Table B.1 Derivation of U.S. Net Wealth - B1

Data - FRB

Author

François Geerolf

Info

Last observation: 2020-06-30 (N = 35)

First observation: 1945-12-31 (N = 31)

Last data update: 16 aoû 2026, 20:48. Last compile: 17 aoû 2026, 21:53

Example: LM152010005.Q

LM152010005.Q: Households and nonprofit organizations; nonfinancial assets

– SERIES_PREFIX == “LM”: Market value levels, NSA

– SERIES_SECTOR == “15”: Households and nonprofit organizations (S14+S15)

– SERIES_INSTRUMENT == “20100”: Nonfinancial assets

– SERIES_TYPE == “5”: Computed series

Layout

Examples

Net Worth

Z1_csv

All

Code

Z1_table_variable |>
  mutate(line = parse_number(pos)) |>
  filter(table == "B1",
         line %in% c(2, 7, 13)) |>
  left_join(Z1_csv, by = c("variable", "table")) |>
  select(date, table, pos, Variable, value) |>
  left_join(gdp_Q |> rename(gdp = value), by = "date") |>
  mutate(value = value / gdp) |>
  ggplot() + theme_minimal() +
  geom_line(aes(x = date, y = value, color = Variable, linetype = Variable)) +
  theme(legend.title = element_blank(),
        legend.position = c(0.4, 0.9)) +
  scale_x_date(breaks = seq(1930, 2100, 5) |> paste0("-01-01") |> as.Date(),
               labels = date_format("%Y")) +
  ylab("% of GDP") + xlab("") +
  geom_rect(data = nber_recessions |>
              filter(Peak > as.Date("1950-01-01")), 
            aes(xmin = Peak, xmax = Trough, ymin = -Inf, ymax = +Inf), 
            fill = 'grey', alpha = 0.5) +
  scale_color_manual(values = viridis(4)[1:3]) +
  scale_y_continuous(breaks = 0.01*seq(-100, 700, 50),
                     labels = scales::percent_format(accuracy = 1))

2000-

Code

Z1_table_variable |>
  mutate(line = parse_number(pos)) |>
  filter(table == "B1",
         line %in% c(2, 7, 13)) |>
  left_join(Z1_csv, by = c("variable", "table")) |>
  select(date, table, pos, Variable, value) |>
  left_join(gdp_Q |> rename(gdp = value), by = "date") |>
  mutate(value = value / gdp) |>
  filter(date >= as.Date("2000-01-01")) |>
  ggplot() + theme_minimal() +
  geom_line(aes(x = date, y = value, color = Variable, linetype = Variable)) +
  theme(legend.title = element_blank(),
        legend.position = c(0.4, 0.9)) +
  scale_x_date(breaks = seq(1930, 2100, 5) |> paste0("-01-01") |> as.Date(),
               labels = date_format("%Y")) +
  ylab("% of GDP") + xlab("") +
  geom_rect(data = nber_recessions |>
              filter(Peak > as.Date("2000-01-01")), 
            aes(xmin = Peak, xmax = Trough, ymin = -Inf, ymax = +Inf), 
            fill = 'grey', alpha = 0.5) +
  scale_color_manual(values = viridis(4)[1:3]) +
  scale_y_continuous(breaks = 0.01*seq(-100, 700, 50),
                     labels = scales::percent_format(accuracy = 1))

Z1_SDMX

Code

Z1 |>
  filter(SERIES_NAME %in% c("LM152010005.Q", "LM112010005.Q", "LM883164105.Q"),
         OBS_STATUS != "ND") |>
  rename(date = TIME_PERIOD) |>
  left_join(gdp_Q |>
              rename(gdp = value), by = "date") |>
  left_join(SERIES_SECTOR, by = "SERIES_SECTOR") |>
  left_join(SERIES_INSTRUMENT, by = "SERIES_INSTRUMENT") |>
  ggplot() + theme_minimal() + ylab("% of GDP") + xlab("") +
  geom_line(aes(x = date, y = OBS_VALUE / gdp, color = paste0(Series_instrument, Series_sector))) +
  theme(legend.title = element_blank(),
        legend.position = c(0.4, 0.8)) +
  scale_x_date(breaks = seq(1930, 2100, 5) |> paste0("-01-01") |> as.Date(),
               labels = date_format("%Y")) +
  geom_rect(data = nber_recessions |>
              filter(Peak > as.Date("1950-01-01")), 
            aes(xmin = Peak, xmax = Trough, ymin = -Inf, ymax = +Inf), 
            fill = 'grey', alpha = 0.5) +
  scale_color_manual(values = viridis(4)[1:3]) +
  scale_y_continuous(breaks = 0.01*seq(-100, 700, 50),
                     labels = scales::percent_format(accuracy = 1))

Corporate Equities, Pensions, Real Estate

Code

Z1_table_variable |>
  mutate(line = parse_number(pos)) |>
  filter(table == "B1",
         line %in% c(31,34)) |>
  left_join(Z1_csv, by = c("variable", "table")) |>
  select(date, table, pos, Variable, value) |>
  left_join(gdp_Q |> rename(gdp = value), by = "date") |>
  mutate(value = value / gdp) |>
  ggplot() + theme_minimal() + ylab("% of GDP") + xlab("") +
  geom_line(aes(x = date, y = value, color = Variable)) +
  theme(legend.title = element_blank(),
        legend.position = c(0.4, 0.9)) +
  scale_x_date(breaks = seq(1930, 2100, 5) |> paste0("-01-01") |> as.Date(),
               labels = date_format("%Y")) +
  geom_rect(data = nber_recessions |>
              filter(Peak > as.Date("1950-01-01")), 
            aes(xmin = Peak, xmax = Trough, ymin = -Inf, ymax = +Inf), 
            fill = 'grey', alpha = 0.5) +
  scale_color_manual(values = viridis(3)[1:2]) +
  scale_y_continuous(breaks = 0.01*seq(-100, 600, 20),
                     labels = scales::percent_format(accuracy = 1))

Corporate Equities, Real Estate

English

Code

Z1_table_variable |>
  mutate(line = parse_number(pos)) |>
  filter(table == "B1",
         line %in% c(3, 14)) |>
  left_join(Z1_csv, by = c("variable", "table")) |>
  left_join(gdp_Q |> rename(gdp = value), by = "date") |>
  mutate(value = value / gdp,
         Variable = case_when(line == 3 ~ "Real estate (households)",
                              line == 14 ~ "Equities (corporate sector)")) |>
  ggplot() + theme_minimal() +
  geom_line(aes(x = date, y = value, color = Variable, linetype = Variable)) +
  theme(legend.title = element_blank(),
        legend.position = c(0.4, 0.9)) +
  scale_x_date(breaks = seq(1930, 2100, 5) |> paste0("-01-01") |> as.Date(),
               labels = date_format("%Y")) +
  ylab("% of GDP") + xlab("") +
  geom_rect(data = nber_recessions |>
              filter(Peak > as.Date("1950-01-01")), 
            aes(xmin = Peak, xmax = Trough, ymin = -Inf, ymax = +Inf), 
            fill = 'grey', alpha = 0.5) +
  scale_color_manual(values = viridis(4)[1:3]) +
  scale_y_continuous(breaks = 0.01*seq(-100, 600, 10),
                     labels = scales::percent_format(accuracy = 1))

Français

Code

Z1_table_variable |>
  mutate(line = parse_number(pos)) |>
  filter(table == "B1",
         line %in% c(3, 14)) |>
  left_join(Z1_csv, by = c("variable", "table")) |>
  left_join(gdp_Q |> rename(gdp = value), by = "date") |>
  mutate(value = value / gdp,
         Variable = case_when(line == 3 ~ "Immobilier (ménages)",
                              line == 14 ~ "Actions (secteur corporate)")) |>
  ggplot() + theme_minimal() +
  geom_line(aes(x = date, y = value, color = Variable)) +
  theme(legend.title = element_blank(),
        legend.position = c(0.4, 0.9)) +
  scale_x_date(breaks = seq(1930, 2100, 5) |> paste0("-01-01") |> as.Date(),
               labels = date_format("%Y")) +
  ylab("% du PIB ") + xlab("") +
  geom_rect(data = nber_recessions |>
              filter(Peak > as.Date("1950-01-01")), 
            aes(xmin = Peak, xmax = Trough, ymin = -Inf, ymax = +Inf), 
            fill = 'grey', alpha = 0.5) +
  scale_color_manual(values = viridis(3)[1:2]) +
  scale_y_continuous(breaks = 0.01*seq(-100, 600, 10),
                     labels = scales::percent_format(accuracy = 1))

2019

Billions

Code

Z1_table_variable |>
  filter(table == "B1") |>
  left_join(Z1_csv |>
              filter(date == as.Date("2019-12-31")),
            by = c("variable", "table")) |>
  select(table, pos, Variable, value) %>%
  mutate(value = round(value/1000) %>% paste0("$ ", ., " Bn")) %>%
  {if (is_html_output()) datatable(., filter = 'top', rownames = F) else .}