Table 1.12. National Income by Type of Income (A) (Q) - T11200

Data - BEA

Author

François Geerolf

Info

Last observation: Q: 2023Q3 (N = 49) · A: 2022 (N = 49)

First observation: A: 1929 (N = 47) · Q: 1947Q1 (N = 46)

Last data update: 25 jul 2026, 12:36. Last compile: 17 aoû 2026, 22:36

Layout

  • NIPA Website. html

Replicating Abel et al. (1989)

Rents

1938, 1958, 1978, 1998, 2018 Table

Percent

Code 
T11200 |> filter(FREQ == "A") |> select(-FREQ) |>
  year_to_date() |>
  mutate(year = year(date)) |>
  filter(year %in% c(1938, 1958, 1978, 1998, 2018)) |>
  group_by(year) |>
  mutate(value = round(100*DataValue/DataValue[1], 1)) |>
  ungroup() |>
  select(2, 3, 6, 7) |>
  spread(year, value) %>%
  {if (is_html_output()) datatable(., filter = 'top', rownames = F) else .}

Percent - pdf

Code 
i_g("bib/bea/T11200.png")

Billions

Code 
T11200 |> filter(FREQ == "A") |> select(-FREQ) |>
  year_to_date() |>
  mutate(year = year(date)) |>
  filter(year %in% c(1938, 1958, 1978, 1998, 2018)) |>
  group_by(year) |>
  mutate(value = round(DataValue/1000)) |>
  ungroup() |>
  select(2, 3, 6, 7) |>
  spread(year, value) %>%
  {if (is_html_output()) datatable(., filter = 'top', rownames = F) else .}

Econ 102

Net dividends and undistributed profits

Code 
T11200 |> filter(FREQ == "A") |> select(-FREQ) |>
  year_to_date() |>
  filter(LineNumber %in% c(1, 16, 15, 17)) |>
  group_by(date) |>
  summarise(`Net Dividends` = DataValue[LineNumber == 16]/DataValue[LineNumber == 1],
            `Profits after tax with IVA and CCAdj` = DataValue[LineNumber == 15]/DataValue[LineNumber == 1],
            `Undistributed profits with IVA and CCAdj` = DataValue[LineNumber == 17]/DataValue[LineNumber == 1]) |>
  gather(variable, value, -date) |>
  ggplot() + theme_minimal() + xlab("") + ylab("Profits (% of GDI)") +
  geom_line(aes(x = date, y = value, color = variable)) +
  theme(legend.title = element_blank(),
        legend.position = c(0.3, 0.85)) +
  geom_rect(data = nber_recessions |>
              filter(Peak > as.Date("1928-01-01")), 
            aes(xmin = Peak, xmax = Trough, ymin = -Inf, ymax = +Inf), 
            fill = 'grey', alpha = 0.5) + 
  scale_x_date(breaks = nber_recessions$Peak,
               labels = date_format("%Y")) +
  scale_y_continuous(breaks = seq(0, 0.80, 0.01),
                     labels = scales::percent_format(accuracy = 1),
                     limits = c(0, 0.14))

Compensation of Employees

(ref:comp-emp) Compensation of Employees.

Code 
T11200 |> filter(FREQ == "A") |> select(-FREQ) |>
  year_to_date() |>
  filter(LineNumber %in% c(1, 2)) |>
  group_by(date) |>
  summarise(value_gdp = DataValue[LineNumber == 2]/DataValue[LineNumber == 1]) |>
  select(date, value_gdp) |>
  left_join(gdp_real_A, by = "date") |>
  mutate(year = year(date),
         gdp_real_A_hp10000 = log(gdp_real_A) |> hpfilter(freq = 10000) |> pluck("trend") |> exp(),
         gdp_real_A_LLtrend = lm(log(gdp_real_A) ~ year) |> fitted() |> exp()) |>
  transmute(date,
            `Compensation of Employees (% of GDI)` = value_gdp,
            `Compensation of Employees (% of Log-Linear GDI Trend)` = value_gdp * gdp_real_A / gdp_real_A_LLtrend,
            `Compensation of Employees (% of HP GDI Trend)` = value_gdp * gdp_real_A / gdp_real_A_hp10000) |>
  gather(variable, value, -date) |>
  ggplot() + theme_minimal() + xlab("") + ylab("Compens. of Employees (% of GDI)") +
  geom_line(aes(x = date, y = value, color = variable)) +
  theme(legend.title = element_blank(),
        legend.position = c(0.5, 0.2)) +
  geom_rect(data = nber_recessions |>
              filter(Peak > as.Date("1928-01-01")), 
            aes(xmin = Peak, xmax = Trough, ymin = -Inf, ymax = +Inf), 
            fill = 'grey', alpha = 0.5) + 
  scale_x_date(breaks = nber_recessions$Peak,
               labels = date_format("%Y")) +
  scale_y_continuous(breaks = seq(0.4, 0.80, 0.05),
                     labels = scales::percent_format(accuracy = 1))

(ref:comp-emp)