Table B.103 Balance Sheet of Nonfinancial Corporate Business - B103
Data - FRB
[~](https://fgeerolf.com/)/data/frb/Z1.html
Info
- Flow of funds. pdf
Layout
Liabilities
Code
Z1_table_variable %>%
mutate(line = parse_number(pos)) %>%
filter(table == "B103",
line %in% c(27, 30)) %>%
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(0.3, 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, 2),
labels = scales::percent_format(accuracy = 1))
Types of Assets
Code
Z1_table_variable %>%
mutate(line = parse_number(pos)) %>%
filter(table == "B103",
line %in% c(3, 4, 6)) %>%
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, 600, 10),
labels = scales::percent_format(accuracy = 1))
Types of Assets
Code
Z1_table_variable %>%
mutate(line = parse_number(pos)) %>%
filter(table == "B103",
line %in% c(3, 4, 6)) %>%
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)) +
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))
Total Assets
Code
Z1_table_variable %>%
mutate(line = parse_number(pos)) %>%
filter(table == "B103",
line %in% c(1, 2, 7)) %>%
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(0.3, 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))
Net Worth VS Market value of equities
Code
Z1_table_variable %>%
mutate(line = parse_number(pos)) %>%
filter(table == "B103",
line %in% c(42, 52)) %>%
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(0.3, 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))
Tobin Q Ratios
Market Assets / Book Assets
Code
Z1 %>%
filter(SERIES_NAME %in% c("FL102000005.Q", "FL102000115.Q"),
TIME_PERIOD >= as.Date("1955-01-01")) %>%
select(TIME_PERIOD, OBS_VALUE, SERIES_NAME) %>%
spread(SERIES_NAME, OBS_VALUE) %>%
ggplot + geom_line(aes(x = TIME_PERIOD, y = `FL102000005.Q`/ `FL102000115.Q`)) +
theme_minimal() + ylab("Tobin's Q") + xlab("") +
geom_rect(data = nber_recessions %>%
filter(Peak > as.Date("1955-01-01")),
aes(xmin = Peak, xmax = Trough, ymin = -Inf, ymax = +Inf),
fill = 'grey', alpha = 0.5) +
scale_x_date(breaks = "10 years",
minor_breaks = "5 years",
labels = date_format("%Y")) +
scale_y_continuous(breaks = seq(0, 3, .05))
Market Real Estate Assets / Book Real Estate Assets
Code
Z1 %>%
filter(SERIES_NAME %in% c("LM105035005.Q", "FL105035045.Q"),
TIME_PERIOD >= as.Date("1955-01-01")) %>%
select(TIME_PERIOD, OBS_VALUE, SERIES_NAME) %>%
spread(SERIES_NAME, OBS_VALUE) %>%
ggplot + geom_line(aes(x = TIME_PERIOD, y = `LM105035005.Q`/ `FL105035045.Q`)) +
theme_minimal() + ylab("Real Estate Tobin's Q") + xlab("") +
geom_rect(data = nber_recessions %>%
filter(Peak > as.Date("1955-01-01")),
aes(xmin = Peak, xmax = Trough, ymin = -Inf, ymax = +Inf),
fill = 'grey', alpha = 0.5) +
scale_x_date(breaks = "10 years",
minor_breaks = "5 years",
labels = date_format("%Y")) +
scale_y_continuous(breaks = seq(0, 3, 0.2))
Tobin’s Q for Nonfinancial Corporations
csv
- B.103 Balance Sheet of Nonfinancial Corporate Business
Code
Z1_csv %>%
filter(variable == "FL103164106.Q") %>%
ggplot + geom_line(aes(x = date, y = value/100)) + theme_minimal() +
ylab("Tobin's Q") + xlab("") +
geom_rect(data = nber_recessions %>%
filter(Peak > as.Date("1945-01-01")),
aes(xmin = Peak, xmax = Trough, ymin = -Inf, ymax = +Inf),
fill = 'grey', alpha = 0.5) +
scale_x_date(breaks = "10 years",
minor_breaks = "5 years",
labels = date_format("%Y")) +
scale_y_continuous(breaks = 0.01*seq(0, 300, 20))
SDMX
- B.103 Balance Sheet of Nonfinancial Corporate Business
Code
Z1 %>%
filter(SERIES_NAME == "FL103164106.Q",
TIME_PERIOD >= as.Date("1955-01-01")) %>%
ggplot + geom_line(aes(x = TIME_PERIOD, y = OBS_VALUE/100)) + theme_minimal() +
ylab("Tobin's Q") + xlab("") +
geom_rect(data = nber_recessions %>%
filter(Peak > as.Date("1955-01-01")),
aes(xmin = Peak, xmax = Trough, ymin = -Inf, ymax = +Inf),
fill = 'grey', alpha = 0.5) +
scale_x_date(breaks = "10 years",
minor_breaks = "5 years",
labels = date_format("%Y")) +
scale_y_continuous(breaks = seq(0, 3,.2))
Code
Z1 %>%
filter(SERIES_NAME == "FL103164106.Q",
OBS_STATUS == "A") %>%
ggplot + geom_line(aes(x = TIME_PERIOD, y = OBS_VALUE/100)) +
theme_minimal() +
ylab("Tobin's Q") + xlab("") +
geom_rect(data = nber_recessions %>%
filter(Peak > as.Date("1945-01-01")),
aes(xmin = Peak, xmax = Trough, ymin = -Inf, ymax = +Inf),
fill = 'grey', alpha = 0.5) +
scale_x_date(breaks = seq(1945, 2100, 10) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
scale_y_continuous(breaks = 0.01*seq(0, 300, 20)) +
scale_color_manual(values = viridis(5)[1:4]) +
theme(legend.position = c(0.2, 0.3),
legend.title = element_blank(),
legend.text = element_text(size = 8),
legend.key.size = unit(0.9, 'lines'))
2019
Billions
Code
Z1_table_variable %>%
filter(table == "B103") %>%
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 .}