Balance Sheet Items

Data - ECB

Author

François Geerolf

Info

source dataset .html .RData
ecb BSI 2024-10-08 2024-11-19

Information

  • Link to SDW. html

  • Data Structure Definition. (DSD) html

Data on monetary policy

source dataset .html .RData
bdf FM 2024-11-18 2024-11-18
bdf MIR 2024-07-26 2024-07-01
bdf MIR1 2024-10-16 2024-10-16
bis CBPOL 2024-08-09 2024-09-15
ecb BSI 2024-10-08 2024-11-19
ecb BSI_PUB 2024-11-19 2024-11-19
ecb FM 2024-11-18 2024-11-18
ecb ILM 2024-10-08 2024-11-11
ecb ILM_PUB 2024-10-08 2024-09-10
ecb liq_daily 2024-10-08 2024-09-11
ecb MIR 2024-06-19 2024-11-18
ecb RAI 2024-10-08 2024-10-30
ecb SUP 2024-10-08 2024-10-08
ecb YC 2024-11-19 2024-11-19
ecb YC_PUB 2024-10-08 2024-10-08
eurostat ei_mfir_m 2024-11-18 2024-11-18
eurostat irt_st_m 2024-11-18 2024-11-18
fred r 2024-11-18 2024-11-18
oecd MEI 2024-04-16 2024-06-30
oecd MEI_FIN 2024-09-15 2024-11-18

LAST_COMPILE

LAST_COMPILE
2024-11-19

Last

Code 
BSI %>%
  group_by(TIME_PERIOD, FREQ) %>%
  summarise(Nobs = n()) %>%
  arrange(desc(TIME_PERIOD)) %>%
  head(5) %>%
  print_table_conditional()
TIME_PERIOD FREQ Nobs
2024-Q3 Q 1248
2024-Q2 Q 18931
2024-Q1 Q 18931
2024-12 M 1
2024-10 M 44

FREQ

Code 
BSI %>%
  left_join(FREQ,  by = "FREQ") %>%
  group_by(FREQ, Freq) %>%
  summarise(Nobs = n()) %>%
  arrange(-Nobs) %>%
  {if (is_html_output()) print_table(.) else .}
FREQ Freq Nobs
M Monthly 5963672
Q Quarterly 1284775
A Annual 109

REF_AREA

Code 
BSI %>%
  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 .}

ADJUSTMENT

Code 
BSI %>%
  left_join(ADJUSTMENT,  by = "ADJUSTMENT") %>%
  group_by(ADJUSTMENT, Adjustment) %>%
  summarise(Nobs = n()) %>%
  arrange(-Nobs) %>%
  {if (is_html_output()) datatable(., filter = 'top', rownames = F) else .}

BS_REP_SECTOR

Code 
BSI %>%
  left_join(BS_REP_SECTOR,  by = "BS_REP_SECTOR") %>%
  group_by(BS_REP_SECTOR, Bs_rep_sector) %>%
  summarise(Nobs = n()) %>%
  arrange(-Nobs) %>%
  {if (is_html_output()) datatable(., filter = 'top', rownames = F) else .}

BS_ITEM

Code 
BSI %>%
  left_join(BS_ITEM,  by = "BS_ITEM") %>%
  group_by(BS_ITEM, Bs_item) %>%
  summarise(Nobs = n()) %>%
  {if (is_html_output()) datatable(., filter = 'top', rownames = F) else .}

MATURITY_ORIG

Code 
BSI %>%
  left_join(MATURITY_ORIG,  by = "MATURITY_ORIG") %>%
  group_by(MATURITY_ORIG, Maturity_orig) %>%
  summarise(Nobs = n()) %>%
  {if (is_html_output()) datatable(., filter = 'top', rownames = F) else .}

DATA_TYPE

Code 
BSI %>%
  left_join(DATA_TYPE,  by = "DATA_TYPE") %>%
  group_by(DATA_TYPE, Data_type) %>%
  summarise(Nobs = n()) %>%
  {if (is_html_output()) datatable(., filter = 'top', rownames = F) else .}

COUNT_AREA

Code 
BSI %>%
  left_join(COUNT_AREA,  by = "COUNT_AREA") %>%
  group_by(COUNT_AREA, Count_area) %>%
  summarise(Nobs = n()) %>%
  {if (is_html_output()) datatable(., filter = 'top', rownames = F) else .}

BS_COUNT_SECTOR

Code 
BSI %>%
  left_join(BS_COUNT_SECTOR,  by = "BS_COUNT_SECTOR") %>%
  group_by(BS_COUNT_SECTOR, Bs_count_sector) %>%
  summarise(Nobs = n()) %>%
  {if (is_html_output()) datatable(., filter = 'top', rownames = F) else .}

BS_SUFFIX

Code 
BSI %>%
  left_join(BS_SUFFIX,  by = "BS_SUFFIX") %>%
  group_by(BS_SUFFIX, Bs_suffix) %>%
  summarise(Nobs = n()) %>%
  {if (is_html_output()) datatable(., filter = 'top', rownames = F) else .}

COLLECTION

Code 
BSI %>%
  left_join(COLLECTION,  by = "COLLECTION") %>%
  group_by(COLLECTION, Collection) %>%
  summarise(Nobs = n()) %>%
  {if (is_html_output()) datatable(., filter = 'top', rownames = F) else .}

Floating rates data

France

Households

Code 
BSI %>%
  filter(REF_AREA %in% c("FR"),
         BS_COUNT_SECTOR == "2250",
         MATURITY_ORIG %in% c("KF", "HL", "KKF", "HHL")) %>%
  quarter_to_date %>%
  arrange(desc(date)) %>%
  left_join(MATURITY_ORIG, by = "MATURITY_ORIG") %>%
  mutate(OBS_VALUE = OBS_VALUE/1000) %>%
  ggplot + geom_line(aes(x = date, y = OBS_VALUE, color = Maturity_orig)) +
  ylab("") + xlab("") + theme_minimal() +
  theme(legend.title = element_blank(),
        legend.position = "bottom",
        legend.direction = "vertical") +
  scale_y_continuous(breaks = seq(0, 1000, 20)) +
  scale_x_date(breaks = as.Date(paste0(seq(1940, 2030, 2), "-01-01")),
               labels = date_format("%Y"))

Corporations

Code 
BSI %>%
  filter(REF_AREA %in% c("FR"),
         BS_COUNT_SECTOR == "2240",
         MATURITY_ORIG %in% c("KF", "HL", "KKF", "HHL")) %>%
  quarter_to_date %>%
  arrange(desc(date)) %>%
  left_join(MATURITY_ORIG, by = "MATURITY_ORIG") %>%
  mutate(OBS_VALUE = OBS_VALUE/1000) %>%
  ggplot + geom_line(aes(x = date, y = OBS_VALUE, color = Maturity_orig)) +
  ylab("") + xlab("") + theme_minimal() +
  theme(legend.title = element_blank(),
        legend.position = "bottom",
        legend.direction = "vertical") +
  scale_y_continuous(breaks = seq(0, 1000, 20)) +
  scale_x_date(breaks = as.Date(paste0(seq(1940, 2030, 2), "-01-01")),
               labels = date_format("%Y"))

KKF - Original maturity over 1 year, remaining maturity over 1 year and interest rate reset within the next 12 months

Code 
BSI %>%
  filter(MATURITY_ORIG == "KKF",
         REF_AREA %in% c("FR", "DE")) %>%
  quarter_to_date %>%
  left_join(REF_AREA, by = "REF_AREA") %>%
  left_join(BS_COUNT_SECTOR, by = "BS_COUNT_SECTOR") %>%
  mutate(OBS_VALUE = OBS_VALUE/1000) %>%
  left_join(colors, by = c("Ref_area" = "country")) %>%
  select(date, OBS_VALUE, Bs_count_sector, Ref_area, color) %>%
  ggplot + geom_line(aes(x = date, y = OBS_VALUE, color = color, linetype = Bs_count_sector)) +
  ylab("") + xlab("") + theme_minimal() +
  add_flags(4) + scale_color_identity() +
  theme(legend.position = c(0.45, 0.9),
        legend.title = element_blank()) +
  scale_y_continuous(breaks = seq(0, 1000, 20)) +
  scale_x_date(breaks = as.Date(paste0(seq(1940, 2030, 2), "-01-01")),
               labels = date_format("%Y"))

HHL - Original maturity over 2 years, remaining maturity over 2 years and interest rate reset within the next 24 months

Code 
BSI %>%
  filter(MATURITY_ORIG == "HHL",
         REF_AREA %in% c("FR", "DE")) %>%
  quarter_to_date %>%
  left_join(REF_AREA, by = "REF_AREA") %>%
  left_join(BS_COUNT_SECTOR, by = "BS_COUNT_SECTOR") %>%
  mutate(OBS_VALUE = OBS_VALUE/1000) %>%
  left_join(colors, by = c("Ref_area" = "country")) %>%
  select(date, OBS_VALUE, Bs_count_sector, Ref_area, color) %>%
  ggplot + geom_line(aes(x = date, y = OBS_VALUE, color = color, linetype = Bs_count_sector)) +
  ylab("") + xlab("") + theme_minimal() +
  add_flags(4) + scale_color_identity() +
  theme(legend.position = c(0.45, 0.9),
        legend.title = element_blank()) +
  scale_y_continuous(breaks = seq(0, 1000, 20)) +
  scale_x_date(breaks = as.Date(paste0(seq(1940, 2030, 2), "-01-01")),
               labels = date_format("%Y"))

HL - Original maturity over 2 years and remaining maturity up to 2 years

Code 
BSI %>%
  filter(MATURITY_ORIG == "HL",
         REF_AREA %in% c("FR", "DE")) %>%
  quarter_to_date %>%
  left_join(REF_AREA, by = "REF_AREA") %>%
  left_join(BS_COUNT_SECTOR, by = "BS_COUNT_SECTOR") %>%
  mutate(OBS_VALUE = OBS_VALUE/1000) %>%
  left_join(colors, by = c("Ref_area" = "country")) %>%
  select(date, OBS_VALUE, Bs_count_sector, Ref_area, color) %>%
  ggplot + geom_line(aes(x = date, y = OBS_VALUE, color = color, linetype = Bs_count_sector)) +
  ylab("") + xlab("") + theme_minimal() +
  add_flags(4) + scale_color_identity() +
  theme(legend.position = c(0.45, 0.9),
        legend.title = element_blank()) +
  scale_y_continuous(breaks = seq(0, 1000, 20)) +
  scale_x_date(breaks = as.Date(paste0(seq(1940, 2030, 2), "-01-01")),
               labels = date_format("%Y"))

Adjusted loans

Euro area Non Financial corporations (NFCs)

France Germany, Italy

All

Code 
BSI %>%
  filter(KEY %in% c("BSI.M.FR.N.A.A20T.A.I.U2.2240.Z01.A",
                    "BSI.M.IT.N.A.A20T.A.I.U2.2240.Z01.A",
                    "BSI.M.DE.N.A.A20T.A.I.U2.2240.Z01.A")) %>%
  month_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, 90, 2),
                     labels = scales::percent_format(accuracy = 1)) +
  scale_x_date(breaks = as.Date(paste0(seq(1940, 2030, 2), "-01-01")),
               labels = date_format("%Y"))

2017-

Code 
BSI %>%
  filter(KEY %in% c("BSI.M.FR.N.A.A20T.A.I.U2.2240.Z01.A",
                    "BSI.M.IT.N.A.A20T.A.I.U2.2240.Z01.A",
                    "BSI.M.DE.N.A.A20T.A.I.U2.2240.Z01.A")) %>%
  month_to_date %>%
  filter(date >= as.Date("2017-01-01")) %>%
  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, 90, 2),
                     labels = scales::percent_format(accuracy = 1)) +
  scale_x_date(breaks = as.Date(paste0(seq(1940, 2030, 1), "-01-01")),
               labels = date_format("%Y"))

France, Germany, Italy, Euro

Table

Code 
BSI %>%
  filter(FREQ == "M",
         REF_AREA == "FR",
         BS_ITEM == "A20T",
         BS_COUNT_SECTOR == "2240",
         COUNT_AREA == "U2",
         MATURITY_ORIG == "A") %>%
  select_if(~ n_distinct(.) > 1) %>%
  group_by(KEY, TITLE, TITLE_COMPL) %>%
  arrange(desc(TIME_PERIOD)) %>%
  summarise(OBS_VALUE = first(OBS_VALUE)) %>%
  print_table_conditional()
KEY TITLE TITLE_COMPL OBS_VALUE
BSI.M.FR.N.A.A20T.A.1.U2.2240.Z01.E Adjusted loans vis-a-vis euro area NFCs reported by MFIs excl. ESCB in France (stocks) France, Outstanding amounts at the end of the period (stocks), MFIs excluding ESCB reporting sector - Adjusted loans, Total maturity, All currencies combined - Euro area (changing composition) counterpart, Non-Financial corporations (S.11) sector, denominated in Euro, data Neither seasonally nor working day adjusted 1.442086e+06
BSI.M.FR.N.A.A20T.A.4.U2.2240.Z01.E Adjusted loans vis-a-vis euro area NFCs reported by MFIs excl. ESCB in France (transactions) France, Financial transactions (flows), MFIs excluding ESCB reporting sector - Adjusted loans, Total maturity, All currencies combined - Euro area (changing composition) counterpart, Non-Financial corporations (S.11) sector, denominated in Euro, data Neither seasonally nor working day adjusted 2.266943e+02
BSI.M.FR.N.A.A20T.A.I.U2.2240.Z01.A Adjusted loans vis-a-vis euro area NFCs reported by MFIs excl. ESCB in France (annual growth rate) France, Index of Notional Stocks, MFIs excluding ESCB reporting sector - Adjusted loans, Total maturity, All currencies combined - Euro area (changing composition) counterpart, Non-Financial corporations (S.11) sector, Annual growth rate, data Neither seasonally nor working day adjusted 2.567658e+00
BSI.M.FR.Y.A.A20T.A.1.U2.2240.Z01.E Adjusted loans vis-a-vis euro area NFCs reported by MFIs excl. ESCB in France (stocks) France, Outstanding amounts at the end of the period (stocks), MFIs excluding ESCB reporting sector - Adjusted loans, Total maturity, All currencies combined - Euro area (changing composition) counterpart, Non-Financial corporations (S.11) sector, denominated in Euro, data Working day and seasonally adjusted 1.448835e+06
BSI.M.FR.Y.A.A20T.A.4.U2.2240.Z01.E Adjusted loans vis-a-vis euro area NFCs reported by MFIs excl. ESCB in France (transactions) France, Financial transactions (flows), MFIs excluding ESCB reporting sector - Adjusted loans, Total maturity, All currencies combined - Euro area (changing composition) counterpart, Non-Financial corporations (S.11) sector, denominated in Euro, data Working day and seasonally adjusted 2.552000e+03
BSI.M.FR.Y.A.A20T.A.I.U2.2240.Z01.A Adjusted loans vis-a-vis euro area NFCs reported by MFIs excl. ESCB in France (annual growth rate) France, Index of Notional Stocks, MFIs excluding ESCB reporting sector - Adjusted loans, Total maturity, All currencies combined - Euro area (changing composition) counterpart, Non-Financial corporations (S.11) sector, Annual growth rate, data Working day and seasonally adjusted 2.647273e+00

Outstanding amounts: manual growth

Code 
BSI %>%
  filter(FREQ == "M",
         REF_AREA %in% c("FR", "DE", "U2"),
         BS_ITEM == "A20T",
         BS_COUNT_SECTOR == "2240",
         ADJUSTMENT == "Y",
         COUNT_AREA == "U2",
         MATURITY_ORIG == "A",
         DATA_TYPE == "1") %>%
  month_to_date %>%
  left_join(REF_AREA, by = "REF_AREA") %>%
  group_by(Ref_area) %>%
  arrange(date) %>%
  mutate(OBS_VALUE = OBS_VALUE/lag(OBS_VALUE, 12)-1) %>%
  mutate(Ref_area = ifelse(REF_AREA == "U2", "Europe", Ref_area)) %>%
  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, 90, 2),
                     labels = scales::percent_format(accuracy = 1)) +
  scale_x_date(breaks = as.Date(paste0(seq(1940, 2030, 2), "-01-01")),
               labels = date_format("%Y"))

Annual growth
Code 
BSI %>%
  filter(KEY %in% c("BSI.M.FR.N.A.A20T.A.I.U2.2240.Z01.A",
                    "BSI.M.IT.N.A.A20T.A.I.U2.2240.Z01.A",
                    "BSI.M.DE.N.A.A20T.A.I.U2.2240.Z01.A",
                    "BSI.M.U2.N.A.A20T.A.I.U2.2240.Z01.A")) %>%
  month_to_date %>%
  left_join(REF_AREA, by = "REF_AREA") %>%
  mutate(OBS_VALUE = OBS_VALUE/100) %>%
  mutate(Ref_area = ifelse(REF_AREA == "U2", "Europe", Ref_area)) %>%
  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(4) + scale_color_identity() +
  theme(legend.position = c(0.45, 0.9),
        legend.title = element_blank()) +
  scale_y_continuous(breaks = 0.01*seq(-100, 90, 2),
                     labels = scales::percent_format(accuracy = 1)) +
  scale_x_date(breaks = as.Date(paste0(seq(1940, 2030, 2), "-01-01")),
               labels = date_format("%Y"))

2016-

Code 
BSI %>%
  filter(KEY %in% c("BSI.M.FR.N.A.A20T.A.I.U2.2240.Z01.A",
                    "BSI.M.IT.N.A.A20T.A.I.U2.2240.Z01.A",
                    "BSI.M.DE.N.A.A20T.A.I.U2.2240.Z01.A",
                    "BSI.M.U2.N.A.A20T.A.I.U2.2240.Z01.A")) %>%
  month_to_date %>%
  left_join(REF_AREA, by = "REF_AREA") %>%
  mutate(OBS_VALUE = OBS_VALUE/100) %>%
  mutate(Ref_area = ifelse(REF_AREA == "U2", "Europe", Ref_area)) %>%
  left_join(colors, by = c("Ref_area" = "country")) %>%
  filter(date >= as.Date("2016-01-01")) %>%
  ggplot + geom_line(aes(x = date, y = OBS_VALUE, color = color)) +
  ylab("Adjusted loans vs. € area NFCs, annual growth") + xlab("") + theme_minimal() +
  add_flags(4) + scale_color_identity() +
  theme(legend.position = c(0.45, 0.9),
        legend.title = element_blank()) +
  scale_y_continuous(breaks = 0.01*seq(-100, 90, 2),
                     labels = scales::percent_format(accuracy = 1)) +
  scale_x_date(breaks = as.Date(paste0(seq(1940, 2030, 1), "-01-01")),
               labels = date_format("%Y"))

2019-

Code 
BSI %>%
  filter(KEY %in% c("BSI.M.FR.N.A.A20T.A.I.U2.2240.Z01.A",
                    "BSI.M.IT.N.A.A20T.A.I.U2.2240.Z01.A",
                    "BSI.M.DE.N.A.A20T.A.I.U2.2240.Z01.A")) %>%
  month_to_date %>%
  left_join(REF_AREA, by = "REF_AREA") %>%
  mutate(OBS_VALUE = OBS_VALUE/100) %>%
  filter(date >= as.Date("2021-01-01")) %>%
  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(),
        axis.text.x = element_text(angle = 45, vjust = 1, hjust = 1)) +
  scale_y_continuous(breaks = 0.01*seq(-100, 90, 2),
                     labels = scales::percent_format(accuracy = 1)) +
  scale_x_date(breaks = seq.Date(from = as.Date("2019-01-01"), Sys.Date(), "3 months"),
               labels = date_format("%b %Y"))

2021, all

Code 
BSI %>%
  filter(FREQ == "M",
         REF_AREA %in% c("BE", "DE", "EE", "IE", "GR", "ES", "FR", "IT",
                       "LV", "LT", "LU", "MT", "NL", "AT", "PT", "SI",  "FI"),
         ADJUSTMENT == "N",
         BS_REP_SECTOR == "A",
         BS_ITEM == "A20T",
         MATURITY_ORIG == "A",
         DATA_TYPE == "I",
         COUNT_AREA == "U2",
         BS_COUNT_SECTOR == "2240") %>%
  month_to_date %>%
  left_join(REF_AREA, by = "REF_AREA") %>%
  mutate(OBS_VALUE = OBS_VALUE/100) %>%
  filter(date >= as.Date("2021-01-01")) %>%
  arrange(desc(date)) %>%
  left_join(colors, by = c("Ref_area" = "country")) %>%
  select(date, REF_AREA, color, everything()) %>%
  mutate(color = ifelse(REF_AREA == "FR", color2, color)) %>%
  ggplot + geom_line(aes(x = date, y = OBS_VALUE, color = color)) +
  ylab("Adjusted loans vs. € area NFCs, annual growth") + xlab("") + theme_minimal() +
  scale_color_identity() +
  geom_image(data = . %>%
               filter(date == as.Date("2022-09-01")) %>%
               mutate(image = paste0("../../icon/flag/round/", str_to_lower(gsub(" ", "-", Ref_area)), ".png")),
             aes(x = date, y = OBS_VALUE, image = image), asp = 1.5, size=.02) +
  theme(legend.position = c(0.45, 0.9),
        legend.title = element_blank(),
        axis.text.x = element_text(angle = 45, vjust = 1, hjust = 1)) +
  scale_y_continuous(breaks = 0.01*seq(-100, 90, 2),
                     labels = scales::percent_format(accuracy = 1)) +
  scale_x_date(breaks = seq.Date(from = as.Date("2019-01-01"), Sys.Date(), "3 months"),
               labels = date_format("%b %Y"))

2022, all

Code 
BSI %>%
  filter(FREQ == "M",
         REF_AREA %in% c("BE", "DE", "EE", "IE", "GR", "ES", "FR", "IT",
                       "LV", "LT", "LU", "MT", "NL", "AT", "PT", "SI",  "FI"),
         ADJUSTMENT == "N",
         BS_REP_SECTOR == "A",
         BS_ITEM == "A20T",
         MATURITY_ORIG == "A",
         DATA_TYPE == "I",
         COUNT_AREA == "U2",
         BS_COUNT_SECTOR == "2240") %>%
  month_to_date %>%
  left_join(REF_AREA, by = "REF_AREA") %>%
  mutate(OBS_VALUE = OBS_VALUE/100) %>%
  filter(date >= as.Date("2022-01-01")) %>%
  arrange(desc(date)) %>%
  left_join(colors, by = c("Ref_area" = "country")) %>%
  select(date, REF_AREA, color, everything()) %>%
  mutate(color = ifelse(REF_AREA == "FR", color2, color)) %>%
  ggplot + geom_line(aes(x = date, y = OBS_VALUE, color = color)) +
  ylab("Adjusted loans vs. € area NFCs, annual growth") + xlab("") + theme_minimal() +
  scale_color_identity() +
  geom_image(data = . %>%
               filter(date == as.Date("2022-09-01")) %>%
               mutate(image = paste0("../../icon/flag/round/", str_to_lower(gsub(" ", "-", Ref_area)), ".png")),
             aes(x = date, y = OBS_VALUE, image = image), asp = 1.5, size=.02) +
  theme(legend.position = c(0.45, 0.9),
        legend.title = element_blank(),
        axis.text.x = element_text(angle = 45, vjust = 1, hjust = 1)) +
  scale_y_continuous(breaks = 0.01*seq(-100, 90, 2),
                     labels = scales::percent_format(accuracy = 1)) +
  scale_x_date(breaks = seq.Date(from = as.Date("2019-01-01"), Sys.Date(), "1 month"),
               labels = date_format("%b %Y"))

Households

France, Germany, Italy, Euro

All

Code 
BSI %>%
  filter(KEY %in% c("BSI.M.FR.N.A.A20T.A.I.U2.2250.Z01.A")) %>%
  month_to_date %>%
  left_join(REF_AREA, by = "REF_AREA") %>%
  mutate(OBS_VALUE = OBS_VALUE/100) %>%
  select(date, REF_AREA, Ref_area, OBS_VALUE) %>%
  mutate(Ref_area = ifelse(REF_AREA == "U2", "Europe", Ref_area)) %>%
  left_join(colors, by = c("Ref_area" = "country")) %>%
  ggplot + geom_line(aes(x = date, y = OBS_VALUE)) +
  ylab("Adjusted loans vs. households, annual growth, France") + xlab("") + theme_minimal() +
 
  theme(legend.position = c(0.45, 0.9),
        legend.title = element_blank()) +
  scale_y_continuous(breaks = 0.01*seq(-100, 90, 2),
                     labels = scales::percent_format(accuracy = 1)) +
  scale_x_date(breaks = as.Date(paste0(seq(1940, 2030, 2), "-01-01")),
               labels = date_format("%Y"))

France, Germany, Italy, Euro

All

Code 
data <- BSI %>%
  filter(KEY %in% c("BSI.M.FR.N.A.A20T.A.I.U2.2250.Z01.A",
                    "BSI.M.IT.N.A.A20T.A.I.U2.2250.Z01.A",
                    "BSI.M.DE.N.A.A20T.A.I.U2.2250.Z01.A",
                    "BSI.M.U2.N.A.A20T.A.I.U2.2250.Z01.A")) %>%
  month_to_date %>%
  left_join(REF_AREA, by = "REF_AREA") %>%
  mutate(OBS_VALUE = OBS_VALUE/100) %>%
  select(date, REF_AREA, Ref_area, OBS_VALUE)
write_excel_csv(data, file = "BSI_alter_eco.csv")
BSI %>%
  filter(KEY %in% c("BSI.M.FR.N.A.A20T.A.I.U2.2250.Z01.A",
                    "BSI.M.IT.N.A.A20T.A.I.U2.2250.Z01.A",
                    "BSI.M.DE.N.A.A20T.A.I.U2.2250.Z01.A",
                    "BSI.M.U2.N.A.A20T.A.I.U2.2250.Z01.A")) %>%
  month_to_date %>%
  left_join(REF_AREA, by = "REF_AREA") %>%
  mutate(OBS_VALUE = OBS_VALUE/100) %>%
  select(date, REF_AREA, Ref_area, OBS_VALUE) %>%
  mutate(Ref_area = ifelse(REF_AREA == "U2", "Europe", Ref_area)) %>%
  left_join(colors, by = c("Ref_area" = "country")) %>%
  ggplot + geom_line(aes(x = date, y = OBS_VALUE, color = color)) +
  ylab("Adjusted loans vs. € area households, annual growth") + xlab("") + theme_minimal() +
  add_flags(4) + scale_color_identity() +
  theme(legend.position = c(0.45, 0.9),
        legend.title = element_blank()) +
  scale_y_continuous(breaks = 0.01*seq(-100, 90, 2),
                     labels = scales::percent_format(accuracy = 1)) +
  scale_x_date(breaks = as.Date(paste0(seq(1940, 2030, 2), "-01-01")),
               labels = date_format("%Y"))

2016-

Code 
BSI %>%
  filter(KEY %in% c("BSI.M.FR.N.A.A20T.A.I.U2.2240.Z01.A",
                    "BSI.M.IT.N.A.A20T.A.I.U2.2240.Z01.A",
                    "BSI.M.DE.N.A.A20T.A.I.U2.2240.Z01.A",
                    "BSI.M.U2.N.A.A20T.A.I.U2.2240.Z01.A")) %>%
  month_to_date %>%
  left_join(REF_AREA, by = "REF_AREA") %>%
  mutate(OBS_VALUE = OBS_VALUE/100) %>%
  mutate(Ref_area = ifelse(REF_AREA == "U2", "Europe", Ref_area)) %>%
  left_join(colors, by = c("Ref_area" = "country")) %>%
  filter(date >= as.Date("2016-01-01")) %>%
  ggplot + geom_line(aes(x = date, y = OBS_VALUE, color = color)) +
  ylab("Adjusted loans vs. € area households, annual growth") + xlab("") + theme_minimal() +
  add_flags(4) + scale_color_identity() +
  theme(legend.position = c(0.45, 0.9),
        legend.title = element_blank()) +
  scale_y_continuous(breaks = 0.01*seq(-100, 90, 2),
                     labels = scales::percent_format(accuracy = 1)) +
  scale_x_date(breaks = as.Date(paste0(seq(1940, 2030, 1), "-01-01")),
               labels = date_format("%Y"))

All

Code 
BSI %>%
  filter(KEY %in% c("BSI.M.FR.N.A.A20T.A.I.U2.2250.Z01.A",
                    "BSI.M.IT.N.A.A20T.A.I.U2.2250.Z01.A",
                    "BSI.M.DE.N.A.A20T.A.I.U2.2250.Z01.A")) %>%
  month_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. households, 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, 90, 2),
                     labels = scales::percent_format(accuracy = 1)) +
  scale_x_date(breaks = as.Date(paste0(seq(1940, 2030, 2), "-01-01")),
               labels = date_format("%Y"))

2021, all

Code 
BSI %>%
  filter(FREQ == "M",
         REF_AREA %in% c("BE", "DE", "EE", "IE", "GR", "ES", "FR", "IT",
                       "LV", "LT", "LU", "MT", "NL", "AT", "PT", "SI",  "FI"),
         ADJUSTMENT == "N",
         BS_REP_SECTOR == "A",
         BS_ITEM == "A20T",
         MATURITY_ORIG == "A",
         DATA_TYPE == "I",
         COUNT_AREA == "U2",
         BS_COUNT_SECTOR == "2250") %>%
  month_to_date %>%
  left_join(REF_AREA, by = "REF_AREA") %>%
  mutate(OBS_VALUE = OBS_VALUE/100) %>%
  filter(date >= as.Date("2021-01-01")) %>%
  arrange(desc(date)) %>%
  left_join(colors, by = c("Ref_area" = "country")) %>%
  select(date, REF_AREA, color, everything()) %>%
  mutate(color = ifelse(REF_AREA == "FR", color2, color)) %>%
  ggplot + geom_line(aes(x = date, y = OBS_VALUE, color = color)) +
  ylab("Adjusted loans vs. households, annual growth") + xlab("") + theme_minimal() +
  scale_color_identity() +
  geom_image(data = . %>%
               filter(date == as.Date("2023-02-01")) %>%
               mutate(image = paste0("../../icon/flag/round/", str_to_lower(gsub(" ", "-", Ref_area)), ".png")),
             aes(x = date, y = OBS_VALUE, image = image), asp = 1.5, size = .02) +
  theme(legend.position = c(0.45, 0.9),
        legend.title = element_blank(),
        axis.text.x = element_text(angle = 45, vjust = 1, hjust = 1)) +
  scale_y_continuous(breaks = 0.01*seq(-100, 90, 2),
                     labels = scales::percent_format(accuracy = 1)) +
  scale_x_date(breaks = seq.Date(from = as.Date("2019-01-01"), Sys.Date(), "3 months"),
               labels = date_format("%b %Y"))

2022, all

Code 
BSI %>%
  filter(FREQ == "M",
         REF_AREA %in% c("BE", "DE", "EE", "IE", "GR", "ES", "FR", "IT",
                       "LV", "LT", "LU", "MT", "NL", "AT", "PT", "SI",  "FI"),
         ADJUSTMENT == "N",
         BS_REP_SECTOR == "A",
         BS_ITEM == "A20T",
         MATURITY_ORIG == "A",
         DATA_TYPE == "I",
         COUNT_AREA == "U2",
         BS_COUNT_SECTOR == "2250") %>%
  month_to_date %>%
  left_join(REF_AREA, by = "REF_AREA") %>%
  mutate(OBS_VALUE = OBS_VALUE/100) %>%
  filter(date >= as.Date("2022-01-01")) %>%
  arrange(desc(date)) %>%
  left_join(colors, by = c("Ref_area" = "country")) %>%
  select(date, REF_AREA, color, everything()) %>%
  mutate(color = ifelse(REF_AREA == "FR", color2, color)) %>%
  ggplot + geom_line(aes(x = date, y = OBS_VALUE, color = color)) +
  ylab("Adjusted loans vs. households, annual growth") + xlab("") + theme_minimal() +
  scale_color_identity() +
  geom_image(data = . %>%
               filter(date == as.Date("2023-02-01")) %>%
               mutate(image = paste0("../../icon/flag/round/", str_to_lower(gsub(" ", "-", Ref_area)), ".png")),
             aes(x = date, y = OBS_VALUE, image = image), asp = 1.5, size = .02) +
  theme(legend.position = c(0.45, 0.9),
        legend.title = element_blank(),
        axis.text.x = element_text(angle = 45, vjust = 1, hjust = 1)) +
  scale_y_continuous(breaks = 0.01*seq(-100, 90, 2),
                     labels = scales::percent_format(accuracy = 1)) +
  scale_x_date(breaks = seq.Date(from = as.Date("2019-01-01"), Sys.Date(), "1 month"),
               labels = date_format("%b %Y"))

Monetary aggregates

M1

Code 
BSI %>%
  filter(KEY %in% c("BSI.M.U2.Y.V.M10.X.1.U2.2300.Z01.E",
                    "BSI.M.U2.N.V.M10.X.1.U2.2300.Z01.E")) %>%
  left_join(BS_ITEM,  by = "BS_ITEM") %>%
  month_to_date %>%
  arrange(desc(date)) %>%
  ggplot + geom_line(aes(x = date, y = OBS_VALUE/10^3, color = KEY)) +
  xlab("") + ylab("") + theme_minimal() +
  scale_x_date(breaks = as.Date(paste0(seq(1940, 2030, 5), "-01-01")),
               labels = date_format("%Y")) +
  theme(legend.position = c(0.25, 0.9),
        legend.title = element_blank()) +
  scale_y_continuous(breaks = 1000*seq(-100, 90, 1),
                     labels = scales::dollar_format(acc = 1, su = " Bn€", pre = "")) +
  geom_hline(yintercept = 0, linetype = "dashed")

M1, M2, M3

Niveau

Code 
BSI %>%
  filter(BS_ITEM %in% c("M10", "M20", "M30"),
         BS_SUFFIX == "E",
         FREQ == "M",
         ADJUSTMENT == "N",
         COLLECTION == "E",
         DATA_TYPE == "1") %>%
  left_join(BS_ITEM,  by = "BS_ITEM") %>%
  month_to_date %>%
  arrange(desc(date)) %>%
  ggplot + geom_line(aes(x = date, y = OBS_VALUE/10^3, color = Bs_item)) +
  xlab("") + ylab("") + theme_minimal() +
  scale_x_date(breaks = as.Date(paste0(seq(1940, 2030, 5), "-01-01")),
               labels = date_format("%Y")) +
  theme(legend.position = c(0.25, 0.9),
        legend.title = element_blank()) +
  scale_y_continuous(breaks = 1000*seq(-100, 90, 1),
                     labels = scales::dollar_format(acc = 1, su = " Bn€", pre = "")) +
  geom_hline(yintercept = 0, linetype = "dashed")

Croissance

Code 
BSI %>%
  filter(BS_ITEM %in% c("M10", "M20", "M30"),
         BS_SUFFIX == "A",
         FREQ == "M") %>%
  left_join(BS_ITEM,  by = "BS_ITEM") %>%
  month_to_date %>%
  ggplot + geom_line(aes(x = date, y = OBS_VALUE/100, color = Bs_item)) +
  xlab("") + ylab("") + theme_minimal() +
  scale_x_date(breaks = as.Date(paste0(seq(1940, 2030, 5), "-01-01")),
               labels = date_format("%Y")) +
  theme(legend.position = c(0.25, 0.9),
        legend.title = element_blank()) +
  scale_y_continuous(breaks = 0.01*seq(-100, 90, 2),
                     labels = scales::percent_format(accuracy = 1)) +
  geom_hline(yintercept = 0, linetype = "dashed")

M1

M1 = overnights deposits + currency in circulation.

Level

Code 
BSI %>%
  filter(BS_ITEM %in% c("L10", "L21", "M10"),
         BS_COUNT_SECTOR == "2300",
         BS_SUFFIX == "E",
         FREQ == "M",
         ADJUSTMENT == "N",
         COLLECTION == "E",
         DATA_TYPE == "1",
         BS_REP_SECTOR == "V",
         REF_AREA == "U2") %>%
  left_join(BS_ITEM,  by = "BS_ITEM") %>%
  month_to_date %>%
  arrange(desc(date)) %>%
  group_by(date) %>%
  filter(n() == 3) %>%
  mutate(Bs_item = factor(Bs_item, levels = c("Monetary aggregate M1", "Overnight deposits", "Currency in circulation"))) %>%
  ggplot + geom_line(aes(x = date, y = OBS_VALUE/10^3, color = Bs_item)) +
  xlab("") + ylab("") + theme_minimal() +
  scale_x_date(breaks = as.Date(paste0(seq(1940, 2030, 2), "-01-01")),
               labels = date_format("%Y")) +
  theme(legend.position = c(0.25, 0.9),
        legend.title = element_blank()) +
  scale_y_continuous(breaks = 1000*seq(-100, 90, 1),
                     labels = scales::dollar_format(acc = 1, su = " Bn€", pre = "")) +
  geom_hline(yintercept = 0, linetype = "dashed")

Growth

All

Code 
BSI %>%
  filter(BS_ITEM %in% c("L10", "L21", "M10"),
         BS_COUNT_SECTOR == "2300",
         BS_SUFFIX == "A",
         FREQ == "M",
         BS_REP_SECTOR == "V",
         REF_AREA == "U2",
         ADJUSTMENT == "N") %>%
  left_join(BS_ITEM,  by = "BS_ITEM") %>%
  month_to_date %>%
  arrange(desc(date)) %>%
  ggplot + geom_line(aes(x = date, y = OBS_VALUE/100, color = Bs_item)) +
  xlab("") + ylab("") + theme_minimal() +
  scale_x_date(breaks = as.Date(paste0(seq(1940, 2030, 2), "-01-01")),
               labels = date_format("%b %Y")) +
  theme(legend.position = c(0.25, 0.9),
        legend.title = element_blank()) +
  scale_y_continuous(breaks = 0.01*seq(-100, 90, 2),
                     labels = scales::percent_format(accuracy = 1)) +
  geom_hline(yintercept = 0, linetype = "dashed")

2005-

Code 
BSI %>%
  filter(BS_ITEM %in% c("L10", "L21", "M10"),
         BS_COUNT_SECTOR == "2300",
         BS_SUFFIX == "A",
         FREQ == "M",
         BS_REP_SECTOR == "V",
         REF_AREA == "U2",
         ADJUSTMENT == "N") %>%
  left_join(BS_ITEM,  by = "BS_ITEM") %>%
  month_to_date %>%
  arrange(desc(date)) %>%
  filter(date >= as.Date("2005-01-01")) %>%
  ggplot + geom_line(aes(x = date, y = OBS_VALUE/100, color = Bs_item)) +
  xlab("") + ylab("") + theme_minimal() +
  scale_x_date(breaks = as.Date(paste0(seq(1940, 2030, 2), "-01-01")),
               labels = date_format("%Y")) +
  theme(legend.position = c(0.25, 0.2),
        legend.title = element_blank()) +
  scale_y_continuous(breaks = 0.01*seq(-100, 90, 2),
                     labels = scales::percent_format(accuracy = 1)) +
  geom_hline(yintercept = 0, linetype = "dashed")

M2-M1 (other short-term deposits)

M2-M1 = deposits with an agreed maturity of up to 2 years + deposits redeemable at notice of up to 3 months

Level

Code 
BSI %>%
  filter(BS_ITEM %in% c("L2A", "L22", "L23"),
         BS_COUNT_SECTOR == "2300",
         BS_SUFFIX == "E",
         FREQ == "M",
         ADJUSTMENT == "N",
         COLLECTION == "E",
         DATA_TYPE == "1",
         BS_REP_SECTOR == "V",
         REF_AREA == "U2") %>%
  left_join(BS_ITEM,  by = "BS_ITEM") %>%
  month_to_date %>%
  arrange(desc(date)) %>%
  group_by(date) %>%
  filter(n() == 3) %>%
  mutate(Bs_item = ifelse(BS_ITEM == "L2A", "Monetary aggregate M2-M1", Bs_item)) %>%
  mutate(Bs_item = factor(Bs_item, levels = c("Monetary aggregate M2-M1", "Deposits with agreed maturity", "Deposits redeemable at notice"))) %>%
  ggplot + geom_line(aes(x = date, y = OBS_VALUE/10^3, color = Bs_item)) +
  xlab("") + ylab("") + theme_minimal() +
  scale_x_date(breaks = as.Date(paste0(seq(1940, 2030, 2), "-01-01")),
               labels = date_format("%Y")) +
  theme(legend.position = c(0.2, 0.9),
        legend.title = element_blank()) +
  scale_y_continuous(breaks = 1000*seq(-100, 90, .5),
                     labels = scales::dollar_format(acc = 1, su = " Bn€", pre = ""))

Growth

All

Code 
BSI %>%
  filter(BS_ITEM %in% c("L2A", "L22", "L23"),
         BS_COUNT_SECTOR == "2300",
         BS_SUFFIX == "A",
         FREQ == "M",
         BS_REP_SECTOR == "V",
         REF_AREA == "U2",
         ADJUSTMENT == "N") %>%
  left_join(BS_ITEM,  by = "BS_ITEM") %>%
  month_to_date %>%
  arrange(desc(date)) %>%
  ggplot + geom_line(aes(x = date, y = OBS_VALUE/100, color = Bs_item)) +
  xlab("") + ylab("") + theme_minimal() +
  scale_x_date(breaks = as.Date(paste0(seq(1940, 2030, 5), "-01-01")),
               labels = date_format("%Y")) +
  theme(legend.position = c(0.25, 0.9),
        legend.title = element_blank()) +
  scale_y_continuous(breaks = 0.01*seq(-100, 90, 2),
                     labels = scales::percent_format(accuracy = 1)) +
  geom_hline(yintercept = 0, linetype = "dashed")

2005-

Code 
BSI %>%
  filter(BS_ITEM %in% c("L2A", "L22", "L23"),
         BS_COUNT_SECTOR == "2300",
         BS_SUFFIX == "A",
         FREQ == "M",
         BS_REP_SECTOR == "V",
         REF_AREA == "U2",
         ADJUSTMENT == "N") %>%
  left_join(BS_ITEM,  by = "BS_ITEM") %>%
  month_to_date %>%
  arrange(desc(date)) %>%
  filter(date >= as.Date("2005-01-01")) %>%
  ggplot + geom_line(aes(x = date, y = OBS_VALUE/100, color = Bs_item)) +
  xlab("") + ylab("") + theme_minimal() +
  scale_x_date(breaks = as.Date(paste0(seq(1940, 2030, 2), "-01-01")),
               labels = date_format("%Y")) +
  theme(legend.position = c(0.3, 0.8),
        legend.title = element_blank()) +
  scale_y_continuous(breaks = 0.01*seq(-100, 90, 10),
                     labels = scales::percent_format(accuracy = 1)) +
  geom_hline(yintercept = 0, linetype = "dashed")

M3-M2 (marketable instruments)

M3-M2 = Repurchase agreements + money market fund shares + Debt securities with maturity of up to 2 years

Level

Code 
BSI %>%
  filter(BS_ITEM %in% c("LT3", "L24A", "L40", "L30"),
         BS_COUNT_SECTOR == "2300",
         BS_SUFFIX == "E",
         FREQ == "M",
         ADJUSTMENT == "N",
         COLLECTION == "E",
         DATA_TYPE == "1",
         BS_REP_SECTOR == "V",
         REF_AREA == "U2") %>%
  left_join(BS_ITEM,  by = "BS_ITEM") %>%
  month_to_date %>%
  arrange(desc(date)) %>%
  group_by(date) %>%
  filter(n() == 4) %>%
  mutate(Bs_item = ifelse(BS_ITEM == "LT3", "Monetary aggregate M3-M2", Bs_item)) %>%
  mutate(Bs_item = factor(Bs_item, levels = c("Monetary aggregate M3-M2", "Money Market Funds shares/units", "Repurchase agreements excluding repos with central counterparties", "Debt securities issued"))) %>%
  ggplot + geom_line(aes(x = date, y = OBS_VALUE/10^3, color = Bs_item)) +
  xlab("") + ylab("") + theme_minimal() +
  scale_x_date(breaks = as.Date(paste0(seq(1940, 2030, 2), "-01-01")),
               labels = date_format("%Y")) +
  theme(legend.position = c(0.4, 0.8),
        legend.title = element_blank()) +
  scale_y_continuous(breaks = 1000*seq(-100, 90, .1),
                     labels = scales::dollar_format(acc = 1, su = " Bn€", pre = ""))

Growth

All

Code 
BSI %>%
  filter(BS_ITEM %in% c("LT3", "L24A", "L40", "L30"),
         BS_COUNT_SECTOR == "2300",
         BS_SUFFIX == "A",
         FREQ == "M",
         BS_REP_SECTOR == "V",
         REF_AREA == "U2",
         ADJUSTMENT == "N") %>%
  left_join(BS_ITEM,  by = "BS_ITEM") %>%
  month_to_date %>%
  arrange(desc(date)) %>%
  ggplot + geom_line(aes(x = date, y = OBS_VALUE/100, color = Bs_item)) +
  xlab("") + ylab("") + theme_minimal() +
  scale_x_date(breaks = as.Date(paste0(seq(1940, 2030, 5), "-01-01")),
               labels = date_format("%Y")) +
  theme(legend.position = c(0.25, 0.9),
        legend.title = element_blank()) +
  scale_y_continuous(breaks = 0.01*seq(-100, 90, 2),
                     labels = scales::percent_format(accuracy = 1)) +
  geom_hline(yintercept = 0, linetype = "dashed")

2005-

Code 
BSI %>%
  filter(BS_ITEM %in% c("LT3", "L24A", "L40", "L30"),
         BS_COUNT_SECTOR == "2300",
         BS_SUFFIX == "A",
         FREQ == "M",
         BS_REP_SECTOR == "V",
         REF_AREA == "U2",
         ADJUSTMENT == "N") %>%
  left_join(BS_ITEM,  by = "BS_ITEM") %>%
  month_to_date %>%
  arrange(desc(date)) %>%
  filter(date >= as.Date("2005-01-01")) %>%
  ggplot + geom_line(aes(x = date, y = OBS_VALUE/100, color = Bs_item)) +
  xlab("") + ylab("") + theme_minimal() +
  scale_x_date(breaks = as.Date(paste0(seq(1940, 2030, 2), "-01-01")),
               labels = date_format("%Y")) +
  theme(legend.position = c(0.3, 0.8),
        legend.title = element_blank()) +
  scale_y_continuous(breaks = 0.01*seq(-100, 90, 10),
                     labels = scales::percent_format(accuracy = 1)) +
  geom_hline(yintercept = 0, linetype = "dashed")

Deposits subject to reserve requirements

Total (June): 24.5 Tn€ (greater than M3)

Source: https://data.ecb.europa.eu/publications/ecbeurosystem-policy-and-exchange-rates/3030611

Liabilities to which a 1% reserve coefficient is applied

Code 
BSI %>%
  filter(FREQ == "M",
         REF_AREA == "U2",
         ADJUSTMENT == "N",
         BS_REP_SECTOR == "R",
         BS_ITEM %in% c("L40", "L2B"),
         BS_SUFFIX == "E",
         BS_COUNT_SECTOR == "3000") %>%
  left_join(BS_ITEM,  by = "BS_ITEM") %>%
  month_to_date %>%
  arrange(desc(date)) %>%
  ggplot + geom_line(aes(x = date, y = OBS_VALUE/10^3, color = Bs_item)) +
  xlab("") + ylab("") + theme_minimal() +
  scale_x_date(breaks = as.Date(paste0(seq(1940, 2030, 2), "-01-01")),
               labels = date_format("%Y")) +
  theme(legend.position = c(0.4, 0.9),
        legend.title = element_blank()) +
  scale_y_continuous(breaks = 1000*seq(-100, 90, 1),
                     labels = scales::dollar_format(acc = 1, su = " Bn€", pre = ""))

Liabilities to which a 0% reserve coefficient is applied

Code 
BSI %>%
  filter(FREQ == "M",
         REF_AREA == "U2",
         ADJUSTMENT == "N",
         BS_REP_SECTOR == "R",
         BS_ITEM %in% c("L2A", "L24", "L40"),
         BS_SUFFIX == "E",
         BS_COUNT_SECTOR == "3000") %>%
  left_join(BS_ITEM,  by = "BS_ITEM") %>%
  month_to_date %>%
  arrange(desc(date)) %>%
  ggplot + geom_line(aes(x = date, y = OBS_VALUE/10^3, color = Bs_item)) +
  xlab("") + ylab("") + theme_minimal() +
  scale_x_date(breaks = as.Date(paste0(seq(1940, 2030, 2), "-01-01")),
               labels = date_format("%Y")) +
  theme(legend.position = c(0.4, 0.9),
        legend.title = element_blank()) +
  scale_y_continuous(breaks = 1000*seq(-100, 90, .2),
                     labels = scales::dollar_format(acc = 1, su = " Bn€", pre = ""))

Other

Breakdown of deposits in M3: Households, NFCs, Non-MFIs (Households + NFCs)

Level

Code 
BSI %>%
  filter(BS_ITEM %in% c("L2C"),
         BS_COUNT_SECTOR %in% c("2300", "2250", "2240"),
         BS_SUFFIX == "E",
         FREQ == "M",
         ADJUSTMENT == "Y",
         COLLECTION == "E",
         DATA_TYPE == "1",
         BS_REP_SECTOR == "V",
         REF_AREA == "U2") %>%
  left_join(BS_ITEM,  by = "BS_ITEM") %>%
  left_join(BS_COUNT_SECTOR,  by = "BS_COUNT_SECTOR") %>%
  month_to_date %>%
  arrange(desc(date)) %>%
  ggplot + geom_line(aes(x = date, y = OBS_VALUE/1000, color = Bs_count_sector)) +
  xlab("") + ylab("") + theme_minimal() +
  scale_x_date(breaks = as.Date(paste0(seq(1940, 2030, 2), "-01-01")),
               labels = date_format("%Y")) +
  theme(legend.position = c(0.5, 0.9),
        legend.title = element_blank()) +
  scale_y_continuous(breaks = 1000*seq(-100, 90, 1),
                     labels = scales::dollar_format(acc = 1, su = " Bn€", pre = ""))

Growth

All

Code 
BSI %>%
  filter(BS_ITEM %in% c("L2C"),
         BS_COUNT_SECTOR %in% c("2300", "2250", "2240"),
         BS_SUFFIX == "A",
         FREQ == "M",
         ADJUSTMENT == "Y",
         COLLECTION == "E",
         REF_AREA == "U2") %>%
  left_join(BS_ITEM,  by = "BS_ITEM") %>%
  left_join(BS_COUNT_SECTOR,  by = "BS_COUNT_SECTOR") %>%
  month_to_date %>%
  arrange(desc(date)) %>%
  ggplot + geom_line(aes(x = date, y = OBS_VALUE/100, color = Bs_count_sector)) +
  xlab("") + ylab("") + theme_minimal() +
  scale_x_date(breaks = as.Date(paste0(seq(1940, 2030, 2), "-01-01")),
               labels = date_format("%Y")) +
  theme(legend.position = c(0.4, 0.9),
        legend.title = element_blank()) +
  scale_y_continuous(breaks = 0.01*seq(-100, 90, 2),
                     labels = scales::percent_format(accuracy = 1)) +
  geom_hline(yintercept = 0, linetype = "dashed")

2004-

Code 
BSI %>%
  filter(BS_ITEM %in% c("L2C"),
         BS_COUNT_SECTOR %in% c("2300", "2250", "2240"),
         BS_SUFFIX == "A",
         FREQ == "M",
         ADJUSTMENT == "Y",
         COLLECTION == "E",
         REF_AREA == "U2") %>%
  left_join(BS_ITEM,  by = "BS_ITEM") %>%
  left_join(BS_COUNT_SECTOR,  by = "BS_COUNT_SECTOR") %>%
  month_to_date %>%
  arrange(desc(date)) %>%
  filter(date >= as.Date("2004-01-01")) %>%
  ggplot + geom_line(aes(x = date, y = OBS_VALUE/100, color = Bs_count_sector)) +
  xlab("") + ylab("") + theme_minimal() +
  scale_x_date(breaks = as.Date(paste0(seq(1940, 2030, 2), "-01-01")),
               labels = date_format("%Y")) +
  theme(legend.position = c(0.4, 0.9),
        legend.title = element_blank()) +
  scale_y_continuous(breaks = 0.01*seq(-100, 90, 2),
                     labels = scales::percent_format(accuracy = 1)) +
  geom_hline(yintercept = 0, linetype = "dashed")

BSI.M.U2.Y.V.L2C.M.1.U2.2300.Z01.E BSI.M.U2.Y.V.L2C.M.1.U2.2250.Z01.E BSI.M.U2.Y.V.L2C.M.1.U2.2240.Z01.E

Reserves

Required reserves

Europe

Code 
BSI %>%
  filter(BS_ITEM %in% c("LRR"),
         REF_AREA == "U2") %>%
  month_to_date %>%
  arrange(desc(date)) %>%
  left_join(BS_ITEM,  by = "BS_ITEM") %>%
  ggplot + geom_line(aes(x = date, y = OBS_VALUE/1000)) +
  xlab("") + ylab("Required reserves") + theme_minimal() +
  scale_x_date(breaks = as.Date(paste0(seq(1940, 2030, 2), "-01-01")),
               labels = date_format("%Y")) +
  theme(legend.position = c(0.25, 0.9),
        legend.title = element_blank()) +
  scale_y_continuous(breaks = seq(0, 600, 20),
                     labels = scales::dollar_format(acc = 1, su = " Bn€", pre = ""),
                     limits = c(0, 225))

France, Germany, Italy

Code 
BSI %>%
  filter(KEY %in% c("BSI.M.FR.N.R.LRR.X.1.A1.3000.Z01.E",
                    "BSI.M.DE.N.R.LRR.X.1.A1.3000.Z01.E",
                    "BSI.M.IT.N.R.LRR.X.1.A1.3000.Z01.E")) %>%
  month_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("Required reserves in banks") + 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 = seq(0, 40000, 50),
                     labels = scales::dollar_format(acc = 1, su = " Bn€", pre = "")) +
  scale_x_date(breaks = as.Date(paste0(seq(1940, 2030, 1), "-01-01")),
               labels = date_format("%Y"))

All reserves

U2 - Europe

Code 
BSI %>%
  filter(BS_ITEM %in% c("LRR", "LRE"),
         REF_AREA == "U2") %>%
  month_to_date %>%
  arrange(desc(date)) %>%
  left_join(BS_ITEM,  by = "BS_ITEM") %>%
  ggplot + geom_line(aes(x = date, y = OBS_VALUE/1000, color = Bs_item)) +
  xlab("") + ylab("") + theme_minimal() +
  scale_x_date(breaks = as.Date(paste0(seq(1940, 2030, 2), "-01-01")),
               labels = date_format("%Y")) +
  theme(legend.position = c(0.25, 0.9),
        legend.title = element_blank()) +
  scale_y_continuous(breaks = seq(0, 40000, 500)) +
  geom_hline(yintercept = 0, linetype = "dashed")

DE - Germany

Code 
BSI %>%
  filter(BS_ITEM %in% c("LRR", "LRE"),
         REF_AREA == "DE") %>%
  month_to_date %>%
  arrange(desc(date)) %>%
  left_join(BS_ITEM,  by = "BS_ITEM") %>%
  ggplot + geom_line(aes(x = date, y = OBS_VALUE/1000, color = Bs_item)) +
  xlab("") + ylab("") + theme_minimal() +
  scale_x_date(breaks = as.Date(paste0(seq(1940, 2030, 2), "-01-01")),
               labels = date_format("%Y")) +
  theme(legend.position = c(0.25, 0.9),
        legend.title = element_blank()) +
  scale_y_continuous(breaks = seq(0, 40000, 100)) +
  geom_hline(yintercept = 0, linetype = "dashed")

FR - Germany

Code 
BSI %>%
  filter(BS_ITEM %in% c("LRR", "LRE"),
         REF_AREA == "FR") %>%
  month_to_date %>%
  arrange(desc(date)) %>%
  left_join(BS_ITEM,  by = "BS_ITEM") %>%
  ggplot + geom_line(aes(x = date, y = OBS_VALUE/1000, color = Bs_item)) +
  xlab("") + ylab("") + theme_minimal() +
  scale_x_date(breaks = as.Date(paste0(seq(1940, 2030, 2), "-01-01")),
               labels = date_format("%Y")) +
  theme(legend.position = c(0.25, 0.9),
        legend.title = element_blank()) +
  scale_y_continuous(breaks = seq(0, 40000, 100)) +
  geom_hline(yintercept = 0, linetype = "dashed")

IT - Italy

Code 
BSI %>%
  filter(BS_ITEM %in% c("LRR", "LRE"),
         REF_AREA == "IT") %>%
  month_to_date %>%
  arrange(desc(date)) %>%
  left_join(BS_ITEM,  by = "BS_ITEM") %>%
  ggplot + geom_line(aes(x = date, y = OBS_VALUE/1000, color = Bs_item)) +
  xlab("") + ylab("") + theme_minimal() +
  scale_x_date(breaks = as.Date(paste0(seq(1940, 2030, 2), "-01-01")),
               labels = date_format("%Y")) +
  theme(legend.position = c(0.25, 0.9),
        legend.title = element_blank()) +
  scale_y_continuous(breaks = seq(0, 40000, 100)) +
  geom_hline(yintercept = 0, linetype = "dashed")

Reserve base

Code 
BSI %>%
  filter(BS_ITEM %in% c("LR0")) %>%
  month_to_date %>%
  arrange(desc(date)) %>%
  left_join(BS_ITEM,  by = "BS_ITEM") %>%
  ggplot + geom_line(aes(x = date, y = OBS_VALUE/1000, color = Bs_item)) +
  xlab("") + ylab("") + theme_minimal() +
  scale_x_date(breaks = as.Date(paste0(seq(1940, 2030, 2), "-01-01")),
               labels = date_format("%Y")) +
  theme(legend.position = c(0.25, 0.9),
        legend.title = element_blank()) +
  scale_y_continuous(breaks = seq(0, 40000, 2000)) +
  geom_hline(yintercept = 0, linetype = "dashed")

Total excess reserves of credit institutions

Excess liquidity = Excess reserves +

Europe

https://data.ecb.europa.eu/publications/money-credit-and-banking/3031796

Code 
BSI %>%
  filter(KEY %in% c("BSI.M.U2.N.R.LRE.X.1.A1.3000.Z01.E")) %>%
  month_to_date %>%
  arrange(desc(date)) %>%
  left_join(BS_ITEM,  by = "BS_ITEM") %>%
  ggplot + geom_line(aes(x = date, y = OBS_VALUE/1000, color = Bs_item)) +
  xlab("") + ylab("") + theme_minimal() +
  scale_x_date(breaks = as.Date(paste0(seq(1940, 2030, 2), "-01-01")),
               labels = date_format("%Y")) +
  theme(legend.position = c(0.25, 0.9),
        legend.title = element_blank()) +
  scale_y_continuous(breaks = seq(0, 40000, 500)) +
  geom_hline(yintercept = 0, linetype = "dashed")

Euro area Non Financial corporations (NFCs)

France, Germany, Italy

Code 
BSI %>%
  filter(KEY %in% c("BSI.M.FR.N.R.LRE.X.1.A1.3000.Z01.E",
                    "BSI.M.DE.N.R.LRE.X.1.A1.3000.Z01.E",
                    "BSI.M.IT.N.R.LRE.X.1.A1.3000.Z01.E")) %>%
  month_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("Excess reserves in banks") + 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 = seq(0, 40000, 1000)) +
  scale_x_date(breaks = as.Date(paste0(seq(1940, 2030, 1), "-01-01")),
               labels = date_format("%Y"))

Spain, Netherlands, Portugal

Code 
BSI %>%
  filter(KEY %in% c("BSI.M.ES.N.R.LRE.X.1.A1.3000.Z01.E",
                    "BSI.M.NL.N.R.LRE.X.1.A1.3000.Z01.E",
                    "BSI.M.PT.N.R.LRE.X.1.A1.3000.Z01.E")) %>%
  month_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("Excess reserves in banks") + 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 = seq(0, 40000, 500)) +
  scale_x_date(breaks = as.Date(paste0(seq(1940, 2030, 1), "-01-01")),
               labels = date_format("%Y"))

Excess reserves

Code 
BSI %>%
  filter(KEY %in% c("BSI.M.FR.N.A.A20T.A.I.U2.2240.Z01.A",
                    "BSI.M.IT.N.A.A20T.A.I.U2.2240.Z01.A",
                    "BSI.M.DE.N.A.A20T.A.I.U2.2240.Z01.A")) %>%
  month_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, 90, 2),
                     labels = scales::percent_format(accuracy = 1)) +
  scale_x_date(breaks = as.Date(paste0(seq(1940, 2030, 2), "-01-01")),
               labels = date_format("%Y"))