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Error in readChar(con, 5L, useBytes = TRUE) : cannot open the connection
Error in readChar(con, 5L, useBytes = TRUE) : cannot open the connection
Error in readChar(con, 5L, useBytes = TRUE) : cannot open the connection
Error in readChar(con, 5L, useBytes = TRUE) : cannot open the connection
Error in readChar(con, 5L, useBytes = TRUE) : cannot open the connection
Error in readChar(con, 5L, useBytes = TRUE) : cannot open the connection
Error in readChar(con, 5L, useBytes = TRUE) : cannot open the connection
Error in readChar(con, 5L, useBytes = TRUE) : cannot open the connection
Info
Last
Code
QSA %>%
group_by(TIME_PERIOD) %>%
summarise(Nobs = n()) %>%
arrange(desc(TIME_PERIOD)) %>%
head(1) %>%
print_table_conditional()
Other info
- Households and non-financial corporations in the euro area: first quarter of 2023. html
ADJUSTMENT
Code
QSA %>%
left_join(ADJUSTMENT, by = "ADJUSTMENT") %>%
group_by(ADJUSTMENT, Adjustment) %>%
summarise(Nobs = n()) %>%
arrange(-Nobs) %>%
{if (is_html_output()) print_table(.) else .}
N |
Neither seasonally nor working day adjusted |
26273505 |
Y |
Working day and seasonally adjusted |
136388 |
COUNTERPART_AREA
Code
QSA %>%
left_join(COUNTERPART_AREA, by = "COUNTERPART_AREA") %>%
group_by(COUNTERPART_AREA, Counterpart_area) %>%
summarise(Nobs = n()) %>%
arrange(-Nobs) %>%
{if (is_html_output()) print_table(.) else .}
W0 |
Intra-EU (changing composition) not allocated |
12986512 |
W2 |
Intra-Euro area not allocated |
11229410 |
W1 |
Gaza and Jericho |
2181428 |
4Y |
All European Community Institutions, Organs and Organisms, including ECB, ESM and EFSF |
5407 |
B0 |
Emerging and developing economies |
2154 |
D0 |
EU (changing composition) |
2154 |
U2 |
Euro area (changing composition) |
1414 |
U4 |
Extra Euro area |
1414 |
COUNTERPART_SECTOR
Code
QSA %>%
left_join(COUNTERPART_SECTOR, by = "COUNTERPART_SECTOR") %>%
group_by(COUNTERPART_SECTOR, Counterpart_sector) %>%
summarise(Nobs = n()) %>%
arrange(-Nobs) %>%
{if (is_html_output()) print_table(.) else .}
S1 |
Total economy |
16022913 |
S12 |
Financial corporations |
831202 |
S12P |
Other financial institutions (Financial corporations other than MFIs, insurance corporations and pension funds) |
821483 |
S124 |
Non MMF investment funds |
815257 |
S12K |
Monetary financial institutions (MFI) |
800848 |
S11 |
Non financial corporations |
743746 |
S12O |
Other financial institutions (Financial corporations other than MFIs, insurance corporations, pension funds and non MMFs investment funds) |
741421 |
S128 |
Insurance corporations |
735106 |
S13 |
General government |
734697 |
S12Q |
Insurance corporations and Pension Funds |
730079 |
S129 |
Pension funds |
729030 |
S1M |
Households and non profit institutions serving households (NPISH) |
649850 |
S126 |
Financial auxiliaries |
578483 |
S125 |
Other financial intermediaries, except insurance corporations and pension funds |
578340 |
S127 |
Captive financial institutions and money lenders |
577935 |
S121 |
Central bank |
158130 |
S12T |
Monetary financial institutions other than central bank |
156390 |
S1V |
Non-financial corporations, households and NPISH |
4983 |
EXPENDITURE
Code
QSA %>%
left_join(EXPENDITURE, by = "EXPENDITURE") %>%
group_by(EXPENDITURE, Expenditure) %>%
summarise(Nobs = n()) %>%
arrange(-Nobs) %>%
{if (is_html_output()) print_table(.) else .}
_Z |
Not applicable |
26282097 |
_T |
Total |
127796 |
FREQ
Code
QSA %>%
left_join(FREQ, by = "FREQ") %>%
group_by(FREQ, Freq) %>%
summarise(Nobs = n()) %>%
arrange(-Nobs) %>%
{if (is_html_output()) print_table(.) else .}
Q |
Quarterly |
26355182 |
A |
Annual |
54711 |
INSTR_ASSET
Code
QSA %>%
left_join(INSTR_ASSET, by = "INSTR_ASSET") %>%
group_by(INSTR_ASSET, Instr_asset) %>%
summarise(Nobs = n()) %>%
arrange(-Nobs) %>%
{if (is_html_output()) print_table(.) else .}
F4 |
NA |
6312614 |
F3 |
Debt securities |
6032437 |
F511 |
Listed shares |
1861477 |
_Z |
NA |
1406125 |
F2M |
Deposits |
851307 |
F52 |
Investment fund shares/units |
785462 |
F |
NA |
748149 |
F81 |
NA |
568745 |
F89 |
NA |
567887 |
F51M |
Unlisted shares and other equity |
560092 |
F6 |
NA |
372731 |
F8 |
NA |
371890 |
F7 |
NA |
370166 |
F5 |
NA |
369835 |
F51 |
NA |
364086 |
F6M |
NA |
349040 |
F519 |
NA |
345017 |
F6N |
NA |
341874 |
F512 |
NA |
326953 |
F6O |
NA |
323920 |
F6P |
NA |
308858 |
F2 |
NA |
261670 |
F21 |
NA |
242831 |
F522 |
NA |
231557 |
F62 |
Life insurance and annuity entitlements |
229691 |
F29 |
NA |
226969 |
F22 |
NA |
226574 |
F521 |
NA |
223453 |
F62B |
NA |
183853 |
F63 |
NA |
165985 |
F62A |
NA |
155707 |
F63B |
NA |
146149 |
F63A |
NA |
144816 |
F1 |
NA |
118437 |
F12 |
NA |
118120 |
F11 |
NA |
117073 |
F3T4 |
NA |
25220 |
FPT |
NA |
14981 |
FP |
NA |
13144 |
FR0 |
NA |
5389 |
NUN |
Housing wealth (net) |
3868 |
FX4 |
NA |
3276 |
NYN |
NA |
2433 |
N11G |
NA |
2220 |
N11N |
NA |
2220 |
N111G |
NA |
888 |
N111N |
NA |
888 |
N112G |
NA |
444 |
N112N |
NA |
444 |
N11LG |
NA |
444 |
N11LN |
NA |
444 |
N11MG |
NA |
444 |
N11MN |
NA |
444 |
N21111 |
NA |
444 |
F2B |
NA |
118 |
F2MF |
NA |
118 |
F3F |
NA |
118 |
F3M |
NA |
118 |
FF |
NA |
118 |
FM |
NA |
118 |
PRICES
Code
QSA %>%
left_join(PRICES, by = "PRICES") %>%
group_by(PRICES, Prices) %>%
summarise(Nobs = n()) %>%
arrange(-Nobs) %>%
{if (is_html_output()) print_table(.) else .}
V |
Current prices |
26366234 |
L |
Chain linked volume |
18416 |
_Z |
Not applicable |
14879 |
D |
Deflator (index) |
7984 |
LR |
Chain linked volume (rebased) |
1190 |
Y |
Previous year prices |
1190 |
REF_AREA
Code
QSA %>%
left_join(REF_AREA, by = "REF_AREA") %>%
group_by(REF_AREA, Ref_area) %>%
summarise(Nobs = n()) %>%
arrange(-Nobs) %>%
{if (is_html_output()) datatable(., filter = 'top', rownames = F) else .}
REF_SECTOR
Code
QSA %>%
left_join(REF_SECTOR, by = "REF_SECTOR") %>%
group_by(REF_SECTOR, Ref_sector) %>%
summarise(Nobs = n()) %>%
arrange(-Nobs) %>%
{if (is_html_output()) datatable(., filter = 'top', rownames = F) else .}
STO
Code
QSA %>%
left_join(STO, by = "STO") %>%
group_by(STO, Sto) %>%
summarise(Nobs = n()) %>%
arrange(-Nobs) %>%
{if (is_html_output()) datatable(., filter = 'top', rownames = F) else .}
Loans granted to households as % of GDP
QSA.Q.N.BG.W0.S1M.S1.N.L.LE.F4.T._Z.XDC_R_B1GQ_CY._T.S.V.N._T QSA.Q.N.SE.W0.S1V.S1.N.L.F.F3T4.T._Z.XDC_R_B1GQ_CY._T.S.V.CY._T
Loans granted to households as a ratio of GDP
Loans granted to households as a ratio of GDP: QSA.Q.N.I9.W0.S1M.S1.N.L.LE.F4.T._Z.XDC_R_B1GQ_CY._T.S.V.N._T
Loans granted to households as a % of GDI
Loans granted to households as a ratio of gross disposable income
QSA.Q.N.AT.W0.S1M.S1.N.L.LE.F4.T._Z.XDC_R_B6G_CY._T.S.V.N._T
Adjusted loans
Euro area Non Financial corporations (NFCs)
Code
QSA %>%
filter(KEY %in% c("QSA.Q.N.FR.W0.S1M.S1.N.L.LE.F4.T._Z.XDC_R_B6G_CY._T.S.V.N._T",
"QSA.Q.N.DE.W0.S1M.S1.N.L.LE.F4.T._Z.XDC_R_B6G_CY._T.S.V.N._T",
"QSA.Q.N.IT.W0.S1M.S1.N.L.LE.F4.T._Z.XDC_R_B6G_CY._T.S.V.N._T")) %>%
quarter_to_date %>%
left_join(REF_AREA, by = "REF_AREA") %>%
mutate(OBS_VALUE = OBS_VALUE/100) %>%
left_join(colors, by = c("Ref_area" = "country")) %>%
ggplot + geom_line(aes(x = date, y = OBS_VALUE, color = color)) +
ylab("Adjusted loans vs. € area NFCs, annual growth") + xlab("") + theme_minimal() +
add_flags(3) + scale_color_identity() +
theme(legend.position = c(0.45, 0.9),
legend.title = element_blank()) +
scale_y_continuous(breaks = 0.01*seq(-100, 300, 25),
labels = scales::percent_format(accuracy = 1)) +
scale_x_date(breaks = as.Date(paste0(seq(1940, 2100, 2), "-01-01")),
labels = date_format("%Y"))
QSA.Q.N.I9.W0.S1M.S1.N.L.LE.F4.T._Z.XDC_R_B6GA_CY._T.S.V.N._T
Households
Code
QSA %>%
filter(KEY %in% c("QSA.Q.N.AT.W0.S1M.S1.N.L.LE.F4.T._Z.XDC_R_B6G_CY._T.S.V.N._T",
"QSA.Q.N.DE.W0.S1M.S1.N.L.LE.F4.T._Z.XDC_R_B6G_CY._T.S.V.N._T",
"QSA.Q.N.IT.W0.S1M.S1.N.L.LE.F4.T._Z.XDC_R_B6G_CY._T.S.V.N._T")) %>%
quarter_to_date %>%
left_join(REF_AREA, by = "REF_AREA") %>%
mutate(OBS_VALUE = OBS_VALUE/100) %>%
left_join(colors, by = c("Ref_area" = "country")) %>%
ggplot + geom_line(aes(x = date, y = OBS_VALUE, color = color)) +
ylab("Adjusted loans vs. € area NFCs, annual growth") + xlab("") + theme_minimal() +
add_flags(3) + scale_color_identity() +
theme(legend.position = c(0.45, 0.9),
legend.title = element_blank()) +
scale_y_continuous(breaks = 0.01*seq(-100, 300, 25),
labels = scales::percent_format(accuracy = 1)) +
scale_x_date(breaks = as.Date(paste0(seq(1940, 2100, 2), "-01-01")),
labels = date_format("%Y"))
Total financial liabilities of Non financial corporations
Non-financial corporations’ financing increased at lower annual rate of 1.5%, after 2.0%
QSA.Q.N.I9.W0.S11.S1.N.L.F.F._Z._Z.XDC._T.S.V.N._T