Table 1.1. Current-Cost Net Stock of Fixed Assets and Consumer Durable Goods (A) - FAAt101

Data - BEA

~/data/bea/FixedAssets.html

Info

LAST_DOWNLOAD

file	LAST_DOWNLOAD
FAAt101	2024-05-20

LAST_COMPILE

LAST_COMPILE
2024-05-20

Last

date	Nobs
2022-12-31	24

Layout

• Fixed Assets Website. html

Capital

What is capital?

% of Trend GDP

FAAt101 %>%
  year_to_date %>%
  filter(LineNumber %in% c(2, 3)) %>%
  rename(variable = LineDescription) %>%
  left_join(gdp, by = "date") %>%
  mutate(DataValue = DataValue / gdp,
         variable = case_when(variable == "Fixed assets" ~ "Fixed assets (Private + Government)",
                              variable == "Private" ~ "Fixed assets (Private)")) %>%
  ggplot + geom_line(aes(x = date, y = DataValue, color = variable)) + 
  ylab("% of GDP") + xlab("") + 
  theme_minimal()+
  geom_rect(data = nber_recessions %>%
              filter(Peak > as.Date("1927-01-01")),
            aes(xmin = Peak, xmax = Trough, ymin = -Inf, ymax = +Inf), 
            fill = 'grey', alpha = 0.5) +
  scale_x_date(breaks = nber_recessions$Peak,
               minor_breaks = "5 years",
               labels = date_format("%y")) + 
  scale_y_continuous(breaks = 0.01*seq(0, 500, 20),
                     labels = scales::percent_format(accuracy = 1)) +
  theme(legend.position = c(0.2, 0.9),
        legend.title = element_blank(),
        legend.text = element_text(size = 8),
        legend.key.size = unit(0.9, 'lines'))

% of GDP

FAAt101 %>%
  year_to_date %>%
  filter(LineNumber %in% c(2, 3)) %>%
  rename(variable = LineDescription) %>%
  left_join(gdp_A %>%
              select(date, GDP = value), by = "date") %>%
  mutate(DataValue = DataValue / GDP,
         variable = case_when(variable == "Fixed assets" ~ "Fixed assets (Private + Government)",
                              variable == "Private" ~ "Fixed assets (Private)")) %>%
  ggplot + geom_line(aes(x = date, y = DataValue, color = variable)) + 
  ylab("% of GDP") + xlab("") + theme_minimal()+
  geom_rect(data = nber_recessions %>%
              filter(Peak > as.Date("1927-01-01")),
            aes(xmin = Peak, xmax = Trough, ymin = -Inf, ymax = +Inf), 
            fill = 'grey', alpha = 0.5) +
  scale_x_date(breaks = nber_recessions$Peak,
               minor_breaks = "5 years",
               labels = date_format("%y")) + 
  scale_y_continuous(breaks = 0.01*seq(0, 500, 20),
                     labels = scales::percent_format(accuracy = 1)) +
  theme(legend.position = c(0.7, 0.9),
        legend.title = element_blank(),
        legend.text = element_text(size = 8),
        legend.key.size = unit(0.9, 'lines'))

U.S. Main Fixed Asset Components (1929-2017)

% of Trend GDP

FAAt101 %>%
  year_to_date %>%
  filter(LineNumber %in% c(5, 6, 8, 15)) %>%
  rename(variable = LineDescription) %>%
  left_join(gdp_adjustment, by = "date") %>%
  mutate(DataValue =  `Real GDP / Real GDP Trend (Log Linear)` * DataValue / GDP) %>%
  ggplot + geom_line(aes(x = date, y = DataValue, color = variable)) + 
  ylab("% of GDP") + xlab("") + 
  theme_minimal()+
  geom_rect(data = nber_recessions %>%
              filter(Peak > as.Date("1927-01-01")),
            aes(xmin = Peak, xmax = Trough, ymin = -Inf, ymax = +Inf), 
            fill = 'grey', alpha = 0.5) +
  scale_x_date(breaks = nber_recessions$Peak,
               minor_breaks = "5 years",
               labels = date_format("%y")) + 
  scale_y_continuous(breaks = 0.01*seq(0, 160, 10),
                     labels = scales::percent_format(accuracy = 1)) +
  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'))

% of GDP

FAAt101 %>%
  year_to_date %>%
  filter(LineNumber %in% c(5, 6, 8, 15)) %>%
  rename(variable = LineDescription) %>%
  left_join(gdp_A %>%
              select(date, GDP = value), by = "date") %>%
  mutate(DataValue = DataValue / GDP) %>%
  ggplot + geom_line(aes(x = date, y = DataValue, color = variable)) + 
  ylab("% of GDP") + xlab("") + 
  theme_minimal()+
  geom_rect(data = nber_recessions %>%
              filter(Peak > as.Date("1927-01-01")),
            aes(xmin = Peak, xmax = Trough, ymin = -Inf, ymax = +Inf), 
            fill = 'grey', alpha = 0.5) +
  scale_x_date(breaks = nber_recessions$Peak,
               minor_breaks = "5 years",
               labels = date_format("%y")) + 
  scale_y_continuous(breaks = 0.01*seq(0, 160, 10),
                     labels = scales::percent_format(accuracy = 1)) +
  theme(legend.position = c(0.8, 0.9),
        legend.title = element_blank(),
        legend.text = element_text(size = 8),
        legend.key.size = unit(0.9, 'lines'))

Total Fixed Assets

% of Trend GDP

FAAt101 %>%
  year_to_date %>%
  filter(LineNumber %in% c(1, 2)) %>%
  rename(variable = LineDescription) %>%
  left_join(gdp_adjustment, by = "date") %>%
  mutate(DataValue = `Real GDP / Real GDP Trend (Log Linear)` * DataValue / GDP) %>%
  ggplot + geom_line(aes(x = date, y = DataValue, color = variable)) + 
  ylab("% of GDP") + xlab("") + 
  theme_minimal()+
  geom_rect(data = nber_recessions %>%
              filter(Peak > as.Date("1927-01-01")),
            aes(xmin = Peak, xmax = Trough, ymin = -Inf, ymax = +Inf), 
            fill = 'grey', alpha = 0.5) +
  scale_x_date(breaks = nber_recessions$Peak,
               minor_breaks = "5 years",
               labels = date_format("%y")) + 
  scale_y_continuous(breaks = 0.01*seq(0, 400, 10),
                     labels = scales::percent_format(accuracy = 1)) +
  scale_color_manual(values = viridis(3)[1:2]) +
  theme(legend.position = c(0.25, 0.9),
        legend.title = element_blank(),
        legend.text = element_text(size = 8),
        legend.key.size = unit(0.9, 'lines'))

% of GDP

FAAt101 %>%
  year_to_date %>%
  filter(LineNumber %in% c(1, 2)) %>%
  rename(variable = LineDescription) %>%
  left_join(gdp_A %>%
              select(date, GDP = value), by = "date") %>%
  mutate(DataValue = DataValue / GDP) %>%
  ggplot + geom_line(aes(x = date, y = DataValue, color = variable)) + 
  ylab("% of GDP") + xlab("") + 
  theme_minimal()+
  geom_rect(data = nber_recessions %>%
              filter(Peak > as.Date("1927-01-01")),
            aes(xmin = Peak, xmax = Trough, ymin = -Inf, ymax = +Inf), 
            fill = 'grey', alpha = 0.5) +
  scale_x_date(breaks = nber_recessions$Peak,
               minor_breaks = "5 years",
               labels = date_format("%y")) + 
  scale_y_continuous(breaks = 0.01*seq(0, 600, 50),
                     labels = scales::percent_format(accuracy = 1)) +
  theme(legend.position = c(0.7, 0.9),
        legend.title = element_blank(),
        legend.text = element_text(size = 8),
        legend.key.size = unit(0.9, 'lines'))

1938, 1958, 1978, 1998, 2018 Table

Percent

FAAt101 %>%
  year_to_date %>%
  mutate(year = year(date)) %>%
  filter(year %in% c(1938, 1958, 1978, 1998, 2018)) %>%
  group_by(year) %>%
  mutate(DataValue = round(100*DataValue/DataValue[1], 1)) %>%
  ungroup %>%
  select(LineNumber, LineDescription, year, DataValue) %>%
  spread(year, DataValue) %>%
  {if (is_html_output()) datatable(., filter = 'top', rownames = F) else .}

Billions

FAAt101 %>%
  year_to_date %>%
  mutate(year = year(date)) %>%
  filter(year %in% c(1938, 1958, 1978, 1998, 2018)) %>%
  group_by(year) %>%
  mutate(DataValue = round(DataValue)) %>%
  ungroup %>%
  select(LineNumber, LineDescription, year, DataValue) %>%
  spread(year, DataValue) %>%
  {if (is_html_output()) datatable(., filter = 'top', rownames = F) else .}