7.401 – Compte des ménages (S14) (En milliards d’euros)

Data - INSEE

Author

François Geerolf

Info

source dataset .html .RData
insee T_7401 2024-12-20 2024-10-18

Données sur le pouvoir d’achat

source dataset .html .RData
insee CNA-2014-RDB 2024-12-22 2024-12-22
insee CNT-2014-CSI 2024-12-22 2024-12-22
insee conso-eff-fonction 2024-12-22 2022-06-14
insee econ-gen-revenu-dispo-pouv-achat-2 2024-12-22 2024-07-05
insee reve-conso-evo-dep-pa 2024-12-22 2024-12-11
insee reve-niv-vie-individu-activite 2024-12-22 NA
insee reve-niv-vie-pouv-achat-trim 2024-12-22 2024-12-11
insee T_7401 2024-12-20 2024-10-18
insee t_men_val 2024-12-22 2024-12-21
insee t_pouvachat_val 2024-12-22 2024-12-21
insee t_recapAgent_val 2024-12-21 2024-12-21
insee t_salaire_val 2024-12-21 2024-12-21
oecd HH_DASH 2024-09-15 2023-09-09

Bibliographie

Français

  • “Mesurer « le » pouvoir d’achat”, F. Geerolf, Document de travail, Juillet 2024. [html] [pdf]

  • “Inflation en France : IPC ou IPCH ?”, F. Geerolf, Document de travail, Juillet 2024. [html] [pdf]

  • “La taxe inflationniste, le pouvoir d’achat, le taux d’épargne et le déficit public”, F. Geerolf, Document de travail, Juillet 2024. [html] [pdf]

LAST_COMPILE

LAST_COMPILE
2024-12-22

Last

Code 
T_7401 %>%
  group_by(year) %>%
  summarise(Nobs = n()) %>%
  arrange(desc(year)) %>%
  head(1) %>%
  print_table_conditional()
year Nobs
2023 101

Exemple

Début

Code 
ig_b("insee", "t_7401_1")

Milieu

Code 
ig_b("insee", "t_7401_2")

Fin

Code 
ig_b("insee", "t_7401_3")

All

Code 
ig_b("insee", "T_7401_bind")

Sources

7.401 – Compte des ménages (S14) (En milliards d’euros):

  • Comptes de la Nation 2022. html / xlsx

  • Comptes de la Nation 2020. html / xlsx

  • Comptes de la Nation 2019. html

Données reliées

  • 2.101 – Revenu disponible brut des ménages et évolution du pouvoir d’achat par personne, par ménage et par unité de consommation (En milliards d’euros et %) - T_2101. html
  • 2.104 – Compte des ménages simplifié et ratios d’épargne (En milliards d’euros et %) - t_2104. html
  • 2.104 – Compte des ménages simplifié et ratios d’épargne (En milliards d’euros et %) - t_2104_2018. html
  • 7.401 – Compte des ménages (S14) (En milliards d’euros) - T_7401. html
  • Comptes des secteurs institutionnels - CNA-2014-CSI. html

Concepts

Revenu primaire au RDB des ménages

2021

Code 
ig_b("insee", "FPORSOC22", "F29", "table2")

2020

Code 
ig_b("insee", "FPS2021", "revenu-primaire-RDB")

Composition du RDB des ménages

Code 
ig_b("insee", "FPS2021", "EC4", "tab1")

Prestations de protection sociale en 2018

Code 
ig_b("insee", "TEF2020", "057", "prestations-sociales")

line, Line

Code 
T_7401 %>%
  group_by(line, variable, Variable) %>%
  summarise(Nobs = n()) %>%
  print_table_conditional()

gdp

Code 
gdp %>%
  print_table_conditional()

Table in 2000, 2005, 2010, 2015, 2020, 2023

Milliards

Code 
T_7401 %>%
  filter(year %in% c(paste0(seq(1980, 2100, 20)), 2023)) %>%
  spread(year, value) %>%
  arrange(line) %>%
  mutate_at(vars(-line, -variable, -Variable), funs(round(.))) %>%
  print_table_conditional()

%

Code 
T_7401 %>%
  left_join(gdp, by = "year") %>%
  mutate(value_gdp = round(100 * value / gdp, 1)) %>%
  filter(year %in% c(paste0(seq(1980, 2100, 20)), 2023)) %>%
  select(-value, -gdp) %>%
  spread(year, value_gdp) %>%
  arrange(line) %>%
  print_table_conditional()

D5 (line 52), D4 (line 30), B3g (line 22)

Tous

Code 
T_7401 %>%
  filter(line %in% c(52, 30, 22)) %>%
  left_join(gdp, by = "year") %>%
  year_to_date2 %>%
  ggplot + geom_line(aes(x = date, y = value / gdp, color = Variable)) +
  theme_minimal() + xlab("") + ylab("% du PIB") +
  scale_x_date(breaks = as.Date(paste0(seq(1940, 2100, 5), "-01-01")),
               labels = date_format("%Y")) +
  scale_y_continuous(breaks = 0.01*seq(-10, 100, 2),
                     labels = percent_format(accuracy = 1)) +
  
  theme(legend.position = c(0.6, 0.9),
        legend.title = element_blank())

1995-

Code 
T_7401 %>%
  filter(line %in% c(52, 30, 22)) %>%
  left_join(gdp, by = "year") %>%
  year_to_date2 %>%
  filter(date >= as.Date("1995-01-01")) %>%
  ggplot + geom_line(aes(x = date, y = value / gdp, color = Variable)) +
  theme_minimal() + xlab("") + ylab("% du PIB") +
  scale_x_date(breaks = seq(1920, 2100, 2) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  scale_y_continuous(breaks = 0.01*seq(-10, 100, 1),
                     labels = percent_format(accuracy = 1)) +
  
  theme(legend.position = c(0.3, 0.9),
        legend.title = element_blank())

Revenu Disponible Brut (RDB), Ajusté (RDBA)

Code 
T_7401 %>%
  filter(variable %in% c("B6G", "B7G")) %>%
  left_join(gdp, by = "year") %>%
  year_to_date2 %>%
  ggplot + geom_line(aes(x = date, y = value / gdp, color = Variable)) +
  theme_minimal() + xlab("") + ylab("% du PIB") +
  scale_x_date(breaks = as.Date(paste0(seq(1940, 2100, 5), "-01-01")),
               labels = date_format("%Y")) +
  scale_y_continuous(breaks = 0.01*seq(-10, 100, 2),
                     labels = percent_format(accuracy = 1)) +
  
  theme(legend.position = c(0.2, 0.15),
        legend.title = element_blank())

Revenu Disponible brut, Solde des revenus primaires bruts

Code 
T_7401 %>%
  filter(variable %in% c("B6G", "B5G", "B7G")) %>%
  left_join(gdp, by = "year") %>%
  year_to_date2 %>%
  ggplot + geom_line(aes(x = date, y = value / gdp, color = Variable)) +
  theme_minimal() + xlab("") + ylab("% du PIB") +
  scale_x_date(breaks = as.Date(paste0(seq(1940, 2100, 5), "-01-01")),
               labels = date_format("%Y")) +
  scale_y_continuous(breaks = 0.01*seq(-10, 100, 2),
                     labels = percent_format(accuracy = 1)) +
  
  theme(legend.position = c(0.2, 0.15),
        legend.title = element_blank())

Consommation finale effective, Dépense de consommation individuelle

Code 
T_7401 %>%
  filter(variable %in% c("P31", "P4")) %>%
  left_join(gdp, by = "year") %>%
  year_to_date2 %>%
  ggplot + geom_line(aes(x = date, y = value / gdp, color = Variable)) +
  theme_minimal() + xlab("") + ylab("% du PIB") +
  scale_x_date(breaks = seq(1920, 2100, 5) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  scale_y_continuous(breaks = 0.01*seq(-10, 100, 2),
                     labels = percent_format(accuracy = 1)) +
  
  theme(legend.position = c(0.7, 0.6),
        legend.title = element_blank())

Dividendes

% du PIB

Code 
T_7401 %>%
  filter(variable %in% c("D42")) %>%
  left_join(gdp, by = "year") %>%
  year_to_date2 %>%
  ggplot + geom_line(aes(x = date, y = value / gdp)) +
  theme_minimal() + xlab("") + ylab("Revenus Distributés des Sociétés - Dividendes (% du PIB)") +
  scale_x_date(breaks =seq.Date(from = as.Date("1947-01-01"), to = Sys.Date(), by = "5 years"),
               labels = date_format("%Y")) +
  scale_y_continuous(breaks = 0.01*seq(-10, 100, .1),
                     labels = percent_format(accuracy = .1)) +
  theme(legend.position = c(0.2, 0.9),
        legend.title = element_blank())

Milliards

Linéaire

Code 
T_7401 %>%
  filter(variable %in% c("D42")) %>%
  left_join(gdp, by = "year") %>%
  year_to_date2 %>%
  ggplot + geom_line(aes(x = date, y = value)) +
  theme_minimal() + xlab("") + ylab("Revenus Distributés des Sociétés - Dividendes (Milliards)") +
  scale_x_date(breaks =seq.Date(from = as.Date("1947-01-01"), to = Sys.Date(), by = "5 years"),
               labels = date_format("%Y")) +
  scale_y_continuous(breaks = seq(-10, 100, 10)) +
  theme(legend.position = c(0.2, 0.9),
        legend.title = element_blank())

Log

Code 
T_7401 %>%
  filter(variable %in% c("D42")) %>%
  left_join(gdp, by = "year") %>%
  year_to_date2 %>%
  ggplot + geom_line(aes(x = date, y = value)) +
  theme_minimal() + xlab("") + ylab("Revenus Distributés des Sociétés - Dividendes (Milliards)") +
  scale_x_date(breaks =seq.Date(from = as.Date("1947-01-01"), to = Sys.Date(), by = "5 years"),
               labels = date_format("%Y")) +
  scale_y_log10(breaks = c(c(1, 2, 3, 5, 8), seq(-10, 100, 10))) +
  theme(legend.position = c(0.2, 0.9),
        legend.title = element_blank())

Revenus de la propriété

All

Code 
T_7401 %>%
  filter(variable %in% c("D4", "D42", "D44"),
         line %in% c(30, 32, 35)) %>%
  left_join(gdp, by = "year") %>%
  year_to_date2 %>%
  ggplot + geom_line(aes(x = date, y = value / gdp, color = Variable)) +
  theme_minimal() + xlab("") + ylab("% du PIB") +
  scale_x_date(breaks ="5 years",
               labels = date_format("%Y")) +
  scale_y_continuous(breaks = 0.01*seq(-10, 100, 1),
                     labels = percent_format(accuracy = 1)) +
  
  theme(legend.position = c(0.2, 0.9),
        legend.title = element_blank())

1995-

Code 
T_7401 %>%
  filter(variable %in% c("D4", "D42", "D44"),
         line %in% c(30, 32, 35)) %>%
  left_join(gdp, by = "year") %>%
  year_to_date2 %>%
  filter(date >= as.Date("1995-01-01")) %>%
  ggplot + geom_line(aes(x = date, y = value / gdp, color = Variable)) +
  theme_minimal() + xlab("") + ylab("% du PIB") +
  scale_x_date(breaks = as.Date(paste0(seq(1940, 2100, 2), "-01-01")),
               labels = date_format("%Y")) +
  scale_y_continuous(breaks = 0.01*seq(-10, 100, 1),
                     labels = percent_format(accuracy = 1)) +
  
  theme(legend.position = c(0.8, 0.9),
        legend.title = element_blank())

Salaires, Rémunération des salariés

Montant

All

Linear

Code 
T_7401 %>%
  filter(line %in% c(18, 26, 42)) %>%
  bind_rows(T_2101 %>%
              filter(line == 3) %>%
              mutate(Variable = "Salaires et traitements nets")) %>%
  left_join(gdp, by = "year") %>%
  year_to_date2 %>%
  ggplot + geom_line(aes(x = date, y = value, color = Variable)) +
  theme_minimal() + xlab("") + ylab("Milliards d'€") +
  scale_x_date(breaks = as.Date(paste0(seq(1940, 2100, 5), "-01-01")),
               labels = date_format("%Y")) +
  scale_y_continuous(breaks = seq(0, 10000, 100),
                     labels = dollar_format(accuracy = 1, pre = "", su ="Mds€")) +
  theme(legend.position = c(0.3, 0.9),
        legend.title = element_blank())

Log

Code 
T_7401 %>%
  filter(line %in% c(18, 26, 42)) %>%
  bind_rows(T_2101 %>%
              filter(line == 3) %>%
              mutate(Variable = "Salaires et traitements nets")) %>%
  left_join(gdp, by = "year") %>%
  year_to_date2 %>%
  ggplot + geom_line(aes(x = date, y = value, color = Variable)) +
  theme_minimal() + xlab("") + ylab("Milliards d'€") +
  scale_x_date(breaks = as.Date(paste0(seq(1940, 2100, 5), "-01-01")),
               labels = date_format("%Y")) +
  scale_y_log10(breaks = c(1,2,5,8,10,20, 50, 80, 100, 200, 500, 1000, 1200, 2000, 3000, 5000, 10000),
                     labels = dollar_format(accuracy = 1, pre = "", su ="Mds€")) +
  theme(legend.position = c(0.3, 0.9),
        legend.title = element_blank())

1995-

Linear

Code 
T_7401 %>%
  filter(line %in% c(18, 26, 42)) %>%
  bind_rows(T_2101 %>%
              filter(line == 3) %>%
              mutate(Variable = "Salaires et traitements nets")) %>%
  left_join(gdp, by = "year") %>%
  year_to_date2 %>%
  filter(date >= as.Date("1995-01-01")) %>%
  ggplot + geom_line(aes(x = date, y = value, color = Variable)) +
  theme_minimal() + xlab("") + ylab("Milliards d'€") +
  scale_x_date(breaks = as.Date(paste0(seq(1940, 2100, 2), "-01-01")),
               labels = date_format("%Y")) +
  scale_y_continuous(breaks = seq(0, 10000, 100),
                     labels = dollar_format(accuracy = 1, pre = "", su ="Mds€")) +
  theme(legend.position = c(0.3, 0.9),
        legend.title = element_blank())

Log

Code 
T_7401 %>%
  filter(line %in% c(18, 26)) %>%
  bind_rows(T_2101 %>%
              filter(line == 3) %>%
              mutate(Variable = "Salaires et traitements nets")) %>%
  left_join(gdp, by = "year") %>%
  year_to_date2 %>%
  filter(date >= as.Date("1995-01-01")) %>%
  ggplot + geom_line(aes(x = date, y = value, color = Variable)) +
  theme_minimal() + xlab("") + ylab("Milliards d'€") +
  scale_x_date(breaks = as.Date(paste0(seq(1940, 2100, 2), "-01-01")),
               labels = date_format("%Y")) +
  scale_y_log10(breaks = seq(0, 1500, 100),
                     labels = dollar_format(accuracy = 1, pre = "", su ="Mds€")) +
  theme(legend.position = c(0.3, 0.9),
        legend.title = element_blank())

% du PIB

All

Code 
T_7401 %>%
  filter(variable %in% c("D1", "D11"),
         line %in% c(25, 26)) %>%
  bind_rows(T_2101 %>%
              filter(line == 3) %>%
              mutate(Variable = "Salaires et traitements nets")) %>%
  left_join(gdp, by = "year") %>%
  year_to_date2 %>%
  ggplot + geom_line(aes(x = date, y = value / gdp, color = Variable)) +
  theme_minimal() + xlab("") + ylab("% du PIB") +
  scale_x_date(breaks = as.Date(paste0(seq(1940, 2100, 5), "-01-01")),
               labels = date_format("%Y")) +
  scale_y_continuous(breaks = 0.01*seq(-10, 100, 2),
                     labels = percent_format(accuracy = 1)) +
  
  theme(legend.position = c(0.2, 0.9),
        legend.title = element_blank())

1995-

Code 
T_7401 %>%
  filter(variable %in% c("D1", "D11"),
         line %in% c(25, 26)) %>%
  bind_rows(T_2101 %>%
              filter(line == 3) %>%
              mutate(Variable = "Salaires et traitements nets")) %>%
  left_join(gdp, by = "year") %>%
  year_to_date2 %>%
  filter(date >= as.Date("1995-01-01")) %>%
  ggplot + geom_line(aes(x = date, y = value / gdp, color = Variable)) +
  theme_minimal() + xlab("") + ylab("% du PIB") +
  scale_x_date(breaks = as.Date(paste0(seq(1940, 2100, 5), "-01-01")),
               labels = date_format("%Y")) +
  scale_y_continuous(breaks = 0.01*seq(-10, 100, 2),
                     labels = percent_format(accuracy = 1)) +
  
  theme(legend.position = c(0.2, 0.7),
        legend.title = element_blank())

% du RDB

All

Code 
T_7401 %>%
  filter(variable %in% c("D1", "D11"),
         line %in% c(25, 26)) %>%
  bind_rows(T_2101 %>%
              filter(line == 3) %>%
              mutate(Variable = "Salaires et traitements nets")) %>%
  left_join(rdb2, by = "year") %>%
  year_to_date2 %>%
  ggplot + geom_line(aes(x = date, y = value / rdb, color = Variable)) +
  theme_minimal() + xlab("") + ylab("% du RDB") +
  scale_x_date(breaks = as.Date(paste0(seq(1940, 2100, 5), "-01-01")),
               labels = date_format("%Y")) +
  scale_y_continuous(breaks = 0.01*seq(-10, 100, 2),
                     labels = percent_format(accuracy = 1)) +
  
  theme(legend.position = c(0.2, 0.9),
        legend.title = element_blank())

1995-

Code 
T_7401 %>%
  filter(variable %in% c("D1", "D11"),
         line %in% c(25, 26)) %>%
  bind_rows(T_2101 %>%
              filter(line == 3) %>%
              mutate(Variable = "Salaires et traitements nets")) %>%
  left_join(rdb2, by = "year") %>%
  year_to_date2 %>%
  filter(date >= as.Date("1995-01-01")) %>%
  ggplot + geom_line(aes(x = date, y = value / rdb, color = Variable)) +
  theme_minimal() + xlab("") + ylab("% du PIB") +
  scale_x_date(breaks = as.Date(paste0(seq(1940, 2100, 2), "-01-01")),
               labels = date_format("%Y")) +
  scale_y_continuous(breaks = 0.01*seq(-10, 100, 2),
                     labels = percent_format(accuracy = 1)) +
  
  theme(legend.position = c(0.2, 0.7),
        legend.title = element_blank())

Dépense de consommation individuelle, revenu disponible brut

Dépense de consommation, RDB

Code 
T_7401 %>%
  filter(variable %in% c("B6G", "P31")) %>%
  left_join(gdp, by = "year") %>%
  year_to_date2 %>%
  ggplot + geom_line(aes(x = date, y = value / gdp, color = Variable)) +
  theme_minimal() + xlab("") + ylab("% du PIB") +
  scale_x_date(breaks = as.Date(paste0(seq(1940, 2100, 5), "-01-01")),
               labels = date_format("%Y")) +
  scale_y_continuous(breaks = 0.01*seq(-10, 100, 2),
                     labels = percent_format(accuracy = 1)) +
  
  theme(legend.position = c(0.7, 0.9),
        legend.title = element_blank())

Avec 0

Code 
T_7401 %>%
  filter(variable %in% c("B6G", "P31")) %>%
  left_join(gdp, by = "year") %>%
  year_to_date2 %>%
  ggplot + geom_line(aes(x = date, y = value / gdp, color = Variable)) +
  theme_minimal() + xlab("") + ylab("% du PIB") +
  scale_x_date(breaks = as.Date(paste0(seq(1940, 2100, 5), "-01-01")),
               labels = date_format("%Y")) +
  scale_y_continuous(breaks = 0.01*seq(-10, 100, 5),
                     labels = percent_format(accuracy = 1),
                     limits = c(0, 0.72)) +
  
  theme(legend.position = c(0.7, 0.3),
        legend.title = element_blank())