Comptes Financiers Trimestriels

Données - BDF

Auteur

François Geerolf

Info

Structure

source dataset Title .html .rData
bdf CFT Comptes Financiers Trimestriels 2026-07-23 2025-03-09
  • Méthodologie. pdf

Last

date Nobs
2024-09-30 4

Liste des publications

  • Épargne et Patrimoine financiers des ménages, 2024T1. pdf / html

  • Comptes financiers des agents non financiers. STAT INFO – 4e trimestre 2023. pdf

  • Épargne des ménages. STAT INFO – février 2024. pdf

Info

  • Épargne et Patrimoine financiers des ménages, 2024T2. pdf html

  • Épargne et Patrimoine financiers des ménages, Comptes financiers des agents non financiers, 15 avril 2024, 2023T4. pdf html

  • Épargne et Patrimoine financiers des ménages, 2023T3. pdf

  • Épargne et Patrimoine financiers des ménages, 2023T2. pdf

  • Présentation trimestrielle de l’épargne des ménages, 2023T1. html pdf

  • Méthodologie. pdf

  • Liste séries. html

  • Épargne et Patrimoine financiers des ménages, 2022T2. pdf

  • Epargne des ménages, 2021T1. pdf

  • Taux d’endettement des agents non financiers – Comparaisons internationales, 2020T4. html / pdf

  • Epargne des ménages, 2020T3. pdf

INSTR_ASSET Instrument and assets classification

Code 
CFT %>%
  
  
  group_by(INSTR_ASSET, Instr_asset) %>%
  summarise(Nobs = n()) %>%
  #arrange(-Nobs) %>%
  print_table_conditional

Grandes masses

2024T2

Code 
ig_b("bdf", "CFT-2024T2")

2023T4

Code 
ig_b("bdf", "CFT-2023T4")

Produits de taux

Code 
CFT %>%
  
  filter(STO == "LE",
         INSTR_ASSET %in% c("PDTX")) %>%
  na.omit %>%
  ggplot + geom_line(aes(x = date, y = value, color = Variable)) + 
  xlab("") + ylab("") + theme_minimal() +
  scale_x_date(breaks = "1 year",
               labels = date_format("%Y")) +
  scale_y_log10(breaks = seq(-200, 10000, 100),
                     labels = dollar_format(acc = 1, prefix = "", su = "Mds€")) +
  theme(legend.position = c(0.35, 0.9),
        legend.title = element_blank(),
        legend.direction = "vertical") +
  geom_label(data = . %>% filter(date == as.Date("2023-12-31")),
             aes(x = date, y = value, color = Variable, label = round(value)))

Produits de fonds propres

Code 
CFT %>%
  
  filter(STO == "LE",
         INSTR_ASSET %in% c("PDFP")) %>%
  na.omit %>%
  ggplot + geom_line(aes(x = date, y = value, color = Variable)) + 
  xlab("") + ylab("") + theme_minimal() +
  scale_x_date(breaks = "1 year",
               labels = date_format("%Y")) +
  scale_y_log10(breaks = seq(-200, 10000, 100),
                     labels = dollar_format(acc = 1, prefix = "", su = "Mds€")) +
  theme(legend.position = c(0.35, 0.9),
        legend.title = element_blank(),
        legend.direction = "vertical") +
  geom_label(data = . %>% filter(date == as.Date("2023-12-31")),
             aes(x = date, y = value, color = Variable, label = round(value)))

Côté, non côté

Code 
CFT %>%
  
  filter(STO == "LE",
         INSTR_ASSET %in% c("PDFP", "F51", "F511", "F51M", "F52")) %>%
  na.omit %>%
  ggplot + geom_line(aes(x = date, y = value, color = Variable)) + 
  xlab("") + ylab("") + theme_minimal() +
  scale_x_date(breaks = "1 year",
               labels = date_format("%Y")) +
  scale_y_log10(breaks = seq(-200, 10000, 100),
                     labels = dollar_format(acc = 1, prefix = "", su = "Mds€")) +
  theme(legend.position = c(0.5, 0.7),
        legend.title = element_blank(),
        legend.direction = "vertical") +
  geom_label(data = . %>% filter(date == as.Date("2023-12-31")),
             aes(x = date, y = value, color = Variable, label = round(value)))

Detail

Linéaire

Code 
CFT %>%
  
  filter(STO == "LE",
         INSTR_ASSET %in% c("F62A", "F62B", "F29R", "F2A", "F29Z")) %>%
  na.omit %>%
  ggplot + geom_line(aes(x = date, y = value, color = Variable)) + 
  xlab("") + ylab("") + theme_minimal() +
  scale_x_date(breaks = "1 year",
               labels = date_format("%Y")) +
  scale_y_continuous(breaks = seq(-200, 10000, 100),
                     labels = dollar_format(acc = 1, prefix = "", su = "Mds€")) +
  theme(legend.position = c(0.45, 0.17),
        legend.title = element_blank(),
        legend.direction = "vertical") +
  geom_label(data = . %>% filter(date == as.Date("2023-12-31")),
             aes(x = date, y = value, color = Variable, label = round(value)))

Log

Code 
CFT %>%
  
  filter(STO == "LE",
         INSTR_ASSET %in% c("F62A", "F62B", "F29R", "F2A", "F29Z")) %>%
  na.omit %>%
  ggplot + geom_line(aes(x = date, y = value, color = Variable)) + 
  xlab("") + ylab("") + theme_minimal() +
  scale_x_date(breaks = "1 year",
               labels = date_format("%Y")) +
  scale_y_log10(breaks = seq(-200, 10000, 100),
                     labels = dollar_format(acc = 1, prefix = "", su = "Mds€")) +
  theme(legend.position = c(0.45, 0.17),
        legend.title = element_blank(),
        legend.direction = "vertical") +
  geom_label(data = . %>% filter(date == as.Date("2023-12-31")),
             aes(x = date, y = value, color = Variable, label = round(value)))

Stock

Numéraires et dépôts à vue

Code 
CFT %>%
  
  
  filter(REF_AREA %in% c("FR", "IT", "DE"),
         INSTR_ASSET == "F2A",
         STO == "LE") %>%
  mutate(Ref_area = ifelse(REF_AREA == "I8", "Europe", Ref_area)) %>%
  left_join(colors, by = c("Ref_area" = "country")) %>%
  na.omit %>%
  ggplot + geom_line(aes(x = date, y = value, color = color)) + 
  xlab("") + ylab("") + theme_minimal() + scale_color_identity() + add_3flags +
  scale_x_date(breaks = "1 year",
               labels = date_format("%Y")) +
  scale_y_log10(breaks = seq(100, 4000, 100),
                labels = dollar_format(accuracy = 1, pre = "", su = " Mds€")) +
  geom_label(data = . %>% filter(date == as.Date("2023-12-31")),
             aes(x = date, y = value, label = round(value)))

Stocks

Code 
CFT %>%
  
  
  filter(REF_AREA %in% c("FR"),
         INSTR_ASSET %in% c("F2A", "F29Z", "F62B", "F29R"),
         STO == "LE") %>%
  select(date, value, Instr_asset) %>%
  na.omit %>%
  ggplot + geom_line(aes(x = date, y = value, color = Instr_asset)) + 
  xlab("") + ylab("") + theme_minimal() +
  
  scale_x_date(breaks = "1 year",
               labels = date_format("%Y")) +
  scale_y_log10(breaks = seq(100, 4000, 100),
                labels = dollar_format(accuracy = 1, pre = "", su = " Mds€")) +
  theme(legend.position = c(0.4, 0.4),
        legend.title = element_blank(),
        legend.direction = "vertical") +
  geom_label(data = . %>% filter(date == as.Date("2023-12-31")),
             aes(x = date, y = value, color = Instr_asset, label = round(value)))

Numéraires et dépôts à vue

Stocks

All

Code 
CFT %>%
  
  
  filter(REF_AREA %in% c("FR"),
         INSTR_ASSET %in% c("F2A", "F29Z", "F62B"),
         STO == "LE") %>%
  select(date, value, Instr_asset) %>%
  na.omit %>%
  ggplot + geom_line(aes(x = date, y = value, color = Instr_asset)) + 
  xlab("") + ylab("") + theme_minimal() +
  
  scale_x_date(breaks = "1 year",
               labels = date_format("%Y")) +
  scale_y_log10(breaks = seq(100, 4000, 100),
                labels = dollar_format(accuracy = 1, pre = "", su = " Mds€")) +
  theme(legend.position = c(0.4, 0.4),
        legend.title = element_blank(),
        legend.direction = "vertical") +
  geom_label(data = . %>% filter(date == as.Date("2023-12-31")),
             aes(x = date, y = value, color = Instr_asset, label = round(value)))

2019-

Code 
CFT %>%
  
  
  filter(REF_AREA %in% c("FR"),
         INSTR_ASSET %in% c("F2A", "F29Z", "F62B"),
         STO == "LE") %>%
  select(date, value, Instr_asset) %>%
  na.omit %>%
  filter(date >= as.Date("2022-01-01")) %>%
  ggplot + geom_line(aes(x = date, y = value, color = Instr_asset)) + 
  xlab("") + ylab("") + theme_minimal() +
  
  scale_x_date(breaks = "3 months",
               labels = date_format("%b %Y")) +
  scale_y_log10(breaks = seq(100, 4000, 100),
                labels = dollar_format(accuracy = 1, pre = "", su = " Mds€")) +
  theme(legend.position = c(0.4, 0.4),
        legend.title = element_blank(),
        legend.direction = "vertical") +
  geom_label(data = . %>% filter(date == as.Date("2023-12-31")),
             aes(x = date, y = value, color = Instr_asset, label = round(value)))

Flux - 4 trimestres

Code 
CFT %>%
  
  
  filter(REF_AREA %in% c("FR"),
         INSTR_ASSET %in% c("F2A", "F29Z", "F62B", "F29R"),
         STO == "F",
         TRANSFORMATION == "C4") %>%
  #filter(date >= as.Date("2016-01-01")) %>%
  select(date, value, Instr_asset) %>%
  na.omit %>%
  ggplot + geom_line(aes(x = date, y = value, color = Instr_asset)) + 
  xlab("") + ylab("") + theme_minimal() +
  
  scale_x_date(breaks = "1 year",
               labels = date_format("%Y")) +
  scale_y_continuous(breaks = seq(-2000, 4000, 10),
                labels = dollar_format(accuracy = 1, pre = "", su = " Mds€")) +
  theme(legend.position = c(0.4, 0.8),
        legend.title = element_blank(),
        legend.direction = "vertical")

Flux

All

Code 
CFT %>%
  
  
  filter(REF_AREA %in% c("FR"),
         INSTR_ASSET %in% c("F2A", "F29Z", "F62B", "F29R"),
         STO == "F",
         TRANSFORMATION == "N",
         FREQ == "Q") %>%
  filter(date >= as.Date("2010-01-01")) %>%
  ggplot + geom_line(aes(x = date, y = value, color = Instr_asset)) + 
  xlab("") + ylab("") + theme_minimal() +
  
  scale_x_date(breaks = "1 year",
               labels = date_format("%Y")) +
  scale_y_continuous(breaks = seq(-1000, 4000, 10),
                labels = dollar_format(accuracy = 1, pre = "", su = " Mds€")) +
  theme(legend.position = c(0.4, 0.8),
        legend.title = element_blank(),
        legend.direction = "vertical")

2016

Code 
CFT %>%
  
  
  filter(REF_AREA %in% c("FR"),
         INSTR_ASSET %in% c("F2A", "F29Z", "F62B", "F29R"),
         STO == "F",
         TRANSFORMATION == "N",
         FREQ == "Q") %>%
  filter(date >= as.Date("2016-01-01")) %>%
  ggplot + geom_line(aes(x = date, y = value, color = Instr_asset)) + 
  xlab("") + ylab("") + theme_minimal() +
  
  scale_x_date(breaks = "1 year",
               labels = date_format("%Y")) +
  scale_y_continuous(breaks = seq(-1000, 4000, 10),
                labels = dollar_format(accuracy = 1, pre = "", su = " Mds€")) +
  theme(legend.position = c(0.4, 0.9),
        legend.title = element_blank(),
        legend.direction = "vertical") +
  geom_hline(yintercept = 0, linetype = "dashed")

Actions côtées / non côtées, AV en unités de compte

Stocks

Code 
CFT %>%
  
  
  filter(REF_AREA %in% c("FR"),
         INSTR_ASSET %in% c( "F511", "F51M", "F51"),
         STO == "LE") %>%
  na.omit %>%
  ggplot + geom_line(aes(x = date, y = value, color = Instr_asset)) + 
  xlab("") + ylab("") + theme_minimal() +
  
  scale_x_date(breaks = "1 year",
               labels = date_format("%Y")) +
  scale_y_log10(breaks = seq(100, 4000, 100),
                labels = dollar_format(accuracy = 1, pre = "", su = " Mds€")) +
  theme(legend.position = c(0.4, 0.6),
        legend.title = element_blank(),
        legend.direction = "vertical") +
  geom_label(data = . %>% filter(date == as.Date("2023-12-31")),
             aes(x = date, y = value, color = Instr_asset, label = round(value)))

Actions côtées / non côtées, AV en unités de compte

Stocks

Code 
CFT %>%
  
  
  filter(REF_AREA %in% c("FR"),
         INSTR_ASSET %in% c( "F511", "F51M", "F62A"),
         STO == "LE") %>%
  na.omit %>%
  ggplot + geom_line(aes(x = date, y = value, color = Instr_asset)) + 
  xlab("") + ylab("") + theme_minimal() +
  
  scale_x_date(breaks = "1 year",
               labels = date_format("%Y")) +
  scale_y_log10(breaks = seq(100, 4000, 100),
                labels = dollar_format(accuracy = 1, pre = "", su = " Mds€")) +
  theme(legend.position = c(0.4, 0.6),
        legend.title = element_blank(),
        legend.direction = "vertical") +
  geom_label(data = . %>% filter(date == as.Date("2023-12-31")),
             aes(x = date, y = value, color = Instr_asset, label = round(value)))

Flux

Code 
CFT %>%
  
  
  filter(REF_AREA %in% c("FR"),
         INSTR_ASSET %in% c( "F511", "F51M", "F62A"),
         STO == "F",
         TRANSFORMATION == "N",
         FREQ == "Q") %>%
  filter(date >= as.Date("2016-01-01")) %>%
  ggplot + geom_line(aes(x = date, y = value, color = Instr_asset)) + 
  xlab("") + ylab("") + theme_minimal() +
  
  scale_x_date(breaks = "1 year",
               labels = date_format("%Y")) +
  scale_y_continuous(breaks = seq(-2000, 4000, 5),
                labels = dollar_format(accuracy = 1, pre = "", su = " Mds€")) +
  theme(legend.position = c(0.4, 0.4),
        legend.title = element_blank(),
        legend.direction = "vertical")

Numéraires et dépôts à vue

Code 
CFT %>%
  
  
  filter(REF_AREA %in% c("FR"),
         INSTR_ASSET %in% c("F2A", "F29Z"),
         STO == "LE") %>%
  na.omit %>%
  ggplot + geom_line(aes(x = date, y = value, color = Instr_asset)) + 
  xlab("") + ylab("") + theme_minimal() +
  
  scale_x_date(breaks = "1 year",
               labels = date_format("%Y")) +
  scale_y_log10(breaks = seq(100, 4000, 100),
                labels = dollar_format(accuracy = 1, pre = "", su = " Mds€")) +
  theme(legend.position = c(0.75, 0.1),
        legend.title = element_blank(),
        legend.direction = "vertical") +
  geom_label(data = . %>% filter(date == as.Date("2023-12-31")),
             aes(x = date, y = value, color = Instr_asset, label = round(value)))

Assurance Vie

Toutes

2007-

Code 
CFT %>%
  
  filter(variable %in% c("CFT.Q.S.FR.W0.S1M.S1.N.A.F.F62B._Z._Z.XDC._T.S.V.N._T",
                         "CFT.Q.S.FR.W0.S1M.S1.N.A.F.F62A._Z._Z.XDC._T.S.V.N._T",
                         "CFT.Q.S.FR.W0.S1M.S1.N.A.F.F29R.T._Z.XDC._T.S.V.N._T",
                         "CFT.Q.S.FR.W0.S1M.S1.N.A.F.F2A.T._Z.XDC._T.S.V.N._T")) %>%
  na.omit %>%
  ggplot + geom_line(aes(x = date, y = value, color = Variable)) + 
  xlab("") + ylab("") + theme_minimal() +
  
  scale_x_date(breaks = "1 year",
               labels = date_format("%Y")) +
  scale_y_continuous(breaks = seq(-200, 1000, 5),
                     limits = c(-25 ,40),
                     labels = dollar_format(acc = 1, prefix = "", su = "Mds€")) +
  theme(legend.position = c(0.45, 0.17),
        legend.title = element_blank(),
        legend.direction = "vertical")

2015-

Code 
CFT %>%
  
  filter(variable %in% c("CFT.Q.S.FR.W0.S1M.S1.N.A.F.F62B._Z._Z.XDC._T.S.V.N._T",
                         "CFT.Q.S.FR.W0.S1M.S1.N.A.F.F29Z.T._Z.XDC._T.S.V.N._T",
                         "CFT.Q.S.FR.W0.S1M.S1.N.A.F.F29R.T._Z.XDC._T.S.V.N._T",
                         "CFT.Q.S.FR.W0.S1M.S1.N.A.F.F2A.T._Z.XDC._T.S.V.N._T")) %>%
  filter(date >= as.Date("2015-01-01")) %>%
  ggplot + geom_line(aes(x = date, y = value, color = Variable)) + 
  xlab("") + ylab("") + theme_minimal() +
  
  scale_x_date(breaks = "1 year",
               labels = date_format("%Y")) +
  scale_y_continuous(breaks = seq(-200, 1000, 5),
                     limits = c(-5 ,40),
                     labels = dollar_format(acc = 1, prefix = "", su = "Mds€")) +
  theme(legend.position = c(0.45, 0.8),
        legend.title = element_blank(),
        legend.direction = "vertical")

Taux d’épargne des ménages

Le taux d’épargne des ménages est le rapport entre l’épargne brute des ménages (B8G) et le revenu disponible brut ajusté des variations de droits à pension. Le revenu disponible brut (B6G) correspond aux revenus que perçoivent les ménages (revenus d’activité et revenus fonciers) après opérations de redistribution (ajout des prestations sociales en espèces reçues, soustraction des cotisations et impôts).

Quant au taux d’épargne financière, il s’agit de la part du revenu disponible brut investie dans des actifs financiers.

  • le taux d’épargne s’obtient en rapportant l’épargne brute au revenu disponible brut ajusté de la variation des droits des ménages sur les fonds de pension, préalablement corrigés des variations saisonnières

  • le taux d’épargne financière est estimé en soustrayant la formation brute de capital fixe à l’épargne brute, ensuite rapportée au revenu disponible brut ajusté de la variation des droits des ménages sur les fonds de pension, puis en corrigeant des variations saisonnières.

Annual

Code 
CFT %>%
  
  filter(variable %in% c("CFT.A.N.FR.W0.S1M.S1.N.B.B8G._Z._Z._Z.XDC_R_B6G_S1M._T.S.V.N._T",
                         "CFT.A.N.FR.W0.S1M.S1.N.B.B9Z._Z._Z._Z.XDC_R_B6G_S1M._T.S.V.N._T")) %>%
  na.omit %>%
  ggplot + geom_line(aes(x = date, y = value/100, color = Variable)) + 
  xlab("") + ylab("") + theme_minimal() +
  
  scale_x_date(breaks = "1 year",
               labels = date_format("%Y")) +
  scale_y_continuous(breaks = 0.01*seq(-10, 100, 1),
                labels = percent_format(accuracy = 1)) +
  theme(legend.position = c(0.4, 0.9),
        legend.title = element_blank(),
        legend.direction = "vertical")

Quarterly

All

Code 
CFT %>%
  
  filter(variable %in% c("CFT.Q.N.FR.W0.S1M.S1.N.B.B8G._Z._Z._Z.XDC_R_B6G_S1M._T.S.V.C4._T",
                         "CFT.Q.N.FR.W0.S1M.S1.N.B.B9Z._Z._Z._Z.XDC_R_B6G_S1M._T.S.V.C4._T")) %>%
  na.omit %>%
  ggplot + geom_line(aes(x = date, y = value/100, color = Variable)) + 
  xlab("") + ylab("") + theme_minimal() +
  scale_x_date(breaks = "2 years",
               labels = date_format("%Y")) +
  scale_y_continuous(breaks = 0.01*seq(-10, 100, 1),
                labels = percent_format(accuracy = 1)) +
  theme(legend.position = c(0.42, 0.9),
        legend.title = element_blank(),
        legend.direction = "vertical")

2010-

Code 
CFT %>%
  
  filter(variable %in% c("CFT.Q.N.FR.W0.S1M.S1.N.B.B8G._Z._Z._Z.XDC_R_B6G_S1M._T.S.V.C4._T",
                         "CFT.Q.N.FR.W0.S1M.S1.N.B.B9Z._Z._Z._Z.XDC_R_B6G_S1M._T.S.V.C4._T")) %>%
  na.omit %>%
  filter(date >= as.Date("2010-01-01")) %>%
  ggplot + geom_line(aes(x = date, y = value/100, color = Variable)) + 
  xlab("") + ylab("") + theme_minimal() +
  scale_x_date(breaks = "2 years",
               labels = date_format("%Y")) +
  scale_y_continuous(breaks = 0.01*seq(-10, 100, 1),
                labels = percent_format(accuracy = 1)) +
  theme(legend.position = c(0.45, 0.9),
        legend.title = element_blank(),
        legend.direction = "vertical")

France, Spain, Italy

Table

Code 
load_data("bdf/REF_AREA.RData")
CFT %>%
  
  
  filter(FREQ == "Q",
         UNIT_MEASURE == "XDC_R_B6G_S1M",
         REF_SECTOR == "S1M",
         date == as.Date("2020-01-01")) %>%
  select(Variable, Ref_area, value) %>%
  arrange(Ref_area) %>%
  print_table_conditional
Variable Ref_area value
NA NA NA
:--------: :--------: :-----:

Taux d’épargne financière

France, Italy, Germany

Code 
CFT %>%
  
  
  filter(FREQ == "Q",
         REF_AREA %in% c("FR", "IT", "DE"),
         UNIT_MEASURE == "XDC_R_B6G_S1M",
         STO == "B9Z",
         REF_SECTOR == "S1M") %>%
  mutate(value = value/100) %>%
  na.omit %>%
  ggplot + geom_line(aes(x = date, y = value, color = Ref_area)) + 
  xlab("") + ylab("") + theme_minimal() +
  scale_color_manual(values = c("#002395", "#000000", "#009246")) +
  scale_x_date(breaks = "2 years",
               labels = date_format("%Y")) +
  add_3flags +
  scale_y_continuous(breaks = 0.01*seq(-10, 100, 1),
                labels = percent_format(accuracy = 1)) +
  theme(legend.position = "none")

France, Italy, Germany, Spain, United States

All

Code 
CFT %>%
  
  
  filter(FREQ == "Q",
         UNIT_MEASURE == "XDC_R_B6G_S1M",
         STO == "B9Z",
         REF_SECTOR == "S1M") %>%
  left_join(colors, by = c("Ref_area" = "country")) %>%
  mutate(value = value/100) %>%
  na.omit %>%
  ggplot + geom_line(aes(x = date, y = value, color = color)) + 
  xlab("") + ylab("") + theme_minimal() + scale_color_identity() + add_6flags +
  scale_x_date(breaks = "2 years",
               labels = date_format("%Y")) +
  scale_y_continuous(breaks = 0.01*seq(-10, 100, 1),
                labels = percent_format(accuracy = 1))

2007-

Code 
CFT %>%
  
  
  filter(FREQ == "Q",
         UNIT_MEASURE == "XDC_R_B6G_S1M",
         STO == "B9Z",
         REF_SECTOR == "S1M") %>%
  left_join(colors, by = c("Ref_area" = "country")) %>%
  mutate(value = value/100) %>%
  filter(date >= as.Date("2007-01-01")) %>%
  na.omit %>%
  ggplot + geom_line(aes(x = date, y = value, color = color)) + 
  xlab("") + ylab("") + theme_minimal() + scale_color_identity() + add_6flags +
  scale_x_date(breaks = "1 year",
               labels = date_format("%Y")) +
  scale_y_continuous(breaks = 0.01*seq(-10, 100, 1),
                labels = percent_format(accuracy = 1))

2015-

Code 
CFT %>%
  
  
  filter(FREQ == "Q",
         UNIT_MEASURE == "XDC_R_B6G_S1M",
         STO == "B9Z",
         REF_SECTOR == "S1M") %>%
  left_join(colors, by = c("Ref_area" = "country")) %>%
  mutate(value = value/100) %>%
  filter(date >= as.Date("2015-01-01")) %>%
  na.omit %>%
  ggplot + geom_line(aes(x = date, y = value, color = color)) + 
  xlab("") + ylab("Taux d'épargne financière (%)") + theme_minimal() + scale_color_identity() + add_6flags +
  scale_x_date(breaks = "1 year",
               labels = date_format("%Y")) +
  scale_y_continuous(breaks = 0.01*seq(-10, 100, 1),
                labels = percent_format(accuracy = 1))

Taux d’épargne

France, Italy, Germany

Code 
CFT %>%
  
  
  filter(FREQ == "Q",
         REF_AREA %in% c("FR", "IT", "DE"),
         UNIT_MEASURE == "XDC_R_B6G_S1M",
         STO == "B8G",
         REF_SECTOR == "S1M") %>%
  left_join(colors, by = c("Ref_area" = "country")) %>%
  mutate(value = value/100) %>%
  na.omit %>%
  ggplot + geom_line(aes(x = date, y = value, color = color)) + 
  xlab("") + ylab("") + theme_minimal() + scale_color_identity() + add_3flags +
  scale_x_date(breaks = "2 years",
               labels = date_format("%Y")) +
  scale_y_continuous(breaks = 0.01*seq(-10, 100, 1),
                labels = percent_format(accuracy = 1))

France, Italy, Germany, Spain, United States

Code 
CFT %>%
  
  
  filter(FREQ == "Q",
         UNIT_MEASURE == "XDC_R_B6G_S1M",
         STO == "B8G",
         REF_SECTOR == "S1M") %>%
  left_join(colors, by = c("Ref_area" = "country")) %>%
  mutate(value = value/100) %>%
  na.omit %>%
  ggplot + geom_line(aes(x = date, y = value, color = color)) + 
  xlab("") + ylab("") + theme_minimal() + scale_color_identity() + add_6flags +
  scale_x_date(breaks = "2 years",
               labels = date_format("%Y")) +
  scale_y_continuous(breaks = 0.01*seq(-10, 100, 1),
                labels = percent_format(accuracy = 1))

Dette

Dette des ménages

All

Code 
CFT %>%
  
  
  filter(INSTR_ASSET == "DETT",
         UNIT_MEASURE == "XDC_R_B1GQ_CY",
         REF_SECTOR == "S1M") %>%
  mutate(Ref_area = ifelse(REF_AREA == "I8", "Europe", Ref_area)) %>%
  mutate(Ref_area = ifelse(REF_AREA == "UK", "United Kingdom", Ref_area)) %>%
  left_join(colors, by = c("Ref_area" = "country")) %>%
  mutate(value = value/100) %>%
  arrange(date) %>%
  select(REF_AREA,everything()) %>%
  ggplot + geom_line(aes(x = date, y = value, color = color)) + 
  xlab("") + ylab("") + theme_minimal() + scale_color_identity() + add_7flags +
  scale_x_date(breaks = "2 years",
               labels = date_format("%Y")) +
  scale_y_continuous(breaks = 0.01*seq(-10, 200, 10),
                labels = percent_format(accuracy = 1))

France, Italy, Germany, Spain, Japan, EU

Code 
CFT %>%
  
  
  filter(REF_AREA %in% c("FR", "IT", "DE", "ES", "JP", "I8"),
         INSTR_ASSET == "DETT",
         UNIT_MEASURE == "XDC_R_B1GQ_CY",
         REF_SECTOR == "S1M") %>%
  mutate(Ref_area = ifelse(REF_AREA == "I8", "Europe", Ref_area)) %>%
  left_join(colors, by = c("Ref_area" = "country")) %>%
  mutate(value = value/100) %>%
  ggplot + geom_line(aes(x = date, y = value, color = color)) + 
  xlab("") + ylab("") + theme_minimal() + scale_color_identity() + add_6flags +
  scale_x_date(breaks = "2 years",
               labels = date_format("%Y")) +
  scale_y_continuous(breaks = 0.01*seq(-10, 100, 5),
                labels = percent_format(accuracy = 1))

France, Italy, Germany

All

Code 
CFT %>%
  
  
  filter(REF_AREA %in% c("FR", "IT", "DE"),
         INSTR_ASSET == "DETT",
         UNIT_MEASURE == "XDC_R_B1GQ_CY",
         REF_SECTOR == "S1M") %>%
  mutate(Ref_area = ifelse(REF_AREA == "I8", "Europe", Ref_area)) %>%
  left_join(colors, by = c("Ref_area" = "country")) %>%
  mutate(value = value/100) %>%
  ggplot + geom_line(aes(x = date, y = value, color = color)) + 
  xlab("") + ylab("") + theme_minimal() + scale_color_identity() + add_3flags +
  scale_x_date(breaks = "2 years",
               labels = date_format("%Y")) +
  scale_y_continuous(breaks = 0.01*seq(-10, 100, 5),
                labels = percent_format(accuracy = 1))

2013-

Code 
CFT %>%
  
  
  filter(REF_AREA %in% c("FR", "IT", "DE"),
         INSTR_ASSET == "DETT",
         UNIT_MEASURE == "XDC_R_B1GQ_CY",
         REF_SECTOR == "S1M") %>%
  mutate(Ref_area = ifelse(REF_AREA == "I8", "Europe", Ref_area)) %>%
  left_join(colors, by = c("Ref_area" = "country")) %>%
  mutate(value = value/100) %>%
  filter(date >=as.Date("2013-01-01")) %>%
  ggplot + geom_line(aes(x = date, y = value, color = color)) + 
  xlab("") + ylab("") + theme_minimal() + scale_color_identity() + add_3flags +
  scale_x_date(breaks = "1 year",
               labels = date_format("%Y")) +
  scale_y_continuous(breaks = 0.01*seq(-10, 100, 2),
                labels = percent_format(accuracy = 1))

Non-financial corporations

France, Italy, Germany

Code 
CFT %>%
  
  
  filter(REF_AREA %in% c("FR", "IT", "DE", "I8"),
         INSTR_ASSET == "DETT",
         UNIT_MEASURE == "XDC_R_B1GQ_CY",
         REF_SECTOR == "S11") %>%
  mutate(Ref_area = ifelse(REF_AREA == "I8", "Europe", Ref_area)) %>%
  left_join(colors, by = c("Ref_area" = "country")) %>%
  mutate(value = value/100) %>%
  ggplot + geom_line(aes(x = date, y = value, color = color)) + 
  xlab("") + ylab("") + theme_minimal() + scale_color_identity() + add_4flags +
  scale_x_date(breaks = "2 years",
               labels = date_format("%Y")) +
  scale_y_continuous(breaks = 0.01*seq(-10, 100, 5),
                labels = dollar_format(accuracy = .01, pre = "", su = " année"))

Taux d’endettement

France

Années de PIB
Code 
CFT %>%
  
  filter(variable %in% c("CFT.Q.S.FR.W0.S1M.S1.N.L.LE.DETT.T._Z.XDC_R_B1GQ_CY._T.S.V.N._T",
                         "CFT.Q.S.FR.W0.S11.S1.C.L.LE.DETT.T._Z.XDC_R_B1GQ_CY._T.S.V.N._T",
                         "CFT.Q.N.FR.W0.S13.S1.C.L.LE.GD.T._Z.XDC_R_B1GQ_CY._T.F.V.N._T")) %>%
  mutate(Variable = gsub(", en % du PIB", "", Variable),
         Variable = gsub(" en % du PIB", "", Variable)) %>%
  ggplot + geom_line(aes(x = date, y = value/100, color = Variable)) + 
  xlab("") + ylab("Dette/PIB (en années de PIB)") + theme_minimal() +
  
  scale_x_date(breaks = "2 years",
               labels = date_format("%Y")) +
  scale_y_continuous(breaks = seq(0, 1.3, 0.1),
                     labels = dollar_format(su = " ans", p = "", acc = 0.1)) +
  theme(legend.position = c(0.3, 0.9),
        legend.title = element_blank(),
        legend.direction = "vertical")

% du PIB
Code 
CFT %>%
  
  filter(variable %in% c("CFT.Q.S.FR.W0.S1M.S1.N.L.LE.DETT.T._Z.XDC_R_B1GQ_CY._T.S.V.N._T",
                         "CFT.Q.S.FR.W0.S11.S1.C.L.LE.DETT.T._Z.XDC_R_B1GQ_CY._T.S.V.N._T",
                         "CFT.Q.N.FR.W0.S13.S1.C.L.LE.GD.T._Z.XDC_R_B1GQ_CY._T.F.V.N._T")) %>%
  ggplot + geom_line(aes(x = date, y = value/100, color = Variable)) + 
  xlab("") + ylab("") + theme_minimal() +
  
  scale_x_date(breaks = "2 years",
               labels = date_format("%Y")) +
  scale_y_continuous(breaks = 0.01*seq(-10, 140, 5),
                labels = percent_format(accuracy = 1)) +
  theme(legend.position = c(0.3, 0.9),
        legend.title = element_blank(),
        legend.direction = "vertical")

Allemagne

Code 
CFT %>%
  
  filter(variable %in% c("CFT.Q.N.DE.W0.S1M.S1.N.L.LE.DETT.T._Z.XDC_R_B1GQ_CY._T.S.V.N._T",
                         "CFT.Q.N.DE.W0.S11.S1.C.L.LE.DETT.T._Z.XDC_R_B1GQ_CY._T.S.V.N._T",
                         "CFT.Q.N.DE.W0.S13.S1.C.L.LE.GD.T._Z.XDC_R_B1GQ_CY._T.F.V.N._T")) %>%
  ggplot + geom_line(aes(x = date, y = value/100, color = Variable)) + 
  xlab("") + ylab("") + theme_minimal() +
  
  scale_x_date(breaks = "2 years",
               labels = date_format("%Y")) +
  scale_y_continuous(breaks = 0.01*seq(-10, 140, 5),
                labels = percent_format(accuracy = 1)) +
  theme(legend.position = c(0.3, 0.9),
        legend.title = element_blank(),
        legend.direction = "vertical")

Italie

Code 
CFT %>%
  
  filter(variable %in% c("CFT.Q.N.IT.W0.S1M.S1.N.L.LE.DETT.T._Z.XDC_R_B1GQ_CY._T.S.V.N._T",
                         "CFT.Q.N.IT.W0.S11.S1.C.L.LE.DETT.T._Z.XDC_R_B1GQ_CY._T.S.V.N._T",
                         "CFT.Q.N.IT.W0.S13.S1.C.L.LE.GD.T._Z.XDC_R_B1GQ_CY._T.F.V.N._T")) %>%
  ggplot + geom_line(aes(x = date, y = value/100, color = Variable)) + 
  xlab("") + ylab("") + theme_minimal() +
  
  scale_x_date(breaks = "2 years",
               labels = date_format("%Y")) +
  scale_y_continuous(breaks = 0.01*seq(-10, 400, 10),
                labels = percent_format(accuracy = 1)) +
  theme(legend.position = c(0.3, 0.9),
        legend.title = element_blank(),
        legend.direction = "vertical")

Espagne

Code 
CFT %>%
  
  filter(variable %in% c("CFT.Q.N.ES.W0.S1M.S1.N.L.LE.DETT.T._Z.XDC_R_B1GQ_CY._T.S.V.N._T",
                         "CFT.Q.N.ES.W0.S11.S1.C.L.LE.DETT.T._Z.XDC_R_B1GQ_CY._T.S.V.N._T",
                         "CFT.Q.N.ES.W0.S13.S1.C.L.LE.GD.T._Z.XDC_R_B1GQ_CY._T.F.V.N._T")) %>%
  ggplot + geom_line(aes(x = date, y = value/100, color = Variable)) + 
  xlab("") + ylab("") + theme_minimal() +
  
  scale_x_date(breaks = "2 years",
               labels = date_format("%Y")) +
  scale_y_continuous(breaks = 0.01*seq(-10, 400, 10),
                labels = percent_format(accuracy = 1)) +
  theme(legend.position = c(0.2, 0.9),
        legend.title = element_blank(),
        legend.direction = "vertical")

Zone Euro

Code 
CFT %>%
  
  filter(variable %in% c("CFT.Q.N.I8.W0.S1M.S1.N.L.LE.DETT.T._Z.XDC_R_B1GQ_CY._T.S.V.N._T",
                         "CFT.Q.N.I8.W0.S11.S1.C.L.LE.DETT.T._Z.XDC_R_B1GQ_CY._T.S.V.N._T",
                         "CFT.Q.N.I8.W0.S13.S1.C.L.LE.GD.T._Z.XDC_R_B1GQ_CY._T.F.V.N._T")) %>%
  ggplot + geom_line(aes(x = date, y = value/100, color = Variable)) + 
  xlab("") + ylab("") + theme_minimal() +
  
  scale_x_date(breaks = "2 years",
               labels = date_format("%Y")) +
  scale_y_continuous(breaks = 0.01*seq(-10, 140, 5),
                labels = percent_format(accuracy = 1)) +
  theme(legend.position = c(0.3, 0.9),
        legend.title = element_blank(),
        legend.direction = "vertical")

Informations supplémentaires

Données sur la macroéconomie en France

source dataset Title .html .rData
bdf CFT Comptes Financiers Trimestriels 2026-07-23 2025-03-09
insee CNA-2014-CONSO-SI Dépenses de consommation finale par secteur institutionnel 2026-07-23 2026-07-23
insee CNA-2014-CSI Comptes des secteurs institutionnels 2026-07-23 2026-07-23
insee CNA-2014-FBCF-BRANCHE Formation brute de capital fixe (FBCF) par branche 2026-07-23 2026-07-23
insee CNA-2014-FBCF-SI Formation brute de capital fixe (FBCF) par secteur institutionnel 2026-07-23 2026-07-23
insee CNA-2014-RDB Revenu et pouvoir d’achat des ménages 2026-07-23 2026-07-23
insee CNA-2020-CONSO-MEN Consommation des ménages 2026-07-23 2026-07-23
insee CNA-2020-PIB Produit intérieur brut (PIB) et ses composantes 2026-07-23 2026-07-23
insee CNT-2014-CB Comptes des branches 2026-07-23 2026-07-23
insee CNT-2014-CSI Comptes de secteurs institutionnels 2026-07-23 2026-07-22
insee CNT-2014-OPERATIONS Opérations sur biens et services 2026-07-23 2026-07-23
insee CNT-2014-PIB-EQB-RF Équilibre du produit intérieur brut 2026-07-23 2026-07-23
insee CONSO-MENAGES-2020 Consommation des ménages en biens 2026-07-23 2026-07-23
insee ICA-2015-IND-CONS Indices de chiffre d'affaires dans l'industrie et la construction 2026-07-23 2026-07-23
insee conso-mensuelle Consommation de biens, données mensuelles 2026-07-23 2023-07-04
insee t_1101 1.101 – Le produit intérieur brut et ses composantes à prix courants (En milliards d'euros) 2026-07-23 2022-01-02
insee t_1102 1.102 – Le produit intérieur brut et ses composantes en volume aux prix de l'année précédente chaînés (En milliards d'euros 2014) 2026-07-23 2020-10-30
insee t_1105 1.105 – Produit intérieur brut - les trois approches à prix courants (En milliards d'euros) - t_1105 2026-07-23 2020-10-30

Data on saving

source dataset Title .html .rData
bdf CFT Comptes Financiers Trimestriels 2026-07-23 2025-03-09
bea T50100 Table 5.1. Saving and Investment by Sector (A) (Q) 2026-07-22 2026-07-22
fred saving Saving - saving 2026-07-22 2026-07-22
oecd NAAG National Accounts at a Glance - NAAG 2024-04-16 2025-05-12
wdi NY.GDS.TOTL.ZS Gross domestic savings (% of GDP) - NY.GDS.TOTL.ZS 2022-09-27 2026-07-22
wdi NY.GNS.ICTR.ZS Gross savings (% of GDP) - NY.GNS.ICTR.ZS 2022-09-27 2026-07-22