Table B.101.e Balance Sheet of Households and Nonprofit Organizations with Debt and Equity Holdings Detail - B101E

Data - BEA

~/data/frb/Z1.html

Layout

Ex 1: Examples

Structures, Real Estate

Z1_csv_var %>%
  mutate(line = parse_number(pos)) %>%
  filter(table == "B101E",
         line %in% c(14, 2)) %>%
  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, linetype = variable_desc)) +
  theme(legend.title = element_blank(),
        legend.position = c(0.5, 0.9)) +
  scale_x_date(breaks = seq(1930, 2020, 5) %>% paste0("-01-01") %>% as.Date,
               limits = c(1950, 2020) %>% 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(0, 600, 20),
                     labels = scales::percent_format(accuracy = 1),
                     limits = c(0, 2.6))

Net Worth

Z1_csv_var %>%
  mutate(line = parse_number(pos)) %>%
  filter(table == "B101E",
         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, linetype = variable_desc)) +
  theme(legend.title = element_blank(),
        legend.position = c(0.4, 0.9)) +
  scale_x_date(breaks = seq(1930, 2020, 5) %>% paste0("-01-01") %>% as.Date,
               limits = c(1950, 2020) %>% 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))

Corporate Equities, Pensions, Real Estates

Z1_csv_var %>%
  mutate(line = parse_number(pos)) %>%
  filter(table == "B101E",
         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, linetype = variable_desc)) +
  theme(legend.title = element_blank(),
        legend.position = c(0.4, 0.9)) +
  scale_x_date(breaks = seq(1930, 2020, 5) %>% paste0("-01-01") %>% as.Date,
               limits = c(1950, 2020) %>% 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))

Residential Structures

Z1_csv_var %>%
  mutate(line = parse_number(pos)) %>%
  filter(table == "B101E",
         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, linetype = variable_desc)) +
  theme(legend.title = element_blank(),
        legend.position = c(0.4, 0.9)) +
  scale_x_date(breaks = seq(1930, 2020, 5) %>% paste0("-01-01") %>% as.Date,
               limits = c(1950, 2020) %>% 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))

2019

Billions

Z1_csv_var %>%
  filter(table == "B101E") %>%
  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 .}