Quarterly Financial Accounts

Data - Banque de France

Author

François Geerolf

🇫🇷 Version française

Info

Last observation: Trimestriel: 30 sep 2024 (N = 22,870) · Annuel: 31 déc 2023 (N = 1,479)

First observation: Trimestriel: 31 déc 1995 (N = 22,870) · Annuel: 31 déc 1995 (N = 1,479)

Last data update: 10 aoû 2026, 16:21. Last compile: 14 aoû 2026, 00:24

Structure

source dataset Title .html .rData
bdf CFT Comptes Financiers Trimestriels 2026-08-12 2026-08-10
  • Methodology. pdf

Last

date Nobs
2024-09-30 4

List of publications

  • Households’ financial saving and wealth, 2024Q1. pdf / html

  • Financial accounts of non-financial agents. STAT INFO – Q4 2023. pdf

  • Household saving. STAT INFO – February 2024. pdf

Info

  • Households’ financial saving and wealth, 2024Q2. pdf html

  • Households’ financial saving and wealth, Financial accounts of non-financial agents, 15 April 2024, 2023Q4. pdf html

  • Households’ financial saving and wealth, 2023Q3. pdf

  • Households’ financial saving and wealth, 2023Q2. pdf

  • Quarterly presentation of household saving, 2023Q1. html pdf

  • Methodology. pdf

  • List of series. html

  • Households’ financial saving and wealth, 2022Q2. pdf

  • Household saving, 2021Q1. pdf

  • Debt ratio of non-financial agents – International comparisons, 2020Q4. html / pdf

  • Household saving, 2020Q3. pdf

INSTR_ASSET Instrument and assets classification

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

Main aggregates

2024Q2

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

2023Q4

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

Fixed-income products

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)))

Equity products

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)))

Listed, unlisted

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

Linear

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)))

Outstanding amounts

Currency and overnight deposits

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)))

Outstanding amounts

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)))

Currency and overnight deposits

Outstanding amounts

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)))

Flows - 4 quarters

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")

Flows

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")

Listed/unlisted shares, unit-linked life insurance

Outstanding amounts

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)))

Listed/unlisted shares, unit-linked life insurance

Outstanding amounts

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)))

Flows

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")

Currency and overnight deposits

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)))

Life insurance

All

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")

Household saving rate

The household saving rate is the ratio of households’ gross saving (B8G) to gross disposable income adjusted for changes in pension entitlements. Gross disposable income (B6G) corresponds to the income received by households (earned income and property income) after redistribution operations (adding cash social benefits received, subtracting contributions and taxes).

As for the financial saving rate, it is the share of gross disposable income invested in financial assets.

  • the saving rate is obtained by dividing gross saving by gross disposable income adjusted for the change in households’ entitlements under pension funds, with both series previously seasonally adjusted

  • the financial saving rate is estimated by subtracting gross fixed capital formation from gross saving, then dividing the result by gross disposable income adjusted for the change in households’ entitlements under pension funds, and then seasonally adjusting the result.

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 
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
NA NA NA
:--------: :--------: :-----:

Financial saving rate

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("Financial saving rate (%)") + 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))

Saving rate

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))

Debt

Household debt

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"))

Debt ratio

France

Years of GDP
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("Debt/GDP (in years of GDP)") + 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")

% of GDP
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")

Germany

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")

Italy

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")

Spain

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")

Euro area

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")

Additional information

Data on the French macroeconomy

source dataset Title .html .rData
bdf CFT Comptes Financiers Trimestriels 2026-08-12 2026-08-10
insee CNA-2014-CONSO-SI Dépenses de consommation finale par secteur institutionnel 2026-08-13 2026-08-12
insee CNA-2014-CSI Comptes des secteurs institutionnels 2026-08-13 2026-08-12
insee CNA-2014-FBCF-BRANCHE Formation brute de capital fixe (FBCF) par branche 2026-08-13 2026-08-12
insee CNA-2014-FBCF-SI Formation brute de capital fixe (FBCF) par secteur institutionnel 2026-08-13 2026-08-12
insee CNA-2014-RDB Revenu et pouvoir d’achat des ménages 2026-08-13 2026-08-12
insee CNA-2020-CONSO-MEN Consommation des ménages 2026-08-13 2026-07-23
insee CNA-2020-PIB Produit intérieur brut (PIB) et ses composantes 2026-08-13 2026-08-12
insee CNT-2014-CB Comptes des branches 2026-08-13 2026-08-12
insee CNT-2014-CSI Comptes de secteurs institutionnels 2026-08-13 2026-08-12
insee CNT-2014-OPERATIONS Opérations sur biens et services 2026-08-13 2026-08-12
insee CNT-2014-PIB-EQB-RF Équilibre du produit intérieur brut 2026-08-13 2026-08-12
insee CONSO-MENAGES-2020 Consommation des ménages en biens 2026-08-13 2026-08-12
insee ICA-2015-IND-CONS Indices de chiffre d'affaires dans l'industrie et la construction 2026-08-13 2026-08-13
insee conso-mensuelle Consommation de biens, données mensuelles 2026-08-13 2026-08-02
insee t_1101 1.101 – Le produit intérieur brut et ses composantes à prix courants (En milliards d'euros) 2026-08-13 2026-08-02
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) 2026-08-13 2026-08-02
insee t_1105 1.105 – Produit intérieur brut - les trois approches à prix courants (En milliards d'euros) - t_1105 2026-08-13 2026-08-02

Data on saving

source dataset Title .html .rData
bdf CFT Comptes Financiers Trimestriels 2026-08-12 2026-08-10
bea T50100 Table 5.1. Saving and Investment by Sector (A) (Q) 2026-08-12 2026-08-12
fred saving Saving - saving 2026-08-12 2026-08-12
oecd NAAG National Accounts at a Glance - NAAG 2026-08-13 2026-08-02
wdi NY.GDS.TOTL.ZS Gross domestic savings (% of GDP) - NY.GDS.TOTL.ZS 2026-08-12 2026-08-12
wdi NY.GNS.ICTR.ZS Gross savings (% of GDP) - NY.GNS.ICTR.ZS 2026-08-12 2026-08-12