Credit to the non-financial sector

Data - BIS

Author

François Geerolf

Info

LAST_DOWNLOAD

source dataset Title Download Compile
bis TOTAL_CREDIT Credit to the non-financial sector 2024-05-10 [2024-07-02]
ecb BLS Bank Lending Survey Statistics - BLS 2024-07-26 [2024-07-01]

LAST_COMPILE

LAST_COMPILE
2024-08-09

Last

date Nobs
2023-04-01 1133
2023-01-01 1133

TC_LENDERS, Lending sector

Code 
TOTAL_CREDIT %>%
  group_by(TC_LENDERS, `Lending sector`) %>%
  summarise(Nobs = n()) %>%
  arrange(-Nobs) %>%
  print_table_conditional()
TC_LENDERS Lending sector Nobs
A All sectors 136165
B Banks, domestic 38137

TC_BORROWERS, Borrowing sector

Code 
TOTAL_CREDIT %>%
  group_by(TC_BORROWERS, `Borrowing sector`) %>%
  summarise(Nobs = n()) %>%
  arrange(-Nobs) %>%
  print_table_conditional()
TC_BORROWERS Borrowing sector Nobs
P Private non-financial sector 76571
G General government 27944
H Households & NPISHs 26375
N Non-financial corporations 26048
C Non financial sector 17364

VALUATION, Valuation

Code 
TOTAL_CREDIT %>%
  group_by(VALUATION, `Valuation method`) %>%
  summarise(Nobs = n()) %>%
  arrange(-Nobs) %>%
  print_table_conditional()
VALUATION Valuation method Nobs
M Market value 157575
N Nominal value 16727

TC_ADJUST, Type of adjustment

Code 
TOTAL_CREDIT %>%
  group_by(TC_ADJUST, `Adjustment`) %>%
  summarise(Nobs = n()) %>%
  arrange(-Nobs) %>%
  print_table_conditional()
TC_ADJUST Adjustment Nobs
A Adjusted for breaks 142415
U Unadjusted 31887

iso3c, iso2c, Borrowers’ country

Code 
TOTAL_CREDIT %>%
  arrange(iso3c, date) %>%
  group_by(iso3c, iso2c, `Borrowers' country`) %>%
  summarise(Nobs = n(),
            start = first(date),
            end = last(date)) %>%
  arrange(-Nobs) %>%
  mutate(Flag = gsub(" ", "-", str_to_lower(`Borrowers' country`)),
         Flag = paste0('<img src="../../bib/flags/vsmall/', Flag, '.png" alt="Flag">')) %>%
  select(Flag, everything()) %>%
  {if (is_html_output()) datatable(., filter = 'top', rownames = F, escape = F) else .}

FREQ, Frequency

Code 
TOTAL_CREDIT %>%
  group_by(FREQ, Frequency) %>%
  summarise(Nobs = n()) %>%
  arrange(-Nobs) %>%
  {if (is_html_output()) print_table(.) else .}
FREQ Frequency Nobs
Q Quarterly 174302

UNIT_TYPE, Unit type

Code 
TOTAL_CREDIT %>%
  group_by(UNIT_TYPE, `Unit type`) %>%
  summarise(Nobs = n()) %>%
  arrange(-Nobs) %>%
  {if (is_html_output()) print_table(.) else .}
UNIT_TYPE Unit type Nobs
XDC Domestic currency (incl. conv. to current ccy made using a fix parity) 77951
USD US dollar 47282
770 Percentage of GDP 46748
799 Percentage of GDP (using PPP exchange rates) 2321

All

Sweden

All

Code 
TOTAL_CREDIT %>%
  filter(iso2c %in% c("SE"),
         UNIT_TYPE == "770",
         TC_LENDERS == "A",
         VALUATION == "M",
         TC_ADJUST == "A") %>%
  ggplot(.) + theme_minimal() + xlab("") + ylab("Credit to GDP (% of GDP)") +
  geom_line(aes(x = date, y = value/100, color = `Borrowing sector`)) +
  scale_x_date(breaks = seq(1940, 2020, 5) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  scale_y_continuous(breaks = 0.01*seq(0, 700, 25),
                labels = percent_format(accuracy = 1)) +
  scale_color_manual(values = viridis(6)[1:5]) +
  theme(legend.position = c(0.2, 0.75),
        legend.title = element_blank())

1980-2000

Code 
TOTAL_CREDIT %>%
  filter(iso2c %in% c("SE"),
         UNIT_TYPE == "770",
         TC_LENDERS == "A",
         VALUATION == "M",
         TC_ADJUST == "A",
         date >= as.Date("1980-01-01"),
         date <= as.Date("1998-01-01")) %>%
  ggplot(.) + theme_minimal() + xlab("") + ylab("Credit to GDP (% of GDP)") +
  geom_line(aes(x = date, y = value/100, color = `Borrowing sector`)) +
  scale_x_date(breaks = seq(1940, 2020, 2) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  scale_y_continuous(breaks = 0.01*seq(0, 700, 10),
                labels = percent_format(accuracy = 1)) +
  scale_color_manual(values = viridis(4)[1:3]) +
  theme(legend.position = c(0.2, 0.85),
        legend.title = element_blank())

Peru

Code 
TOTAL_CREDIT %>%
  filter(iso2c %in% c("PE"),
         UNIT_TYPE == "770",
         TC_LENDERS == "A",
         VALUATION == "M",
         TC_ADJUST == "A") %>%
  ggplot(.) + theme_minimal() + xlab("") + ylab("Credit to GDP (% of GDP)") +
  geom_line(aes(x = date, y = value/100, color = `Borrowing sector`)) +
  scale_x_date(breaks = seq(1940, 2020, 5) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  scale_y_continuous(breaks = 0.01*seq(0, 700, 25),
                labels = percent_format(accuracy = 1)) +
  scale_color_manual(values = viridis(6)[1:5]) +
  theme(legend.position = c(0.2, 0.75),
        legend.title = element_blank())

Netherlands

Code 
TOTAL_CREDIT %>%
  filter(iso2c %in% c("NL"),
         UNIT_TYPE == "770",
         TC_LENDERS == "A",
         VALUATION == "M",
         TC_ADJUST == "A") %>%
  ggplot(.) + theme_minimal() + xlab("") + ylab("Credit to GDP (% of GDP)") +
  geom_line(aes(x = date, y = value/100, color = `Borrowing sector`)) +
  scale_x_date(breaks = seq(1940, 2020, 5) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  scale_y_continuous(breaks = 0.01*seq(0, 700, 25),
                labels = percent_format(accuracy = 1)) +
  scale_color_manual(values = viridis(6)[1:5]) +
  theme(legend.position = c(0.2, 0.75),
        legend.title = element_blank())

Denmark

Code 
TOTAL_CREDIT %>%
  filter(iso2c %in% c("DK"),
         UNIT_TYPE == "770",
         TC_LENDERS == "A",
         VALUATION == "M",
         TC_ADJUST == "A") %>%
  ggplot(.) + theme_minimal() + xlab("") + ylab("Credit to GDP (% of GDP)") +
  geom_line(aes(x = date, y = value/100, color = `Borrowing sector`)) +
  scale_x_date(breaks = seq(1940, 2020, 5) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  scale_y_continuous(breaks = 0.01*seq(0, 700, 25),
                labels = percent_format(accuracy = 1)) +
  scale_color_manual(values = viridis(6)[1:5]) +
  theme(legend.position = c(0.2, 0.75),
        legend.title = element_blank())

France

Code 
TOTAL_CREDIT %>%
  filter(iso2c %in% c("FR"),
         UNIT_TYPE == "770",
         TC_LENDERS == "A",
         VALUATION == "M",
         TC_ADJUST == "A") %>%
  ggplot(.) + theme_minimal() + xlab("") + ylab("Credit to GDP (% of GDP)") +
  geom_line(aes(x = date, y = value/100, color = `Borrowing sector`)) +
  scale_x_date(breaks = seq(1940, 2020, 5) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  scale_y_continuous(breaks = 0.01*seq(0, 700, 25),
                labels = percent_format(accuracy = 1)) +
  scale_color_manual(values = viridis(6)[1:5]) +
  theme(legend.position = c(0.2, 0.75),
        legend.title = element_blank())

United States

Code 
TOTAL_CREDIT %>%
  filter(iso2c %in% c("US"),
         UNIT_TYPE == "770",
         TC_LENDERS == "A",
         VALUATION == "M",
         TC_ADJUST == "A") %>%
  ggplot(.) + theme_minimal() + xlab("") + ylab("Credit to GDP (% of GDP)") +
  geom_line(aes(x = date, y = value/100, color = `Borrowing sector`)) +
  scale_x_date(breaks = seq(1940, 2020, 5) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  scale_y_continuous(breaks = 0.01*seq(0, 700, 25),
                labels = percent_format(accuracy = 1)) +
  scale_color_manual(values = viridis(6)[1:5]) +
  theme(legend.position = c(0.2, 0.75),
        legend.title = element_blank())

Korea

All

Code 
TOTAL_CREDIT %>%
  filter(iso2c %in% c("KR"),
         UNIT_TYPE == "770",
         TC_LENDERS == "A",
         TC_BORROWERS %in% c("G", "H", "N"),
         VALUATION == "M",
         TC_ADJUST == "A") %>%
  left_join(tibble(TC_BORROWERS = c("G", "H", "N"),
                   Tc_borrowers = c("Dette publique", "Dette des ménages", "Dette des entreprises"))) %>%
  ggplot(.) + theme_minimal() + xlab("") + ylab("Dette / PIB en Corée du Sud (en % ou plutôt années de PIB)") +
  geom_line(aes(x = date, y = value/100, color = Tc_borrowers)) +
  scale_x_date(breaks = seq(1940, 2020, 5) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  scale_color_manual(values = viridis(4)[1:3]) +
  scale_y_continuous(breaks = 0.01*seq(0, 700, 10),
                labels = percent_format(accuracy = 10, p = "")) +
  theme(legend.position = c(0.15, 0.85),
        legend.title = element_blank())

1990-

Code 
TOTAL_CREDIT %>%
  filter(iso2c %in% c("KR"),
         UNIT_TYPE == "770",
         TC_LENDERS == "A",
         TC_BORROWERS %in% c("G", "H", "N"),
         date >= as.Date("1990-01-01"),
         VALUATION == "M",
         TC_ADJUST == "A") %>%
  left_join(tibble(TC_BORROWERS = c("G", "H", "N"),
                   Tc_borrowers = c("Dette publique", "Dette des ménages", "Dette des entreprises"))) %>%
  ggplot(.) + theme_minimal() + xlab("") + ylab("Dette / PIB en Corée du Sud (en % ou plutôt années de PIB)") +
  geom_line(aes(x = date, y = value/100, color = Tc_borrowers)) +
  scale_x_date(breaks = seq(1940, 2022, 5) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  scale_color_manual(values = viridis(4)[1:3]) +
  scale_y_continuous(breaks = 0.01*seq(0, 700, 10),
                labels = percent_format(accuracy = 10, p = "")) +
  theme(legend.position = c(0.15, 0.85),
        legend.title = element_blank())

2005-

Code 
TOTAL_CREDIT %>%
  filter(iso2c %in% c("KR"),
         UNIT_TYPE == "770",
         TC_LENDERS == "A",
         TC_BORROWERS %in% c("G", "H", "N"),
         date >= as.Date("2005-01-01"),
         VALUATION == "M",
         TC_ADJUST == "A") %>%
  left_join(tibble(TC_BORROWERS = c("G", "H", "N"),
                   Tc_borrowers = c("Dette publique", "Dette des ménages", "Dette des entreprises"))) %>%
  ggplot(.) + theme_minimal() + xlab("") + ylab("Dette / PIB en Corée du Sud (en % ou plutôt années de PIB)") +
  geom_line(aes(x = date, y = value/100, color = Tc_borrowers)) +
  scale_x_date(breaks = seq(1940, 2022, 2) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  scale_color_manual(values = viridis(4)[1:3]) +
  scale_y_continuous(breaks = 0.01*seq(0, 700, 10),
                labels = percent_format(accuracy = 10, p = "")) +
  theme(legend.position = c(0.15, 0.85),
        legend.title = element_blank())

Spain

Code 
TOTAL_CREDIT %>%
  filter(iso2c %in% c("ES"),
         UNIT_TYPE == "770",
         TC_LENDERS == "A",
         VALUATION == "M",
         TC_ADJUST == "A") %>%
  ggplot(.) + theme_minimal() + xlab("") + ylab("Credit to GDP (% of GDP)") +
  geom_line(aes(x = date, y = value/100, color = `Borrowing sector`)) +
  scale_x_date(breaks = seq(1940, 2020, 5) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  scale_y_continuous(breaks = 0.01*seq(0, 700, 25),
                labels = percent_format(accuracy = 1)) +
  scale_color_manual(values = viridis(6)[1:5]) +
  theme(legend.position = c(0.2, 0.75),
        legend.title = element_blank())

All but Non financial sector

Eurozone

Code 
TOTAL_CREDIT %>%
  filter(iso2c %in% c("XM", "US"),
         UNIT_TYPE == "770",
         TC_LENDERS == "A",
         VALUATION == "M",
         TC_ADJUST == "A",
         TC_BORROWERS %in% c("P", "H", "N")) %>%
  filter(date >= as.Date("2000-01-01")) %>%
  ggplot(.) + theme_minimal() + xlab("") + ylab("Credit to GDP (% of GDP)") +
  geom_line(aes(x = date, y = value/100, color = `Borrowing sector`, linetype = iso2c)) +
  scale_x_date(breaks = seq(1940, 2025, 2) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  scale_y_continuous(breaks = 0.01*seq(0, 700, 25),
                labels = percent_format(accuracy = 1)) +
  theme(legend.position = c(0.2, 0.75),
        legend.title = element_blank())

United States

Code 
TOTAL_CREDIT %>%
  filter(iso2c == "US",
         UNIT_TYPE == "770",
         TC_LENDERS == "A",
         VALUATION == "M",
         TC_ADJUST == "A",
         TC_BORROWERS %in% c("P", "H", "N")) %>%
  ggplot(.) + theme_minimal() + xlab("") + ylab("Credit to GDP (% of GDP)") +
  geom_line(aes(x = date, y = value/100, color = `Borrowing sector`)) +
  scale_x_date(breaks = seq(1940, 2020, 5) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  scale_y_continuous(breaks = 0.01*seq(0, 700, 25),
                labels = percent_format(accuracy = 1)) +
  scale_color_manual(values = viridis(4)[1:3]) +
  theme(legend.position = c(0.2, 0.75),
        legend.title = element_blank())

France

Code 
TOTAL_CREDIT %>%
  filter(iso2c == "FR",
         UNIT_TYPE == "770",
         TC_LENDERS == "A",
         VALUATION == "M",
         TC_ADJUST == "A",
         TC_BORROWERS %in% c("P", "H", "N")) %>%
  ggplot(.) + theme_minimal() + xlab("") + ylab("Credit to GDP (% of GDP)") +
  geom_line(aes(x = date, y = value/100, color = `Borrowing sector`)) +
  scale_x_date(breaks = seq(1940, 2020, 5) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  scale_y_continuous(breaks = 0.01*seq(0, 700, 25),
                labels = percent_format(accuracy = 1)) +
  scale_color_manual(values = viridis(4)[1:3]) +
  theme(legend.position = c(0.2, 0.75),
        legend.title = element_blank())

Germany

Code 
TOTAL_CREDIT %>%
  filter(iso2c == "DE",
         UNIT_TYPE == "770",
         TC_LENDERS == "A",
         VALUATION == "M",
         TC_ADJUST == "A",
         TC_BORROWERS %in% c("P", "H", "N")) %>%
  ggplot(.) + theme_minimal() + xlab("") + ylab("Credit to GDP (% of GDP)") +
  geom_line(aes(x = date, y = value/100, color = `Borrowing sector`)) +
  scale_x_date(breaks = seq(1940, 2020, 5) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  scale_y_continuous(breaks = 0.01*seq(0, 700, 20),
                labels = percent_format(accuracy = 1)) +
  scale_color_manual(values = viridis(4)[1:3]) +
  theme(legend.position = c(0.2, 0.75),
        legend.title = element_blank())

Non-financial corporations

Table

Code 
TOTAL_CREDIT %>%
  filter(UNIT_TYPE == "770",
         TC_BORROWERS %in% c("N"),
         TC_LENDERS == "A",
         VALUATION == "M") %>%
  group_by(iso2c, `Borrowers' country`) %>%
  arrange(date) %>%
  summarise(date = last(date),
            value = last(value)) %>%
  arrange(-value) %>%
  mutate(Flag = gsub(" ", "-", str_to_lower(`Borrowers' country`)),
         Flag = paste0('<img src="../../bib/flags/vsmall/', Flag, '.png" alt="Flag">')) %>%
  select(Flag, everything()) %>%
  {if (is_html_output()) datatable(., filter = 'top', rownames = F, escape = F) else .}

France, Germany, Italy

Code 
TOTAL_CREDIT %>%
  filter(iso2c %in% c("FR", "DE", "IT"),
         UNIT_TYPE == "770",
         TC_BORROWERS %in% c("N"),
         TC_LENDERS == "A",
         VALUATION == "M") %>%
  left_join(colors, by = c("Borrowers' country" = "country")) %>%
  rename(`Reference area` = `Borrowers' country`) %>%
  mutate(value = value/100) %>%
  group_by(date) %>%
  filter(n() == 3) %>%
  ggplot(.) + theme_minimal() + xlab("") + ylab("Debt to GDP (% of GDP)") +
  geom_line(aes(x = date, y = value, color = color)) +
  scale_color_identity() + add_3flags +
  scale_x_date(breaks = seq(1940, 2020, 5) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  scale_y_continuous(breaks = 0.01*seq(0, 700, 10),
                labels = percent_format(accuracy = 1)) +
  theme(legend.position = c(0.15, 0.75),
        legend.title = element_blank())

Private non-financial sector (% of GDP)

Table

Code 
TOTAL_CREDIT %>%
  filter(UNIT_TYPE == "770",
         TC_BORROWERS %in% c("N"),
         TC_LENDERS == "A",
         VALUATION == "M") %>%
  group_by(iso2c, `Borrowers' country`) %>%
  arrange(date) %>%
  summarise(date = last(date),
            value = last(value)) %>%
  arrange(-value) %>%
  mutate(Flag = gsub(" ", "-", str_to_lower(`Borrowers' country`)),
         Flag = paste0('<img src="../../bib/flags/vsmall/', Flag, '.png" alt="Flag">')) %>%
  select(Flag, everything()) %>%
  {if (is_html_output()) datatable(., filter = 'top', rownames = F, escape = F) else .}

Luxembourg, Honk Kong SAR, Sweden

Code 
TOTAL_CREDIT %>%
  filter(iso2c %in% c("LU", "FI", "SE"),
         UNIT_TYPE == "770",
         TC_BORROWERS %in% c("N"),
         TC_LENDERS == "A",
         VALUATION == "M") %>%
  left_join(colors, by = c("Borrowers' country" = "country")) %>%
  rename(`Reference area` = `Borrowers' country`) %>%
  mutate(value = value/100) %>%
  group_by(date) %>%
  filter(n() == 3) %>%
  mutate(color = ifelse(iso2c == "HK", color2, color)) %>%
  ggplot(.) + theme_minimal() + xlab("") + ylab("Debt to GDP (% of GDP)") +
  geom_line(aes(x = date, y = value, color = color)) +
  scale_color_identity() + add_3flags +
  scale_x_date(breaks = seq(1940, 2020, 5) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  scale_y_continuous(breaks = 0.01*seq(0, 700, 10),
                labels = percent_format(accuracy = 1)) +
  theme(legend.position = c(0.15, 0.75),
        legend.title = element_blank())

Spain, Ireland

Code 
TOTAL_CREDIT %>%
  filter(iso2c %in% c("IE", "ES"),
         UNIT_TYPE == "770",
         TC_BORROWERS %in% c("N"),
         TC_LENDERS == "A",
         date >= as.Date("2000-01-01"),
         date <= as.Date("2012-01-01"),
         VALUATION == "M") %>%
  left_join(colors, by = c("Borrowers' country" = "country")) %>%
  rename(`Reference area` = `Borrowers' country`) %>%
  mutate(value = value/100) %>%
  ggplot(.) + theme_minimal() + xlab("") + ylab("Debt to GDP (% of GDP)") +
  geom_line(aes(x = date, y = value, color = color)) +
  scale_color_identity() + add_2flags +
  scale_x_date(breaks = seq(1940, 2020, 1) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  scale_y_continuous(breaks = 0.01*seq(0, 700, 10),
                labels = percent_format(accuracy = 1)) +
  theme(legend.position = c(0.15, 0.75),
        legend.title = element_blank())

France, Germany, Italy, Spain, United States

Code 
TOTAL_CREDIT %>%
  filter(iso2c %in% c("FR", "DE", "IT", "ES", "US"),
         UNIT_TYPE == "770",
         TC_BORROWERS %in% c("N"),
         TC_LENDERS == "A",
         VALUATION == "M") %>%
  left_join(colors, by = c("Borrowers' country" = "country")) %>%
  rename(`Reference area` = `Borrowers' country`) %>%
  mutate(value = value/100) %>%
  group_by(date) %>%
  filter(n() == 5) %>%
  ggplot(.) + theme_minimal() + xlab("") + ylab("Debt to GDP (% of GDP)") +
  geom_line(aes(x = date, y = value, color = color)) +
  scale_color_identity() + add_5flags +
  scale_x_date(breaks = seq(1940, 2025, 5) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  scale_y_continuous(breaks = 0.01*seq(0, 700, 10),
                labels = percent_format(accuracy = 1)) +
  theme(legend.position = c(0.15, 0.75),
        legend.title = element_blank())

Japan, United Kingdom, United States

Code 
TOTAL_CREDIT %>%
  filter(iso2c %in% c("JP", "GB", "US"),
         UNIT_TYPE == "770",
         TC_BORROWERS %in% c("N"),
         TC_LENDERS == "A",
         VALUATION == "M") %>%
  left_join(colors, by = c("Borrowers' country" = "country")) %>%
  rename(`Reference area` = `Borrowers' country`) %>%
  mutate(value = value/100) %>%
  group_by(date) %>%
  filter(n() == 3) %>%
  ggplot(.) + theme_minimal() + xlab("") + ylab("Debt to GDP (% of GDP)") +
  geom_line(aes(x = date, y = value, color = color)) +
  scale_color_identity() + add_3flags +
  scale_x_date(breaks = seq(1940, 2020, 5) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  scale_y_continuous(breaks = 0.01*seq(0, 700, 10),
                labels = percent_format(accuracy = 1)) +
  theme(legend.position = c(0.15, 0.75),
        legend.title = element_blank())