Quarterly Sector Accounts - QSA

Data - ECB

Author

François Geerolf 

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Info

source dataset .html .qmd .RData
ecb QSA [2024-09-19] https://fgee olf.com/data

Data on monetary policy

source dataset Title Download Compile
ecb SUP Supervisory Banking Statistics 2024-10-08 [2024-09-19]
bdf FM Marché financier, taux 2024-06-18 [2024-07-26]
bdf MIR Taux d'intérêt - Zone euro 2024-07-01 [2024-07-26]
bdf MIR1 Taux d'intérêt - France 2024-07-01 [2024-07-26]
bis CBPOL Policy Rates, Daily 2024-09-15 [2024-08-09]
ecb BSI Balance Sheet Items 2024-09-16 [2024-10-08]
ecb BSI_PUB Balance Sheet Items - Published series 2024-10-08 [2024-10-08]
ecb FM Financial market data 2024-10-08 [2024-10-08]
ecb ILM Internal Liquidity Management 2024-10-08 [2024-10-08]
ecb ILM_PUB Internal Liquidity Management - Published series 2024-09-10 [2024-10-08]
ecb MIR MFI Interest Rate Statistics 2024-10-08 [2024-06-19]
ecb RAI Risk Assessment Indicators 2024-10-08 [2024-09-19]
ecb YC Financial market data - yield curve 2024-09-16 [2024-09-19]
ecb YC_PUB Financial market data - yield curve - Published series 2024-10-08 [2024-09-19]
ecb liq_daily Daily Liquidity 2024-09-11 [2024-10-08]
eurostat ei_mfir_m Interest rates - monthly data 2024-09-15 [2024-09-30]
eurostat irt_st_m Money market interest rates - monthly data 2024-10-08 [2024-09-30]
fred r Interest Rates 2024-09-18 [2024-09-18]
oecd MEI Main Economic Indicators 2024-06-30 [2024-04-16]
oecd MEI_FIN Monthly Monetary and Financial Statistics (MEI) 2024-05-21 [2024-09-15]

LAST_COMPILE

LAST_COMPILE
2024-10-09

Last

Code 
QSA %>%
  group_by(TIME_PERIOD) %>%
  summarise(Nobs = n()) %>%
  arrange(desc(TIME_PERIOD)) %>%
  head(1) %>%
  print_table_conditional()
TIME_PERIOD Nobs
2024-Q1 278422

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 .}
ADJUSTMENT Adjustment Nobs
N Neither seasonally nor working day adjusted 21916976
Y Working day and seasonally adjusted 135241

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 .}
COUNTERPART_AREA Counterpart_area Nobs
W0 Intra-EU (changing composition) not allocated 12210389
W2 Intra-Euro area not allocated 7857158
W1 Gaza and Jericho 1972222
4Y All European Community Institutions, Organs and Organisms, including ECB, ESM and EFSF 5364
D0 EU (changing composition) 2142
B0 Emerging and developing economies 2140
U2 Euro area (changing composition) 1402
U4 Extra Euro area 1400

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 .}
COUNTERPART_SECTOR Counterpart_sector Nobs
S1 Total economy 14884323
S12 Financial corporations 677919
S124 Non MMF investment funds 677380
S12P Other financial institutions (Financial corporations other than MFIs, insurance corporations and pension funds) 671052
S12K Monetary financial institutions (MFI) 657423
S11 Non financial corporations 610389
S12O Other financial institutions (Financial corporations other than MFIs, insurance corporations, pension funds and non MMFs investment funds) 609888
S128 Insurance corporations 608871
S13 General government 606600
S12Q Insurance corporations and Pension Funds 603602
S129 Pension funds 601833
S1M Households and non profit institutions serving households (NPISH) 539710
S121 Central bank 149577
S12T Monetary financial institutions other than central bank 148752
S1V Non-financial corporations, households and NPISH 4898

EXPENDITURE

Code 
QSA %>%
  left_join(EXPENDITURE,  by = "EXPENDITURE") %>%
  group_by(EXPENDITURE, Expenditure) %>%
  summarise(Nobs = n()) %>%
  arrange(-Nobs) %>%
  {if (is_html_output()) print_table(.) else .}
EXPENDITURE Expenditure Nobs
_Z Not applicable 21925469
_T Total 126748

FREQ

Code 
QSA %>%
  left_join(FREQ,  by = "FREQ") %>%
  group_by(FREQ, Freq) %>%
  summarise(Nobs = n()) %>%
  arrange(-Nobs) %>%
  {if (is_html_output()) print_table(.) else .}
FREQ Freq Nobs
Q Quarterly 21997662
A Annual 54555

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 .}
INSTR_ASSET Instr_asset Nobs
F4 NA 4733314
F3 Debt securities 4546963
_Z NA 1395441
F511 Listed shares 1370030
F2M Deposits 756886
F NA 685717
F52 Investment fund shares/units 668608
F81 NA 515040
F89 NA 514583
F51M Unlisted shares and other equity 507506
F6 NA 357662
F5 NA 353543
F8 NA 353507
F7 NA 352767
F51 NA 347722
F6M NA 335598
F519 NA 330339
F6N NA 328774
F512 NA 312283
F6O NA 309855
F6P NA 299642
F2 NA 250914
F21 NA 233900
F522 NA 222265
F62 Life insurance and annuity entitlements 220725
F521 NA 218678
F22 NA 217335
F29 NA 217311
F62B NA 165515
F63 NA 136484
F62A NA 131419
F63B NA 121119
F63A NA 120280
F1 NA 114783
F11 NA 114474
F12 NA 114466
F3T4 NA 24555
FPT NA 14561
FP NA 12744
FR0 NA 5389
NUN Housing wealth (net) 3866
FX4 NA 3276
NYN NA 2388
N11G NA 2210
N11N NA 2210
N111G NA 884
N111N NA 884
N112G NA 442
N112N NA 442
N11LG NA 442
N11LN NA 442
N11MG NA 442
N11MN NA 442
N21111 NA 442
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 .}
PRICES Prices Nobs
V Current prices 22008599
L Chain linked volume 18415
_Z Not applicable 14849
D Deflator (index) 7984
LR Chain linked volume (rebased) 1185
Y Previous year prices 1185

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, 2030, 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, 2030, 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