Quarterly Sector Accounts - QSA

Data - ECB

Author

François Geerolf 

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Info

source dataset .html .qmd .RData
ecb QSA [2024-06-30] https://fgee olf.com/data

Data on monetary policy

source dataset Title Download Compile
ecb SUP Supervisory Banking Statistics 2024-07-01 [2024-06-30]
bdf FM Marché financier, taux 2024-06-18 [2024-07-01]
bdf MIR Taux d'intérêt - Zone euro 2024-07-01 [2024-07-01]
bdf MIR1 Taux d'intérêt - France 2024-07-01 [2024-07-01]
bis CBPOL Policy Rates, Daily 2024-07-01 [2024-06-19]
ecb BSI Balance Sheet Items 2024-05-21 [2024-07-01]
ecb BSI_PUB Balance Sheet Items - Published series 2024-07-01 [2024-07-01]
ecb FM Financial market data 2024-07-01 [2024-07-01]
ecb ILM Internal Liquidity Management 2024-07-01 [2024-07-01]
ecb ILM_PUB Internal Liquidity Management - Published series 2024-01-25 [2024-07-01]
ecb MIR MFI Interest Rate Statistics 2024-07-01 [2024-06-19]
ecb RAI Risk Assessment Indicators 2024-07-01 [2024-06-07]
ecb YC Financial market data - yield curve 2024-05-21 [2024-06-30]
ecb YC_PUB Financial market data - yield curve - Published series 2024-07-01 [2024-06-30]
ecb liq_daily Daily Liquidity 2024-05-21 [2024-07-01]
eurostat ei_mfir_m Interest rates - monthly data 2024-06-30 [2024-07-01]
eurostat irt_st_m Money market interest rates - monthly data 2024-07-01 [2024-07-01]
fred r Interest Rates 2024-07-01 [2024-07-01]
oecd MEI Main Economic Indicators 2024-06-30 [2024-04-16]
oecd MEI_FIN Monthly Monetary and Financial Statistics (MEI) 2024-05-21 [2024-07-01]

LAST_COMPILE

LAST_COMPILE
2024-07-02

Last

Code 
QSA %>%
  group_by(TIME_PERIOD) %>%
  summarise(Nobs = n()) %>%
  arrange(desc(TIME_PERIOD)) %>%
  head(1) %>%
  print_table_conditional()
TIME_PERIOD Nobs
2023-Q4 286101

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 21616702
Y Working day and seasonally adjusted 134119

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 12041093
W2 Intra-Euro area not allocated 7749291
W1 Gaza and Jericho 1948052
4Y All European Community Institutions, Organs and Organisms, including ECB, ESM and EFSF 5313
B0 Emerging and developing economies 2136
D0 EU (changing composition) 2136
U2 Euro area (changing composition) 1400
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 14681663
S12 Financial corporations 668859
S124 Non MMF investment funds 668383
S12P Other financial institutions (Financial corporations other than MFIs, insurance corporations and pension funds) 662063
S12K Monetary financial institutions (MFI) 648463
S11 Non financial corporations 602350
S12O Other financial institutions (Financial corporations other than MFIs, insurance corporations, pension funds and non MMFs investment funds) 601849
S128 Insurance corporations 600839
S13 General government 598645
S12Q Insurance corporations and Pension Funds 595566
S129 Pension funds 593843
S1M Households and non profit institutions serving households (NPISH) 532721
S121 Central bank 145748
S12T Monetary financial institutions other than central bank 144984
S1V Non-financial corporations, households and NPISH 4845

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 21625157
_T Total 125664

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 21696317
A Annual 54504

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 4677286
F3 Debt securities 4475509
_Z NA 1384732
F511 Listed shares 1349340
F2M Deposits 746566
F NA 675437
F52 Investment fund shares/units 659518
F81 NA 506763
F89 NA 506312
F51M Unlisted shares and other equity 499428
F6 NA 352866
F5 NA 348820
F8 NA 348698
F7 NA 347968
F51 NA 342994
F6M NA 331120
F519 NA 325961
F6N NA 324376
F512 NA 308090
F6O NA 305689
F6P NA 295366
F2 NA 247705
F21 NA 230836
F522 NA 219445
F62 Life insurance and annuity entitlements 217825
F521 NA 215813
F22 NA 214506
F29 NA 214484
F62B NA 162749
F63 NA 133852
F62A NA 128654
F63B NA 118626
F63A NA 117732
F1 NA 113401
F11 NA 113092
F12 NA 113084
F3T4 NA 24297
FPT NA 14425
FP NA 12636
FR0 NA 5389
NUN Housing wealth (net) 3864
FX4 NA 3276
NYN NA 2343
N11G NA 2200
N11N NA 2200
N111G NA 880
N111N NA 880
N112G NA 440
N112N NA 440
N11LG NA 440
N11LN NA 440
N11MG NA 440
N11MN NA 440
N21111 NA 440
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 21707262
L Chain linked volume 18414
_Z Not applicable 14811
D Deflator (index) 7984
LR Chain linked volume (rebased) 1175
Y Previous year prices 1175

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