Financial transactions - nasa_10_f_tr

Data - Eurostat

Info

Last observation: Annual: 2025 (N = 326,287)

First observation: Annual: 1990 (N = 6,900)

Last data update: 13 aoû 2026, 01:50. Last compile: 13 aoû 2026, 04:53

Structure

freq

unit

co_nco

sector

finpos

na_item

geo

Net Acquisition of Loans (F4), Total Economy

France, Germany, Italy, Spain

nasa_10_f_tr |>
  filter(geo %in% c("FR", "DE", "IT", "ES"),
         sector == "S1",
         finpos == "ASS",
         unit == "PC_GDP",
         na_item == "F4") |>
  year_to_date() |>
  left_join(colors, by = c("Geo" = "country")) |>
  mutate(values = values/100) |>
  ggplot() + geom_line(aes(x = date, y = values, color = color)) +
  theme_minimal() + scale_color_identity() + add_flags +
  scale_x_date(breaks = as.Date(paste0(seq(1990, 2100, 5), "-01-01")),
               labels = date_format("%Y")) +
  xlab("") + ylab("Net acquisition of loans (% of GDP)") +
  scale_y_continuous(labels = scales::percent_format(accuracy = 1)) +
  geom_hline(yintercept = 0, linetype = "dashed", color = "black")

France: Financial Transactions by Instrument, Total Economy

Currency & Deposits, Debt Securities, Loans, Equity

nasa_10_f_tr |>
  filter(geo == "FR",
         sector == "S1",
         finpos == "ASS",
         unit == "PC_GDP",
         na_item %in% c("F2", "F3", "F4", "F5")) |>
  year_to_date() |>
  mutate(values = values/100) |>
  ggplot() + geom_line(aes(x = date, y = values, color = Na_item)) +
  theme_minimal() +
  scale_x_date(breaks = as.Date(paste0(seq(1990, 2100, 5), "-01-01")),
               labels = date_format("%Y")) +
  theme(legend.position = "right", legend.title = element_blank()) +
  xlab("") + ylab("Net acquisition of financial assets (% of GDP)") +
  scale_y_continuous(labels = scales::percent_format(accuracy = 1)) +
  geom_hline(yintercept = 0, linetype = "dashed", color = "black")

Latest Year by Instrument and Country

latest_y <- nasa_10_f_tr |>
  filter(geo %in% c("FR", "DE", "IT", "ES"),
         sector == "S1",
         finpos == "ASS",
         unit == "PC_GDP",
         na_item == "F4",
         co_nco == "CO",
         !is.na(values)) |>
  summarise(m = max(time)) |>
  pull(m)

nasa_10_f_tr |>
  filter(geo %in% c("FR", "DE", "IT", "ES"),
         sector == "S1",
         finpos == "ASS",
         unit == "PC_GDP",
         na_item %in% c("F2", "F3", "F4", "F5", "F6"),
         co_nco == "CO",
         time == latest_y) |>
  mutate(values = values/100) |>
  select(Na_item, Geo, values) |>
  spread(Geo, values) |>
  print_table_conditional()

Na_item	France	Germany	Italy	Spain
Currency and deposits	0.068	0.059	0.032	0.046
Debt securities	0.077	0.039	0.037	0.057
Equity and investment fund shares	0.052	0.049	0.028	0.050
Insurance, pensions and standardised guarantees	0.004	0.000	0.000	0.001
Loans	0.036	0.028	0.006	0.017

na_item

na_item <- read_parquet("na_item.parquet")
nasa_10_f_tr |>
  
  group_by(na_item, Na_item) |>
  summarise(Nobs = n()) |>
  arrange(-Nobs) %>%
  {if (is_html_output()) datatable(., filter = 'top', rownames = F) else .}