Quarterly Sector Accounts - QSA

Data - ECB

Author

François Geerolf 

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Info

source dataset .html .RData
ecb QSA 2025-08-26 2024-11-19

Data on monetary policy

source dataset .html .RData
bdf FM 2025-08-25 2025-08-24
bdf MIR 2025-08-25 2025-08-04
bdf MIR1 2025-08-25 2025-08-04
bis CBPOL 2025-08-25 2024-12-29
ecb BSI 2025-08-26 NA
ecb BSI_PUB 2025-08-26 2025-08-24
ecb FM 2025-08-26 2025-08-26
ecb ILM 2025-08-26 2025-08-24
ecb ILM_PUB 2025-08-26 2024-09-10
ecb liq_daily 2025-08-26 2025-06-06
ecb MIR 2025-08-26 2025-08-24
ecb RAI 2025-08-26 2025-08-24
ecb SUP 2025-08-26 2025-08-24
ecb YC 2025-08-26 2025-08-21
ecb YC_PUB 2025-08-26 2025-08-24
eurostat ei_mfir_m 2025-08-25 2025-08-24
eurostat irt_st_m 2025-08-25 2025-08-24
fred r 2025-08-25 2025-08-24
oecd MEI 2024-04-16 2025-07-24
oecd MEI_FIN 2024-09-15 2025-07-24

LAST_COMPILE

LAST_COMPILE
2025-08-26

Last

Code 
QSA %>%
  group_by(TIME_PERIOD) %>%
  summarise(Nobs = n()) %>%
  arrange(desc(TIME_PERIOD)) %>%
  head(1) %>%
  print_table_conditional()
TIME_PERIOD Nobs
2024-Q2 339182

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 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 .}
COUNTERPART_AREA Counterpart_area Nobs
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 .}
COUNTERPART_SECTOR Counterpart_sector Nobs
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 .}
EXPENDITURE Expenditure Nobs
_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 .}
FREQ Freq Nobs
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 .}
INSTR_ASSET Instr_asset Nobs
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 .}
PRICES Prices Nobs
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