Comptes Financiers Trimestriels

Data - BDF

Author

Info

source	dataset	.html	.RData
bdf	CFT	2024-09-30	2024-07-01

Méthodologie. pdf

Données sur la macroéconomie en France

source	dataset	.html	.RData
bdf	CFT	2024-09-30	2024-07-01
insee	CNA-2014-CONSO-MEN	2024-09-22	2024-09-22
insee	CNA-2014-CONSO-SI	2024-09-22	2024-09-22
insee	CNA-2014-CSI	2024-09-22	2024-09-22
insee	CNA-2014-FBCF-BRANCHE	2024-09-22	2024-09-22
insee	CNA-2014-FBCF-SI	2024-06-07	2024-09-22
insee	CNA-2014-PIB	2024-09-22	2024-09-22
insee	CNA-2014-RDB	2024-09-26	2024-09-26
insee	CNT-2014-CB	2024-09-22	2024-09-22
insee	CNT-2014-CSI	2024-09-22	2024-09-22
insee	CNT-2014-OPERATIONS	2024-09-22	2024-09-22
insee	CNT-2014-PIB-EQB-RF	2024-09-22	2024-09-22
insee	CONSO-MENAGES-2014	2024-09-22	2024-09-22
insee	conso-mensuelle	2024-06-07	2023-07-04
insee	ICA-2015-IND-CONS	2024-09-22	2024-09-22
insee	t_1101	2024-09-22	2022-01-02
insee	t_1102	2024-09-22	2020-10-30
insee	t_1105	2024-09-22	2020-10-30

Data on saving

source	dataset	.html	.RData
bdf	CFT	2024-09-30	2024-07-01
bea	T50100	2024-02-11	2024-03-13
fred	saving	2024-09-18	2024-09-18
oecd	NAAG	2024-04-16	2024-03-13
wdi	NY.GDS.TOTL.ZS	2022-09-27	2024-09-18
wdi	NY.GNS.ICTR.ZS	2022-09-27	2024-09-18

LAST_COMPILE

LAST_COMPILE
2024-09-30

Last

date	Nobs
2024-01-01	6

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, 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	13291
LE	Encours	9837
B8G	Taux d'épargne des ménages	944
B9Z	Taux d'épargne financière des ménages	803
K	Réévaluations et autres changements de volume	254
K5	Impact de valorisation	167
P51G	Formation brute de capital fixe	141

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	23942
A	Annuel	1495

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	13119
S	CVS	11782
Y	CVS/CJO	282
_Z	Non applicable	254

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	18214
DE	Germany	918
ES	Spain	918
IT	Italy	918
US	United States	918
GB	United Kingdom	818
UK	NA	797
I9	NA	672
I8	Euro area 19 (fixed composition)	668
JP	Japan	596

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)	24420
W2	Domestic (home or reference area)	1017

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)	14957
S11	Sociétés non financières	5672
S13	Administrations publiques	3719
S1V	Sociétés non-financières, ménages et NPISH	1089

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	24420
S124	des OPC non monétaires	1017

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	13089
L	Engagements	9713
B	Balance (Crédit moins débit)	1747
N	Net (avoirs moins engagements)	498
NE	Engagements Nets (engagements moins avoirs)	249
D	Débit (dépenses)	141

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	13512
_Z	Non applicable	11925

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	16855
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	3956
XDC_R_B6G_S1M	en % du RDB	2595
XDC_R_B1G_CY	NA	952
XDC_R_DEBT	NA	692
PC	Pourcent	387

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	25437

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	24048
F	Valeur nominale (F)	789
N	Valeur nominale (N)	600

date

Code

CFT %>%
  group_by(date) %>%
  summarise(Nobs = n()) %>%
  arrange(desc(date)) %>%
  print_table_conditional

Grandes masses

Table

Code

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

Produits de taux

Code

CFT %>%
  left_join(CFT_var, by = "variable") %>%
  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")

Produits de fonds propres

Code

CFT %>%
  left_join(CFT_var, by = "variable") %>%
  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")

Côté, non côté

Code

CFT %>%
  left_join(CFT_var, by = "variable") %>%
  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")

Detail

Code

CFT %>%
  left_join(CFT_var, by = "variable") %>%
  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")

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"),
         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€"))

Stocks

Code

CFT %>%
  left_join(CFT_var, by = "variable") %>%
  left_join(INSTR_ASSET, by = "INSTR_ASSET") %>%
  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")

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"),
         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")

2019-

Code

CFT %>%
  left_join(CFT_var, by = "variable") %>%
  left_join(INSTR_ASSET, by = "INSTR_ASSET") %>%
  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")

Flux - 4 trimestres

Code

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

Flux

All

Code

CFT %>%
  left_join(CFT_var, by = "variable") %>%
  left_join(INSTR_ASSET, by = "INSTR_ASSET") %>%
  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 %>%
  left_join(CFT_var, by = "variable") %>%
  left_join(INSTR_ASSET, by = "INSTR_ASSET") %>%
  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 %>%
  left_join(CFT_var, by = "variable") %>%
  left_join(INSTR_ASSET, by = "INSTR_ASSET") %>%
  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")

Flux

Code

CFT %>%
  left_join(CFT_var, by = "variable") %>%
  left_join(INSTR_ASSET, by = "INSTR_ASSET") %>%
  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 %>%
  left_join(CFT_var, by = "variable") %>%
  left_join(INSTR_ASSET, by = "INSTR_ASSET") %>%
  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")

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 %>%
  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 %>%
  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")) %>%
  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.

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 %>%
  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 %>%
  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 %>%
  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 %>%
  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")) %>%
  ggplot + geom_line(aes(x = date, y = value/100, color = Variable)) + 
  xlab("") + ylab("") + theme_minimal() +
  scale_x_date(breaks = "2 years",
               labels = date_format("%Y")) +
  scale_y_continuous(breaks = 0.01*seq(-10, 100, 1),
                labels = percent_format(accuracy = 1)) +
  theme(legend.position = c(0.45, 0.9),
        legend.title = element_blank(),
        legend.direction = "vertical")

France, Spain, Italy

Table

Code

load_data("bdf/REF_AREA.RData")
CFT %>%
  left_join(CFT_var, by = "variable") %>%
  left_join(REF_AREA, by = "REF_AREA") %>%
  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
Dette des ménages - Zone euro, en % du RDB	Euro area 19 (fixed composition)	94.161
Dette des ménages - France, en % du RDB	France	99.078
Taux d'épargne des ménages, flux cumulé sur 4 trimestres - France, en % du RDB	France	15.690
Taux d'épargne financière des ménages, flux cumulé sur 4 trimestres - France, en % du RDB	France	6.196
Dette des ménages - Allemagne, en % du RDB	Germany	86.421
Taux d'épargne des ménages, flux cumulé sur 4 trimestres - Allemagne, en % du RDB	Germany	18.965
Taux d'épargne financière des ménages, flux cumulé sur 4 trimestres - Allemagne, en % du RDB	Germany	9.047
Dette des ménages -Italie, en % du RDB	Italy	61.985
Taux d'épargne des ménages, flux cumulé sur 4 trimestres - Italie, en % du RDB	Italy	11.305
Taux d'épargne financière des ménages, flux cumulé sur 4 trimestres - Italie, en % du RDB	Italy	3.914
Dette des ménages - Japon, en % du RDB	Japan	104.604
Dette des ménages -Espagne, en % du RDB	Spain	89.562
Taux d'épargne des ménages, flux cumulé sur 4 trimestres - Espagne, en % du RDB	Spain	10.046
Taux d'épargne financière des ménages, flux cumulé sur 4 trimestres - Espagne, en % du RDB	Spain	4.668
Taux d'épargne des ménages, flux cumulé sur 4 trimestres - Royaume-Uni, en % du RDB	United Kingdom	5.840
Taux d'épargne financière des ménages, flux cumulé sur 4 trimestres - Royaume-Uni, en % du RDB	United Kingdom	-0.987
Dette des ménages -Royaume-Uni, en % du RDB	United Kingdom	131.346
Dette des ménages - Etats-Unis, en % du RDB	United States	131.713
Taux d'épargne des ménages, flux cumulé sur 4 trimestres - Etats-Unis, en % du RDB	United States	11.602
Taux d'épargne financière des ménages, flux cumulé sur 4 trimestres - Etats-Unis, en % du RDB	United States	6.101
Dette des ménages - Zone euro, en % du RDB	NA	94.036
Dette des ménages -Royaume-Uni, en % du RDB	NA	128.203
Taux d'épargne des ménages, flux cumulé sur 4 trimestres - Royaume-Uni, en % du RDB	NA	5.415
Taux d'épargne financière des ménages, flux cumulé sur 4 trimestres - Royaume-Uni, en % du RDB	NA	-1.397

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

Taux d’épargne

France, Italy, Germany

Code

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

Dette

Dette des ménages

All

Code

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

Taux d’endettement

France

Années de PIB

Code

CFT %>%
  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)) %>%
  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 %>%
  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")) %>%
  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 %>%
  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")) %>%
  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 %>%
  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")) %>%
  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 %>%
  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")) %>%
  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 %>%
  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")) %>%
  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")