Comptes Financiers Trimestriels
Data - BDF
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- Méthodologie. pdf
Last
date | Nobs |
---|---|
2024-09-30 | 4 |
Liste des publications
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
STO
Code
%>%
CFT left_join(CFT_var, by = "variable") %>%
left_join(STO, by = "STO") %>%
group_by(STO, Sto) %>%
summarise(Nobs = n()) %>%
arrange(-Nobs) %>%
print_table_conditional
STO | Sto | Nobs |
---|---|---|
F | flux | 13459 |
LE | Encours | 8793 |
B8G | Taux d'épargne des ménages | 836 |
B9Z | Taux d'épargne financière des ménages | 693 |
K | Réévaluations et autres changements de volume | 258 |
K5 | Impact de valorisation | 167 |
P51G | Formation brute de capital fixe | 143 |
FREQ Frequency
Code
%>%
CFT left_join(CFT_var, by = "variable") %>%
left_join(FREQ, by = "FREQ") %>%
group_by(FREQ, Freq) %>%
summarise(Nobs = n()) %>%
arrange(-Nobs) %>%
print_table_conditional
FREQ | Freq | Nobs |
---|---|---|
Q | Trimestriel | 22870 |
A | Annuel | 1479 |
ADJUSTMENT Adjustment
Code
%>%
CFT left_join(CFT_var, by = "variable") %>%
left_join(ADJUSTMENT, by = "ADJUSTMENT") %>%
group_by(ADJUSTMENT, Adjustment) %>%
summarise(Nobs = n()) %>%
arrange(-Nobs) %>%
print_table_conditional
ADJUSTMENT | Adjustment | Nobs |
---|---|---|
N | Brut | 11931 |
S | CVS | 11874 |
Y | CVS/CJO | 286 |
_Z | Non applicable | 258 |
REF_AREA Reference area - ISO2
Code
%>%
CFT left_join(CFT_var, by = "variable") %>%
left_join(REF_AREA, by = "REF_AREA") %>%
group_by(REF_AREA, Ref_area) %>%
summarise(Nobs = n()) %>%
arrange(-Nobs) %>%
print_table_conditional
REF_AREA | Ref_area | Nobs |
---|---|---|
FR | France | 18452 |
DE | Germany | 936 |
ES | Spain | 936 |
IT | Italy | 936 |
US | United States | 936 |
GB | United Kingdom | 831 |
I9 | NA | 710 |
JP | Japan | 612 |
COUNTERPART_AREA Counterpart area
Code
%>%
CFT left_join(CFT_var, by = "variable") %>%
left_join(COUNTERPART_AREA, by = "COUNTERPART_AREA") %>%
group_by(COUNTERPART_AREA, Counterpart_area) %>%
summarise(Nobs = n()) %>%
arrange(-Nobs) %>%
print_table_conditional
COUNTERPART_AREA | Counterpart_area | Nobs |
---|---|---|
W0 | World (all areas, including reference area, including IO) | 23314 |
W2 | Domestic (home or reference area) | 1035 |
REF_SECTOR Reference sector
Code
%>%
CFT left_join(CFT_var, by = "variable") %>%
left_join(REF_SECTOR, by = "REF_SECTOR") %>%
group_by(REF_SECTOR, Ref_sector) %>%
summarise(Nobs = n()) %>%
arrange(-Nobs) %>%
print_table_conditional
REF_SECTOR | Ref_sector | Nobs |
---|---|---|
S1M | Ménages et Institution sans but lucratif au service des ménages (ISBLSM) | 14519 |
S11 | Sociétés non financières | 5326 |
S13 | Administrations publiques | 3586 |
S1V | Sociétés non-financières, ménages et NPISH | 918 |
COUNTERPART_SECTOR Counterpart sector
Code
%>%
CFT left_join(CFT_var, by = "variable") %>%
left_join(COUNTERPART_SECTOR, by = "COUNTERPART_SECTOR") %>%
group_by(COUNTERPART_SECTOR, Counterpart_sector) %>%
summarise(Nobs = n()) %>%
arrange(-Nobs) %>%
print_table_conditional
COUNTERPART_SECTOR | Counterpart_sector | Nobs |
---|---|---|
S1 | Ensemble de l`économie | 23314 |
S124 | des OPC non monétaires | 1035 |
ACCOUNTING_ENTRY Accounting entries
Code
%>%
CFT left_join(CFT_var, by = "variable") %>%
left_join(ACCOUNTING_ENTRY, by = "ACCOUNTING_ENTRY") %>%
group_by(ACCOUNTING_ENTRY, Accounting_entry) %>%
summarise(Nobs = n()) %>%
arrange(-Nobs) %>%
print_table_conditional
ACCOUNTING_ENTRY | Accounting_entry | Nobs |
---|---|---|
A | Créances | 13245 |
L | Engagements | 8673 |
B | Balance (Crédit moins débit) | 1529 |
N | Net (avoirs moins engagements) | 506 |
NE | Engagements Nets (engagements moins avoirs) | 253 |
D | Débit (dépenses) | 143 |
INSTR_ASSET Instrument and assets classification
Code
%>%
CFT left_join(CFT_var, by = "variable") %>%
left_join(INSTR_ASSET, by = "INSTR_ASSET") %>%
group_by(INSTR_ASSET, Instr_asset) %>%
summarise(Nobs = n()) %>%
#arrange(-Nobs) %>%
print_table_conditional
MATURITY Maturity
Code
%>%
CFT left_join(CFT_var, by = "variable") %>%
left_join(MATURITY, by = "MATURITY") %>%
group_by(MATURITY, Maturity) %>%
summarise(Nobs = n()) %>%
arrange(-Nobs) %>%
print_table_conditional
MATURITY | Maturity | Nobs |
---|---|---|
T | Toutes maturités d`origine | 12536 |
_Z | Non applicable | 11813 |
UNIT_MEASURE Unit of measure
Code
%>%
CFT left_join(CFT_var, by = "variable") %>%
left_join(UNIT_MEASURE, by = "UNIT_MEASURE") %>%
group_by(UNIT_MEASURE, Unit_measure) %>%
summarise(Nobs = n()) %>%
arrange(-Nobs) %>%
print_table_conditional
UNIT_MEASURE | Unit_measure | Nobs |
---|---|---|
XDC | Monnaie nationale | 17073 |
XDC_R_B1GQ_CY | Monnaie nationale (incl. une conversion à la monnaie courante en utilisant une parité fixe); ratio à la somme du glissement annuel du produit intérieur brut | 3260 |
XDC_R_B6G_S1M | en % du RDB | 2202 |
XDC_R_B1G_CY | NA | 809 |
XDC_R_DEBT | NA | 612 |
PC | Pourcent | 393 |
CURRENCY_DENOM Currency denominator
Code
%>%
CFT left_join(CFT_var, by = "variable") %>%
left_join(CURRENCY_DENOM, by = "CURRENCY_DENOM") %>%
group_by(CURRENCY_DENOM, Currency_denom) %>%
summarise(Nobs = n()) %>%
arrange(-Nobs) %>%
print_table_conditional
CURRENCY_DENOM | Currency_denom | Nobs |
---|---|---|
_T | Toutes monnaies d`opération | 24349 |
VALUATION Valuation
Code
%>%
CFT left_join(CFT_var, by = "variable") %>%
left_join(VALUATION, by = "VALUATION") %>%
group_by(VALUATION, Valuation) %>%
summarise(Nobs = n()) %>%
arrange(-Nobs) %>%
print_table_conditional
VALUATION | Valuation | Nobs |
---|---|---|
S | Valorisation standard basée sur ESA2010/SNA2008 | 23129 |
N | Valeur nominale (N) | 612 |
F | Valeur nominale (F) | 608 |
date
Code
%>%
CFT group_by(date) %>%
summarise(Nobs = n()) %>%
arrange(desc(date)) %>%
print_table_conditional
Grandes masses
2024T2
Code
ig_b("bdf", "CFT-2024T2")
2023T4
Code
ig_b("bdf", "CFT-2023T4")
Produits de taux
Code
%>%
CFT left_join(CFT_var, by = "variable") %>%
filter(STO == "LE",
%in% c("PDTX")) %>%
INSTR_ASSET %>%
na.omit + geom_line(aes(x = date, y = value, color = Variable)) +
ggplot 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 left_join(CFT_var, by = "variable") %>%
filter(STO == "LE",
%in% c("PDFP")) %>%
INSTR_ASSET %>%
na.omit + geom_line(aes(x = date, y = value, color = Variable)) +
ggplot 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 left_join(CFT_var, by = "variable") %>%
filter(STO == "LE",
%in% c("PDFP", "F51", "F511", "F51M", "F52")) %>%
INSTR_ASSET %>%
na.omit + geom_line(aes(x = date, y = value, color = Variable)) +
ggplot 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 left_join(CFT_var, by = "variable") %>%
filter(STO == "LE",
%in% c("F62A", "F62B", "F29R", "F2A", "F29Z")) %>%
INSTR_ASSET %>%
na.omit + geom_line(aes(x = date, y = value, color = Variable)) +
ggplot 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 left_join(CFT_var, by = "variable") %>%
filter(STO == "LE",
%in% c("F62A", "F62B", "F29R", "F2A", "F29Z")) %>%
INSTR_ASSET %>%
na.omit + geom_line(aes(x = date, y = value, color = Variable)) +
ggplot 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 left_join(CFT_var, by = "variable") %>%
left_join(REF_AREA, by = "REF_AREA") %>%
filter(REF_AREA %in% c("FR", "IT", "DE"),
== "F2A",
INSTR_ASSET == "LE") %>%
STO mutate(Ref_area = ifelse(REF_AREA == "I8", "Europe", Ref_area)) %>%
left_join(colors, by = c("Ref_area" = "country")) %>%
%>%
na.omit + geom_line(aes(x = date, y = value, color = color)) +
ggplot 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 left_join(CFT_var, by = "variable") %>%
left_join(INSTR_ASSET, by = "INSTR_ASSET") %>%
filter(REF_AREA %in% c("FR"),
%in% c("F2A", "F29Z", "F62B", "F29R"),
INSTR_ASSET == "LE") %>%
STO select(date, value, Instr_asset) %>%
%>%
na.omit + geom_line(aes(x = date, y = value, color = Instr_asset)) +
ggplot 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 left_join(CFT_var, by = "variable") %>%
left_join(INSTR_ASSET, by = "INSTR_ASSET") %>%
filter(REF_AREA %in% c("FR"),
%in% c("F2A", "F29Z", "F62B"),
INSTR_ASSET == "LE") %>%
STO select(date, value, Instr_asset) %>%
%>%
na.omit + geom_line(aes(x = date, y = value, color = Instr_asset)) +
ggplot 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 left_join(CFT_var, by = "variable") %>%
left_join(INSTR_ASSET, by = "INSTR_ASSET") %>%
filter(REF_AREA %in% c("FR"),
%in% c("F2A", "F29Z", "F62B"),
INSTR_ASSET == "LE") %>%
STO select(date, value, Instr_asset) %>%
%>%
na.omit filter(date >= as.Date("2022-01-01")) %>%
+ geom_line(aes(x = date, y = value, color = Instr_asset)) +
ggplot 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 left_join(CFT_var, by = "variable") %>%
left_join(INSTR_ASSET, by = "INSTR_ASSET") %>%
filter(REF_AREA %in% c("FR"),
%in% c("F2A", "F29Z", "F62B", "F29R"),
INSTR_ASSET == "F",
STO == "C4") %>%
TRANSFORMATION #filter(date >= as.Date("2016-01-01")) %>%
select(date, value, Instr_asset) %>%
%>%
na.omit + geom_line(aes(x = date, y = value, color = Instr_asset)) +
ggplot 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 left_join(CFT_var, by = "variable") %>%
left_join(INSTR_ASSET, by = "INSTR_ASSET") %>%
filter(REF_AREA %in% c("FR"),
%in% c("F2A", "F29Z", "F62B", "F29R"),
INSTR_ASSET == "F",
STO == "N",
TRANSFORMATION == "Q") %>%
FREQ filter(date >= as.Date("2010-01-01")) %>%
+ geom_line(aes(x = date, y = value, color = Instr_asset)) +
ggplot 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 left_join(CFT_var, by = "variable") %>%
left_join(INSTR_ASSET, by = "INSTR_ASSET") %>%
filter(REF_AREA %in% c("FR"),
%in% c("F2A", "F29Z", "F62B", "F29R"),
INSTR_ASSET == "F",
STO == "N",
TRANSFORMATION == "Q") %>%
FREQ filter(date >= as.Date("2016-01-01")) %>%
+ geom_line(aes(x = date, y = value, color = Instr_asset)) +
ggplot 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 left_join(CFT_var, by = "variable") %>%
left_join(INSTR_ASSET, by = "INSTR_ASSET") %>%
filter(REF_AREA %in% c("FR"),
%in% c( "F511", "F51M", "F51"),
INSTR_ASSET == "LE") %>%
STO %>%
na.omit + geom_line(aes(x = date, y = value, color = Instr_asset)) +
ggplot 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 left_join(CFT_var, by = "variable") %>%
left_join(INSTR_ASSET, by = "INSTR_ASSET") %>%
filter(REF_AREA %in% c("FR"),
%in% c( "F511", "F51M", "F62A"),
INSTR_ASSET == "LE") %>%
STO %>%
na.omit + geom_line(aes(x = date, y = value, color = Instr_asset)) +
ggplot 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 left_join(CFT_var, by = "variable") %>%
left_join(INSTR_ASSET, by = "INSTR_ASSET") %>%
filter(REF_AREA %in% c("FR"),
%in% c( "F511", "F51M", "F62A"),
INSTR_ASSET == "F",
STO == "N",
TRANSFORMATION == "Q") %>%
FREQ filter(date >= as.Date("2016-01-01")) %>%
+ geom_line(aes(x = date, y = value, color = Instr_asset)) +
ggplot 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 left_join(CFT_var, by = "variable") %>%
left_join(INSTR_ASSET, by = "INSTR_ASSET") %>%
filter(REF_AREA %in% c("FR"),
%in% c("F2A", "F29Z"),
INSTR_ASSET == "LE") %>%
STO %>%
na.omit + geom_line(aes(x = date, y = value, color = Instr_asset)) +
ggplot 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 left_join(CFT_var, by = "variable") %>%
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 + geom_line(aes(x = date, y = value, color = Variable)) +
ggplot 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 left_join(CFT_var, by = "variable") %>%
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")) %>%
+ geom_line(aes(x = date, y = value, color = Variable)) +
ggplot 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.
France
Annual
Code
%>%
CFT left_join(CFT_var, by = "variable") %>%
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 + geom_line(aes(x = date, y = value/100, color = Variable)) +
ggplot 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 left_join(CFT_var, by = "variable") %>%
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 + geom_line(aes(x = date, y = value/100, color = Variable)) +
ggplot 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 left_join(CFT_var, by = "variable") %>%
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")) %>%
+ geom_line(aes(x = date, y = value/100, color = Variable)) +
ggplot 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 left_join(CFT_var, by = "variable") %>%
left_join(REF_AREA, by = "REF_AREA") %>%
filter(FREQ == "Q",
== "XDC_R_B6G_S1M",
UNIT_MEASURE == "S1M",
REF_SECTOR == as.Date("2020-01-01")) %>%
date 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 left_join(CFT_var, by = "variable") %>%
left_join(REF_AREA, by = "REF_AREA") %>%
filter(FREQ == "Q",
%in% c("FR", "IT", "DE"),
REF_AREA == "XDC_R_B6G_S1M",
UNIT_MEASURE == "B9Z",
STO == "S1M") %>%
REF_SECTOR mutate(value = value/100) %>%
%>%
na.omit + geom_line(aes(x = date, y = value, color = Ref_area)) +
ggplot 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 left_join(CFT_var, by = "variable") %>%
left_join(REF_AREA, by = "REF_AREA") %>%
filter(FREQ == "Q",
== "XDC_R_B6G_S1M",
UNIT_MEASURE == "B9Z",
STO == "S1M") %>%
REF_SECTOR left_join(colors, by = c("Ref_area" = "country")) %>%
mutate(value = value/100) %>%
%>%
na.omit + geom_line(aes(x = date, y = value, color = color)) +
ggplot 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 left_join(CFT_var, by = "variable") %>%
left_join(REF_AREA, by = "REF_AREA") %>%
filter(FREQ == "Q",
== "XDC_R_B6G_S1M",
UNIT_MEASURE == "B9Z",
STO == "S1M") %>%
REF_SECTOR left_join(colors, by = c("Ref_area" = "country")) %>%
mutate(value = value/100) %>%
filter(date >= as.Date("2007-01-01")) %>%
%>%
na.omit + geom_line(aes(x = date, y = value, color = color)) +
ggplot 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 left_join(CFT_var, by = "variable") %>%
left_join(REF_AREA, by = "REF_AREA") %>%
filter(FREQ == "Q",
== "XDC_R_B6G_S1M",
UNIT_MEASURE == "B9Z",
STO == "S1M") %>%
REF_SECTOR left_join(colors, by = c("Ref_area" = "country")) %>%
mutate(value = value/100) %>%
filter(date >= as.Date("2015-01-01")) %>%
%>%
na.omit + geom_line(aes(x = date, y = value, color = color)) +
ggplot 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 left_join(CFT_var, by = "variable") %>%
left_join(REF_AREA, by = "REF_AREA") %>%
filter(FREQ == "Q",
%in% c("FR", "IT", "DE"),
REF_AREA == "XDC_R_B6G_S1M",
UNIT_MEASURE == "B8G",
STO == "S1M") %>%
REF_SECTOR left_join(colors, by = c("Ref_area" = "country")) %>%
mutate(value = value/100) %>%
%>%
na.omit + geom_line(aes(x = date, y = value, color = color)) +
ggplot 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 left_join(CFT_var, by = "variable") %>%
left_join(REF_AREA, by = "REF_AREA") %>%
filter(FREQ == "Q",
== "XDC_R_B6G_S1M",
UNIT_MEASURE == "B8G",
STO == "S1M") %>%
REF_SECTOR left_join(colors, by = c("Ref_area" = "country")) %>%
mutate(value = value/100) %>%
%>%
na.omit + geom_line(aes(x = date, y = value, color = color)) +
ggplot 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 left_join(CFT_var, by = "variable") %>%
left_join(REF_AREA, by = "REF_AREA") %>%
filter(INSTR_ASSET == "DETT",
== "XDC_R_B1GQ_CY",
UNIT_MEASURE == "S1M") %>%
REF_SECTOR 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()) %>%
+ geom_line(aes(x = date, y = value, color = color)) +
ggplot 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 left_join(CFT_var, by = "variable") %>%
left_join(REF_AREA, by = "REF_AREA") %>%
filter(REF_AREA %in% c("FR", "IT", "DE", "ES", "JP", "I8"),
== "DETT",
INSTR_ASSET == "XDC_R_B1GQ_CY",
UNIT_MEASURE == "S1M") %>%
REF_SECTOR mutate(Ref_area = ifelse(REF_AREA == "I8", "Europe", Ref_area)) %>%
left_join(colors, by = c("Ref_area" = "country")) %>%
mutate(value = value/100) %>%
+ geom_line(aes(x = date, y = value, color = color)) +
ggplot 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 left_join(CFT_var, by = "variable") %>%
left_join(REF_AREA, by = "REF_AREA") %>%
filter(REF_AREA %in% c("FR", "IT", "DE"),
== "DETT",
INSTR_ASSET == "XDC_R_B1GQ_CY",
UNIT_MEASURE == "S1M") %>%
REF_SECTOR mutate(Ref_area = ifelse(REF_AREA == "I8", "Europe", Ref_area)) %>%
left_join(colors, by = c("Ref_area" = "country")) %>%
mutate(value = value/100) %>%
+ geom_line(aes(x = date, y = value, color = color)) +
ggplot 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 left_join(CFT_var, by = "variable") %>%
left_join(REF_AREA, by = "REF_AREA") %>%
filter(REF_AREA %in% c("FR", "IT", "DE"),
== "DETT",
INSTR_ASSET == "XDC_R_B1GQ_CY",
UNIT_MEASURE == "S1M") %>%
REF_SECTOR 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")) %>%
+ geom_line(aes(x = date, y = value, color = color)) +
ggplot 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 left_join(CFT_var, by = "variable") %>%
left_join(REF_AREA, by = "REF_AREA") %>%
filter(REF_AREA %in% c("FR", "IT", "DE", "I8"),
== "DETT",
INSTR_ASSET == "XDC_R_B1GQ_CY",
UNIT_MEASURE == "S11") %>%
REF_SECTOR mutate(Ref_area = ifelse(REF_AREA == "I8", "Europe", Ref_area)) %>%
left_join(colors, by = c("Ref_area" = "country")) %>%
mutate(value = value/100) %>%
+ geom_line(aes(x = date, y = value, color = color)) +
ggplot 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 left_join(CFT_var, by = "variable") %>%
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)) %>%
+ geom_line(aes(x = date, y = value/100, color = Variable)) +
ggplot 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 left_join(CFT_var, by = "variable") %>%
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")) %>%
+ geom_line(aes(x = date, y = value/100, color = Variable)) +
ggplot 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 left_join(CFT_var, by = "variable") %>%
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")) %>%
+ geom_line(aes(x = date, y = value/100, color = Variable)) +
ggplot 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 left_join(CFT_var, by = "variable") %>%
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")) %>%
+ geom_line(aes(x = date, y = value/100, color = Variable)) +
ggplot 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 left_join(CFT_var, by = "variable") %>%
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")) %>%
+ geom_line(aes(x = date, y = value/100, color = Variable)) +
ggplot 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 left_join(CFT_var, by = "variable") %>%
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")) %>%
+ geom_line(aes(x = date, y = value/100, color = Variable)) +
ggplot 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 | .html | .RData |
---|---|---|---|
bdf | CFT | 2024-12-16 | 2024-12-09 |
insee | CNA-2014-CONSO-SI | 2024-12-22 | 2024-12-22 |
insee | CNA-2014-CSI | 2024-11-22 | 2024-12-22 |
insee | CNA-2014-FBCF-BRANCHE | 2024-12-22 | 2024-12-22 |
insee | CNA-2014-FBCF-SI | 2024-06-07 | 2024-12-22 |
insee | CNA-2014-RDB | 2024-12-22 | 2024-12-22 |
insee | CNA-2020-CONSO-MEN | 2024-11-22 | 2024-09-12 |
insee | CNA-2020-PIB | 2024-12-09 | 2024-09-11 |
insee | CNT-2014-CB | 2024-12-22 | 2024-12-22 |
insee | CNT-2014-CSI | 2024-12-16 | 2024-12-22 |
insee | CNT-2014-OPERATIONS | 2024-12-16 | 2024-12-22 |
insee | CNT-2014-PIB-EQB-RF | 2024-12-22 | 2024-12-22 |
insee | CONSO-MENAGES-2020 | 2024-11-22 | 2024-12-22 |
insee | conso-mensuelle | 2024-06-07 | 2023-07-04 |
insee | ICA-2015-IND-CONS | 2024-12-22 | 2024-12-22 |
insee | t_1101 | 2024-11-22 | 2022-01-02 |
insee | t_1102 | 2024-11-22 | 2020-10-30 |
insee | t_1105 | 2024-11-22 | 2020-10-30 |