Penn World Tables - PWT

Data - PWT

Last observation: 2017 (N = 7785)

First observation: 1950 (N = 2093)

Last data update: 01 août 2026, 14:36

Last compile: 04 sept. 2026, 01:27

Main Variables

Other Variables

country_code

pwt91 |>
  left_join(iso3c, by = "iso3c") |>
  group_by(iso3c, iso3c_desc) |>
  summarise(Nobs = n()) |>
  arrange(-Nobs) %>%
  {if (is_html_output()) datatable(., filter = 'top', rownames = F) else .}

Map - GDP Per Capita

(ref:map-gdp-per-capita) 2014 GDP per capita, by Country (Penn World Tables).

pwt91 |>
  filter(year(date) == 2014,
         variable %in% c("cgdpo", "pop")) |>
  left_join(iso3c, by = "iso3c") |>
  spread(variable, value) |>
  mutate(cgdpo_pop = cgdpo/pop) |>
  left_join(iso3c_to_iso2c, by = "iso3c") |>
  right_join(world, by = "iso2c") |>
  ggplot(aes(long, lat, group = group)) +
  geom_polygon(aes(fill = cgdpo_pop), 
               colour = alpha("white", 1/2), 
               size = 0.1)  +
  scale_fill_viridis_c(labels = scales::dollar,
                       breaks = c(20000, 40000, 60000, 100000, 140000),
                       values = c(0, 0.05, 0.1, 0.2, 0.3, 0.4, 0.5, 1)) +
  theme_void() +
  theme(legend.position = c(0.1, 0.4),
        legend.title = element_blank())

(ref:map-gdp-per-capita)

Ex: Capital Stock

pwt91 |>
  filter(variable == "rkna") |>
  left_join(iso3c, by = "iso3c") |>
  group_by(iso3c, iso3c_desc) %>%
  summarise(`Year 1` = first(year(date)),
            `Year 2` = last(year(date)),
            `Value 1` = first(value) |> round(digits = 1),
            `Value 2` = last(value) |> round(digits = 1)) %>%
  {if (is_html_output()) datatable(., filter = 'top', rownames = F) else .}

Ex: Gross and net returns

pwt91 |>
  filter(year(date) == "2014",
         variable %in% c("cgdpo", "ck", "delta", "pop", "emp", "labsh")) |>
  select(iso3c, variable, value) |>
  spread(variable, value) |>
  left_join(iso3c, by = "iso3c") |>
  mutate(ck_gdp = ck / cgdpo,
         cgdpo_pop = cgdpo/pop,
         cgdpo_emp = cgdpo/emp,
         ck_emp = ck/emp,
         ret_gross = 100*(1-labsh)/ck_gdp,
         delta = 100*delta,
         ret_net = ret_gross - delta) |>
  select(iso3c, iso3c_desc, pop, cgdpo_pop, ck_gdp, ret_gross, delta, ret_net) |>
  # Advanced Economies
  filter(cgdpo_pop >= 30000,
         pop >= 1) %>%
  mutate_at(vars(-iso3c, -iso3c_desc), funs(round(., digits = 2))) %>%
  {if (is_html_output()) datatable(., filter = 'top', rownames = F) else .}