Comptes Financiers Trimestriels - CFT

Name: Comptes Financiers Trimestriels
Creator: François Geerolf
License: https://www.etalab.gouv.fr/licence-ouverte-open-licence/

Données - BDF

Auteur

François Geerolf

🇬🇧 English version

Structure

Méthodologie. pdf

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")) |>
  filter(!is.na(value)) |>
  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")) |>
  filter(!is.na(value)) |>
  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")) |>
  filter(!is.na(value)) |>
  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")) |>
  filter(!is.na(value)) |>
  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")) |>
  filter(!is.na(value)) |>
  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)) |>
  add_flag_color("Ref_area") |>
  filter(!is.na(value)) |>
  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) |>
  filter(!is.na(value)) |>
  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) |>
  filter(!is.na(value)) |>
  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) |>
  filter(!is.na(value)) |>
  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) |>
  filter(!is.na(value)) |>
  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") |>
  filter(!is.na(value)) |>
  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") |>
  filter(!is.na(value)) |>
  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") |>
  filter(!is.na(value)) |>
  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")) |>
  filter(!is.na(value)) |>
  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")) |>
  filter(!is.na(value)) |>
  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")) |>
  filter(!is.na(value)) |>
  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")) |>
  filter(!is.na(value)) |>
  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

REF_AREA <- read_parquet("REF_AREA.parquet")
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
character(0)	c(":--------:", ":--------:", ":-----:")	character(0)

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) |>
  filter(!is.na(value)) |>
  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") |>
  add_flag_color("Ref_area") |>
  mutate(value = value/100) |>
  filter(!is.na(value)) |>
  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") |>
  add_flag_color("Ref_area") |>
  mutate(value = value/100) |>
  filter(date >= as.Date("2007-01-01")) |>
  filter(!is.na(value)) |>
  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") |>
  add_flag_color("Ref_area") |>
  mutate(value = value/100) |>
  filter(date >= as.Date("2015-01-01")) |>
  filter(!is.na(value)) |>
  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") |>
  add_flag_color("Ref_area") |>
  mutate(value = value/100) |>
  filter(!is.na(value)) |>
  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") |>
  add_flag_color("Ref_area") |>
  mutate(value = value/100) |>
  filter(!is.na(value)) |>
  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)) |>
  add_flag_color("Ref_area") |>
  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)) |>
  add_flag_color("Ref_area") |>
  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)) |>
  add_flag_color("Ref_area") |>
  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)) |>
  add_flag_color("Ref_area") |>
  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)) |>
  add_flag_color("Ref_area") |>
  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")

Related Data

Statistiques de l’épargne en France

source	dataset	Title	Updated
bdf	CFT	Comptes Financiers Trimestriels - CFT	2026-08-10
insee	T_6461	6.461-462 - Consommation de capital fixe par branche (38 postes) - T_6461	2026-09-08
insee	T_7401	7.401 – Compte des ménages (S14) (En milliards d’euros) - T_7401	2026-09-08
insee	bdf2017	Budget de famille 2017 - bdf2017	2026-09-04
insee	ip1815	Plus d’épargne chez les plus aisés, plus de dépenses contraintes chez les plus modestes - ip1815	2026-08-02
insee	t_pouvachat_val	Pouvoir d’achat et ratios des comptes des ménages - t_pouvachat_val	2026-09-03
insee	t_recapAgent_val	Récapitulatif des séries des comptes d’agents - t_recapAgent_val	2026-09-03

Housing Market Insights

source	dataset	Title	Updated
bdf	CFT	Comptes Financiers Trimestriels - CFT	2026-08-10
bis	SELECTED_PP	Property prices, selected series - SELECTED_PP	2026-09-17
eurostat	hbs_str_t223	Mean consumption expenditure by income quintile - hbs_str_t223	2026-09-09
eurostat	nama_10_co3_p3	Final consumption expenditure of households by consumption purpose (COICOP 3 digit) - nama_10_co3_p3	2026-09-09
eurostat	nasq_10_ki	Key indicators and growth rates of selected transactions - nasq_10_ki	2026-09-09
eurostat	prc_hicp_midx	HICP (2015 = 100) - monthly data (index) - prc_hicp_midx	2026-09-09
eurostat	prc_hpi_ooq	Owner-occupied housing price index (2015=100) - quarterly data - prc_hpi_ooq	2026-09-09
eurostat	prc_hpi_q	House price index (2015 = 100) - quarterly data - prc_hpi_q	2026-09-09
fhfa	index	Federal Housing Finance Agency - fhfa	2026-09-18
fred	housing	House Prices - housing	2026-09-19
oecd	HOUSING	Residential Property Price Indices (RPPIs) and related housing indicators - HOUSING	2026-08-02
oecd	SNA_TABLE5	Final consumption expenditure of households - SNA_TABLE5	2026-08-02

Indicateurs Macroéconomiques France

source	dataset	Title	Updated
bdf	BPM6	Balance des paiements - 6ème manuel - BPM6	2026-09-04
bdf	CFT	Comptes Financiers Trimestriels - CFT	2026-08-10
eurostat	lfsq_ergaed	Employment rates by sex, age and educational attainment level (%) - lfsq_ergaed	2026-09-11
eurostat	une_rt_m	Unemployment by sex and age – monthly data - une_rt_m	2026-09-17
insee	CHOMAGE-TRIM-NATIONAL	Chômage, taux de chômage par sexe et âge (sens BIT) (1975-) - CHOMAGE-TRIM-NATIONAL	2026-09-17
insee	CNA-2014-FBCF-SI	Formation brute de capital fixe (FBCF) par secteur institutionnel - CNA-2014-FBCF-SI	2026-09-17
insee	CNT-2020-CB	Comptes des branches - CNT-2020-CB	2026-09-17
insee	CNT-2020-CSI	Comptes de secteurs institutionnels - CNT-2020-CSI	2026-09-17
insee	CNT-2020-OPERATIONS	Opérations sur biens et services - CNT-2020-OPERATIONS	2026-09-17
insee	CNT-2020-PIB-EQB-RF	Équilibre du produit intérieur brut - CNT-2020-PIB-EQB-RF	2026-09-17
insee	CONSO-MENAGES-2014	Consommation des ménages en biens - CONSO-MENAGES-2014	2026-09-17
insee	CONSO-MENAGES-2020	Consommation des ménages en biens - CONSO-MENAGES-2020	2026-09-17
insee	DEMANDES-EMPLOIS-NATIONALES	Demandeurs d’emploi inscrits à Pôle Emploi - DEMANDES-EMPLOIS-NATIONALES	2026-09-17
insee	EMPLOI-SALARIE-TRIM-NATIONAL	Estimations d’emploi salarié par secteur d’activité - EMPLOI-SALARIE-TRIM-NATIONAL	2026-09-17
insee	ICA-2015-IND-CONS	Indices de chiffre d’affaires dans l’industrie et la construction - ICA-2015-IND-CONS	2026-09-17
insee	conso-mensuelle	Consommation de biens, données mensuelles - conso-mensuelle	2026-08-02
insee	econ-gen-solde-ech-ext-2	Solde des échanges extérieurs et principales composantes - econ-gen-solde-ech-ext-2	2026-08-02
insee	t_conso_vol	Dépenses de consommation des ménages - volumes aux prix de l’année précédente chaînés (données CVS-CJO), base 2014 - t_conso_vol	2026-09-03
insee	t_recapAgent_val	Récapitulatif des séries des comptes d’agents - t_recapAgent_val	2026-09-03
insee	t_zonexp_val	Exportations - Par zone - valeurs aux prix courants - t_zonexp_val	2026-09-03
wdi	NY.GDP.PCAP.PP.CD	GDP per capita, PPP (current international D) - NY.GDP.PCAP.PP.CD	2026-09-18
wdi	NY.GDP.PCAP.PP.KD	GDP per capita, PPP (constant 2011 international D) - NY.GDP.PCAP.PP.KD	2026-09-18

Data on saving

source	dataset	Title	Updated
bdf	CFT	Comptes Financiers Trimestriels - CFT	2026-08-10
bea	T50100	Table 5.1. Saving and Investment by Sector (A) (Q) - T50100	2026-09-17
eurostat	nasq_10_ki	Key indicators and growth rates of selected transactions - nasq_10_ki	2026-09-09
eurostat	nasq_10_nf_tr	Non-financial transactions - nasq_10_nf_tr	2026-09-09
fred	saving	Saving - saving	2026-09-19
oecd	NAAG	National Accounts at a Glance - NAAG	2026-08-02