Last observation: 2021-03-31 (N = 51)
First observation: 1945-12-31 (N = 46)
Last data update: 16 aoû 2026, 20:48. Last compile: 17 aoû 2026, 21:56
Table B.101 Balance Sheet of Households and Nonprofit Organizations - B101
Data - FRB
Info
Layout
png
Javascript
Code
Z1_csv_var |>
filter(table == "B101") |>
select(pos, variable, variable_desc) |>
print_table_conditional()Checkable Deposits
All
Code
Z1 |>
filter(SERIES_NAME %in% c("FL153020005.Q", "FA086902005.Q"),
OBS_STATUS == "A") |>
select(SERIES_NAME, TIME_PERIOD, OBS_VALUE) |>
spread(SERIES_NAME, OBS_VALUE) |>
ggplot() + theme_minimal() +
geom_line(aes(x = TIME_PERIOD, y = `FL153020005.Q`/`FA086902005.Q`)) +
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("HH checkable deposits and currency (% 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_y_continuous(breaks = 0.01*seq(-100, 600, 2),
labels = scales::percent_format(accuracy = 1))
1990-
Code
Z1 |>
filter(SERIES_NAME %in% c("FL153020005.Q", "FA086902005.Q"),
OBS_STATUS == "A",
TIME_PERIOD >= as.Date("2000-01-01")) |>
select(SERIES_NAME, TIME_PERIOD, OBS_VALUE) |>
spread(SERIES_NAME, OBS_VALUE) |>
ggplot() + theme_minimal() +
geom_line(aes(x = TIME_PERIOD, y = `FL153020005.Q`/`FA086902005.Q`)) +
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("HH checkable deposits and currency (% 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_y_continuous(breaks = 0.01*seq(-100, 600, 2),
labels = scales::percent_format(accuracy = 1))
1990-
Code
Z1 |>
filter(SERIES_NAME %in% c("FL153020005.Q", "FA086902005.Q", "LM713061103.Q"),
OBS_STATUS == "A",
TIME_PERIOD >= as.Date("2000-01-01")) |>
select(SERIES_NAME, TIME_PERIOD, OBS_VALUE) |>
spread(SERIES_NAME, OBS_VALUE) |>
transmute(TIME_PERIOD,
`Checkable deposits and currency held by Households` = `FL153020005.Q`/`FA086902005.Q`,
`Treasury securities held by the Central Bank` = `LM713061103.Q`/`FA086902005.Q`) |>
gather(variable, value, -TIME_PERIOD) |>
ggplot() + theme_minimal() +
geom_line(aes(x = TIME_PERIOD, 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("% 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(3)[1:2]) +
scale_y_continuous(breaks = 0.01*seq(-100, 600, 2),
labels = scales::percent_format(accuracy = 1))
Liabilities
Code
Z1_csv_var |>
mutate(line = parse_number(pos)) |>
filter(table == "B101",
line %in% c(30, 33, 34)) |>
left_join(Z1_csv, by = c("variable", "table")) |>
select(date, table, pos, variable_desc, 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_desc)) +
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_y_continuous(breaks = 0.01*seq(-100, 600, 10),
labels = scales::percent_format(accuracy = 1))
Structures, Real Estate
Code
Z1_csv_var |>
mutate(line = parse_number(pos)) |>
filter(table == "B101",
line %in% c(4, 46)) |>
left_join(Z1_csv, by = c("variable", "table")) |>
select(date, table, pos, variable_desc, 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_desc)) +
theme(legend.title = element_blank(),
legend.position = c(0.5, 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_y_continuous(breaks = 0.01*seq(-100, 600, 10),
labels = scales::percent_format(accuracy = 1),
limits = c(0.5, 2))
Net Worth
Code
Z1_csv_var |>
mutate(line = parse_number(pos)) |>
filter(table == "B101",
line %in% c(1, 9, 40)) |>
left_join(Z1_csv, by = c("variable", "table")) |>
select(date, table, pos, variable_desc, 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_desc)) +
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_y_continuous(breaks = 0.01*seq(-100, 700, 50),
labels = scales::percent_format(accuracy = 1))
Corporate Equities, Pensions, Real Estate
Code
Z1_csv_var |>
mutate(line = parse_number(pos)) |>
filter(table == "B101",
line %in% c(23, 27, 3)) |>
left_join(Z1_csv, by = c("variable", "table")) |>
select(date, table, pos, variable_desc, 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_desc)) +
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_y_continuous(breaks = 0.01*seq(-100, 600, 10),
labels = scales::percent_format(accuracy = 1))
Residential Structures
Code
Z1_csv_var |>
mutate(line = parse_number(pos)) |>
filter(table == "B101",
line %in% c(45, 46)) |>
left_join(Z1_csv, by = c("variable", "table")) |>
select(date, table, pos, variable_desc, 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_desc)) +
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_y_continuous(breaks = 0.01*seq(-100, 600, 10),
labels = scales::percent_format(accuracy = 1))
2019
Billions
Code
Z1_csv_var |>
filter(table == "B101") |>
left_join(Z1_csv |>
filter(date == as.Date("2019-12-31")),
by = c("variable", "table")) |>
select(table, pos, variable_desc, value) %>%
mutate(value = round(value/1000) %>% paste0("$ ", ., " Bn")) %>%
{if (is_html_output()) datatable(., filter = 'top', rownames = F) else .}