[1]/cpih)*(net_brut/net_brut[1]),
    `Pouvoir d'achat du Point d'Indice Net vs. 1996 (IPC, INSEE)`     = (value/value[1])*(cpi[1]/cpi)*(net_brut/net_brut[1])
  ) |>
  gather(variable, OBS_VALUE, -date) |>
  ggplot(aes(x = date, y = OBS_VALUE, color = variable)) +
  
  # Shaded area under each line
#geom_area(aes(fill = variable), alpha = 0.08, position = "identity") +
  geom_line(linewidth = 0.8) +
  
  # Horizontal reference line at 0%
  geom_hline(yintercept = 1, linetype = "dashed", color = "grey40", linewidth = 0.4) +
  
  # End labels
  geom_label(
    data        = . %>% group_by(variable) %>% filter(date == max(date)),
    aes(label   = percent(OBS_VALUE - 1, accuracy = 0.1)),
    fontface    = "bold",
    size        = 3.8,
    label.r     = unit(0.25, "lines"),
    label.padding = unit(0.3, "lines"),
    show.legend = FALSE
  ) +
  
  scale_color_manual(values = colors_fp) +
  scale_fill_manual(values  = colors_fp) +
  
  scale_x_date(
    breaks = c(seq(1997, 2100, 5)) |> paste0("-01-01") |> as.Date(),
    labels = date_format("%Y"),
    expand = expansion(mult = c(0.01, 0.06))   # breathing room on the right for labels
  ) +
  scale_y_continuous(
    breaks = seq(1, 0.50, -0.02),
    labels = percent(seq(1, 0.50, -0.02) - 1, accuracy = 1),
    expand = expansion(mult = c(0.02, 0.02))
  ) +
  
  labs(
    title    = "Pouvoir d'achat du point d'indice de la fonction publique vs. 1996",
    subtitle = "Valeur du point d'indice déflatée par l'IPC (préféré par l'Insee) et l'IPCH (préféré par Eurostat)",
    x = NULL, y = NULL,
    caption  = ""
  ) +
  
  theme_minimal(base_family = "sans") +
  theme(
    plot.title       = element_text(face = "bold", size = 13, margin = margin(b = 4)),
    plot.subtitle    = element_text(size = 10, color = "grey40", margin = margin(b = 10)),
    plot.caption     = element_text(size = 8,  color = "grey55", hjust = 0),
    legend.position  = c(0.4, 0.12),
    legend.title     = element_blank(),
    legend.text      = element_text(size = 9),
    legend.background = element_rect(fill = alpha("white", 0.7), color = NA),
    panel.grid.minor = element_blank(),
    panel.grid.major = element_line(color = "grey92"),
    axis.text        = element_text(size = 9, color = "grey40"),
    plot.margin      = margin(10, 15, 10, 10)
  )

2017T1-

Code

net_brut_mensuel <- read_parquet(here::here("data", "insee", "INDICE-TRAITEMENT-FP-net-brut-mensuel.parquet"))

colors_fp <- c(
  "Pouvoir d'achat du Point d'Indice Net vs. 2017 (IPC, INSEE)"     = "#E07B6A",
  "Pouvoir d'achat du Point d'Indice Net vs. 2017 (IPCH, Eurostat)" = "#4ABFB0"
)

indicefp |>
  select(date, point_indice_en_euros) |>
  arrange(desc(date)) |>
  gather(variable, value, -date) |>
  group_by(variable) |>
  complete(date = seq.Date(min(date), max(date), by = "day")) |>
  fill(value) |>
  ungroup() |>
  left_join(cpi2_m, by = "date") |>
  filter(day(date) == 1) |>
  left_join(net_brut_mensuel, by = "date") |>
  filter(date >= as.Date("2017-01-01")) |>
  transmute(
    date,
    `Pouvoir d'achat du Point d'Indice Net vs. 2017 (IPCH, Eurostat)` = (value/value[1])*(cpih[1]/cpih)*(net_brut/net_brut[1]),
    `Pouvoir d'achat du Point d'Indice Net vs. 2017 (IPC, INSEE)`     = (value/value[1])*(cpi[1]/cpi)*(net_brut/net_brut[1])
  ) |>
  gather(variable, OBS_VALUE, -date) |>
  ggplot(aes(x = date, y = OBS_VALUE, color = variable)) +
  
  # Shaded area under each line
#geom_area(aes(fill = variable), alpha = 0.08, position = "identity") +
  geom_line(linewidth = 0.8) +
  
  # Horizontal reference line at 0%
  geom_hline(yintercept = 1, linetype = "dashed", color = "grey40", linewidth = 0.4) +
  
  # End labels
  geom_label(
    data        = . %>% group_by(variable) %>% filter(date == max(date)),
    aes(label   = percent(OBS_VALUE - 1, accuracy = 0.1)),
    fontface    = "bold",
    size        = 3.8,
    label.r     = unit(0.25, "lines"),
    label.padding = unit(0.3, "lines"),
    show.legend = FALSE
  ) +
  
  scale_color_manual(values = colors_fp) +
  scale_fill_manual(values  = colors_fp) +
  
  scale_x_date(
    breaks = c(seq(1999, 2100, 1), seq(1997, 2100, 5)) |> paste0("-01-01") |> as.Date(),
    labels = date_format("%Y"),
    expand = expansion(mult = c(0.01, 0.06))   # breathing room on the right for labels
  ) +
  scale_y_continuous(
    breaks = seq(1, 0.80, -0.02),
    labels = percent(seq(1, 0.80, -0.02) - 1, accuracy = 1),
    expand = expansion(mult = c(0.02, 0.02))
  ) +
  
  labs(
    title    = "Pouvoir d'achat du point d'indice de la fonction publique vs. 2017",
    subtitle = "Valeur du point d'indice déflatée par l'IPC (préféré par l'Insee) et l'IPCH (préféré par Eurostat)",
    x = NULL, y = NULL,
    caption  = ""
  ) +
  
  theme_minimal(base_family = "sans") +
  theme(
    plot.title       = element_text(face = "bold", size = 13, margin = margin(b = 4)),
    plot.subtitle    = element_text(size = 10, color = "grey40", margin = margin(b = 10)),
    plot.caption     = element_text(size = 8,  color = "grey55", hjust = 0),
    legend.position  = c(0.33, 0.12),
    legend.title     = element_blank(),
    legend.text      = element_text(size = 9),
    legend.background = element_rect(fill = alpha("white", 0.7), color = NA),
    panel.grid.minor = element_blank(),
    panel.grid.major = element_line(color = "grey92"),
    axis.text        = element_text(size = 9, color = "grey40"),
    plot.margin      = margin(10, 15, 10, 10)
  )

2017T2-

Code

net_brut_mensuel <- read_parquet(here::here("data", "insee", "INDICE-TRAITEMENT-FP-net-brut-mensuel.parquet"))

colors_fp <- c(
  "Pouvoir d'achat du Point d'Indice Net vs. 2017 (IPC, INSEE)"     = "#E07B6A",
  "Pouvoir d'achat du Point d'Indice Net vs. 2017 (IPCH, Eurostat)" = "#4ABFB0"
)

indicefp |>
  select(date, point_indice_en_euros) |>
  arrange(desc(date)) |>
  gather(variable, value, -date) |>
  group_by(variable) |>
  complete(date = seq.Date(min(date), max(date), by = "day")) |>
  fill(value) |>
  ungroup() |>
  left_join(cpi2_m, by = "date") |>
  filter(day(date) == 1) |>
  left_join(net_brut_mensuel, by = "date") |>
  filter(date >= as.Date("2017-06-01")) |>
  transmute(
    date,
    `Pouvoir d'achat du Point d'Indice Net vs. 2017 (IPCH, Eurostat)` = (value/value[1])*(cpih[1]/cpih)*(net_brut/net_brut[1]),
    `Pouvoir d'achat du Point d'Indice Net vs. 2017 (IPC, INSEE)`     = (value/value[1])*(cpi[1]/cpi)*(net_brut/net_brut[1])
  ) |>
  gather(variable, OBS_VALUE, -date) |>
  ggplot(aes(x = date, y = OBS_VALUE, color = variable)) +
  
  # Shaded area under each line
#geom_area(aes(fill = variable), alpha = 0.08, position = "identity") +
  geom_line(linewidth = 0.8) +
  
  # Horizontal reference line at 0%
  geom_hline(yintercept = 1, linetype = "dashed", color = "grey40", linewidth = 0.4) +
  
  # End labels
  geom_label(
    data        = . %>% group_by(variable) %>% filter(date == max(date)),
    aes(label   = percent(OBS_VALUE - 1, accuracy = 0.1)),
    fontface    = "bold",
    size        = 3.8,
    label.r     = unit(0.25, "lines"),
    label.padding = unit(0.3, "lines"),
    show.legend = FALSE
  ) +
  
  scale_color_manual(values = colors_fp) +
  scale_fill_manual(values  = colors_fp) +
  
  scale_x_date(
    breaks = c(seq(1999, 2100, 1), seq(1997, 2100, 5)) |> paste0("-01-01") |> as.Date(),
    labels = date_format("%Y"),
    expand = expansion(mult = c(0.01, 0.06))   # breathing room on the right for labels
  ) +
  scale_y_continuous(
    breaks = seq(1, 0.80, -0.02),
    labels = percent(seq(1, 0.80, -0.02) - 1, accuracy = 1),
    expand = expansion(mult = c(0.02, 0.02))
  ) +
  
  labs(
    title    = "Pouvoir d'achat du point d'indice de la fonction publique par rapport à 2017",
    subtitle = "Valeur du point d'indice déflatée par l'IPC (préféré par l'Insee) et l'IPCH (préféré par Eurostat)",
    x = NULL, y = NULL,
    caption  = ""
  ) +
  
  theme_minimal(base_family = "sans") +
  theme(
    plot.title       = element_text(face = "bold", size = 13, margin = margin(b = 4)),
    plot.subtitle    = element_text(size = 10, color = "grey40", margin = margin(b = 10)),
    plot.caption     = element_text(size = 8,  color = "grey55", hjust = 0),
    legend.position  = c(0.3, 0.12),
    legend.title     = element_blank(),
    legend.text      = element_text(size = 9),
    legend.background = element_rect(fill = alpha("white", 0.7), color = NA),
    panel.grid.minor = element_blank(),
    panel.grid.major = element_line(color = "grey92"),
    axis.text        = element_text(size = 9, color = "grey40"),
    plot.margin      = margin(10, 15, 10, 10)
  )

2010-2018

Annuel

Code

indicefp |>
  select(date, point_indice_en_euros) |>
  
  arrange(desc(date)) |>
  gather(variable, value, -date) |>
  group_by(variable) |>
  complete(date = seq.Date(min(date), max(date), by = "day")) |>
  fill(value) |>
  filter(month(date) == 1,
         day(date) == 1) |>
  left_join(cpi, by = "date") |>
  filter(date >= as.Date("2010-01-01"),
         date <= as.Date("2018-01-01")) |>
  mutate(value = value/cpi,
         value = 100*value/value[1]) |>
  ggplot() + geom_line(aes(x = date, y = value)) + theme_minimal() +
  scale_x_date(breaks = seq(1920, 2100, 1) |> paste0("-01-01") |> as.Date(),
               labels = date_format("%Y")) +
  scale_y_log10(breaks = seq(0, 200, 1)) + 
  ylab("Point Indice Fonction Publique (euros constants)") + xlab("")

Related Data

Données sur le pouvoir d’achat

source	dataset	Title	Updated
insee	CNA-2014-RDB	Revenu et pouvoir d’achat des ménages	2026-08-16
insee	CNT-2014-CSI	Comptes de secteurs institutionnels	2026-08-16
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 %)	2026-08-02
insee	T_7401	7.401 – Compte des ménages (S14) (En milliards d'euros)	2026-08-02
insee	conso-eff-fonction	Consommation effective des ménages par fonction	2026-08-02
insee	econ-gen-revenu-dispo-pouv-achat-2	Revenu disponible brut et pouvoir d’achat - Données annuelles	2026-08-02
insee	reve-conso-evo-dep-pa	Évolution de la dépense et du pouvoir d’achat des ménages - Données annuelles de 1960 à 2023	2026-08-02
insee	reve-niv-vie-individu-activite	Niveau de vie selon l'activité - Données annuelles	2026-08-02
insee	reve-niv-vie-pouv-achat-trim	Évolution du revenu disponible brut et du pouvoir d’achat - Données trimestrielles	2026-08-02
insee	t_men_val	Revenu, pouvoir d'achat et comptes des ménages - Valeurs aux prix courants	2026-08-02
insee	t_pouvachat_val	Pouvoir d'achat et ratios des comptes des ménages	2026-08-02
insee	t_recapAgent_val	Récapitulatif des séries des comptes d'agents	2026-08-02
insee	t_salaire_val	Salaire moyen par tête - SMPT (données CVS)	2026-08-02
oecd	HH_DASH	Household Dashboard	2026-08-02