GDP (constant 2015 USD)

Data - WDI

Author

François Geerolf

Info

source dataset Title .html .rData
wdi NY.GDP.MKTP.KD GDP (constant 2015 USD) 2026-01-29 2026-01-29

Data on macro

source dataset Title .html .rData
eurostat nama_10_a10 Gross value added and income by A*10 industry breakdowns 2026-01-29 2026-01-29
eurostat nama_10_a10_e Employment by A*10 industry breakdowns 2026-01-29 2026-01-29
eurostat nama_10_gdp GDP and main components (output, expenditure and income) 2026-01-29 2026-01-29
eurostat nama_10_lp_ulc Labour productivity and unit labour costs 2026-01-29 2026-01-29
eurostat namq_10_a10 Gross value added and income A*10 industry breakdowns 2026-01-29 2026-01-29
eurostat namq_10_a10_e Employment A*10 industry breakdowns 2025-05-24 2026-01-29
eurostat namq_10_gdp GDP and main components (output, expenditure and income) 2025-10-27 2026-01-29
eurostat namq_10_lp_ulc Labour productivity and unit labour costs 2026-01-29 2026-01-29
eurostat namq_10_pc Main GDP aggregates per capita 2026-01-29 2026-01-29
eurostat nasa_10_nf_tr Non-financial transactions 2026-01-29 2026-01-29
eurostat nasq_10_nf_tr Non-financial transactions 2026-01-29 2026-01-29
fred gdp Gross Domestic Product 2026-01-29 2026-01-29
oecd QNA Quarterly National Accounts 2024-06-06 2025-05-24
oecd SNA_TABLE1 Gross domestic product (GDP) 2026-01-16 2025-05-24
oecd SNA_TABLE14A Non-financial accounts by sectors 2026-01-16 2024-06-30
oecd SNA_TABLE2 Disposable income and net lending - net borrowing 2024-07-01 2024-04-11
oecd SNA_TABLE6A Value added and its components by activity, ISIC rev4 2024-07-01 2024-06-30
wdi NE.RSB.GNFS.ZS External balance on goods and services (% of GDP) 2026-01-29 2026-01-29
wdi NY.GDP.MKTP.CD GDP (current USD) 2026-01-29 2026-01-29
wdi NY.GDP.MKTP.PP.CD GDP, PPP (current international D) 2026-01-29 2026-01-29
wdi NY.GDP.PCAP.CD GDP per capita (current USD) 2026-01-29 2026-01-29
wdi NY.GDP.PCAP.KD GDP per capita (constant 2015 USD) 2026-01-29 2026-01-29
wdi NY.GDP.PCAP.PP.CD GDP per capita, PPP (current international D) 2026-01-29 2026-01-29
wdi NY.GDP.PCAP.PP.KD GDP per capita, PPP (constant 2011 international D) 2026-01-29 2026-01-29

LAST_COMPILE

LAST_COMPILE
2026-01-30

Last

Code 
NY.GDP.MKTP.KD %>%
  group_by(year) %>%
  summarise(Nobs = n()) %>%
  arrange(desc(year)) %>%
  head(1) %>%
  print_table_conditional()
year Nobs
2024 240

Nobs - Javascript

Code 
NY.GDP.MKTP.KD %>%
  left_join(iso2c, by = "iso2c") %>%
  group_by(iso2c, Iso2c) %>%
  mutate(value = round(value/(10^9))) %>%
  summarise(Nobs = n(),
            `Year 1` = first(year),
            `GDP 1 (Bn)` = first(value) %>% paste0("$ ", .),
            `Year 2` = last(year),
            `GDP 2 (Bn)` = last(value) %>% paste0("$ ", .)) %>%
  arrange(-Nobs) %>%
  mutate(Flag = gsub(" ", "-", str_to_lower(Iso2c)),
         Flag = paste0('<img src="../../bib/flags/vsmall/', Flag, '.png" alt="Flag">')) %>%
  select(Flag, everything()) %>%
  {if (is_html_output()) datatable(., filter = 'top', rownames = F, escape = F) else .}

Share of World

Png

Code 
ig_b("wdi", "NY.GDP.MKTP.KD_ex2")

Javascript

Code 
NY.GDP.MKTP.KD %>%
  right_join(iso2c %>%
              filter((region != "Aggregates") & (region != "NA")), 
             by = "iso2c") %>%
  group_by(iso2c, Iso2c) %>%
  mutate(value = round(value/(10^9))) %>%
  summarise(`GDP (Bn)` = last(value)) %>%
  arrange(-`GDP (Bn)`) %>%
  mutate() %>%
  ungroup %>%
  slice(1:100) %>%
  mutate(Rank = 1:n(),
         `Cumul.` = 100*cumsum(`GDP (Bn)`)/82709,
         `GDP (Bn)` = paste0("$ ", format(`GDP (Bn)`, big.mark = ","), " Bn"),
         `Cumul.` =  paste0(round(`Cumul.`, 1), " %")) %>%
  mutate(Flag = gsub(" ", "-", str_to_lower(Iso2c)),
         Flag = paste0('<img src="../../bib/flags/vsmall/', Flag, '.png" alt="Flag">')) %>%
  select(Flag, Rank, everything()) %>%
  {if (is_html_output()) datatable(., filter = 'top', rownames = F, escape = F) else .}

Output - Countries

Png

Code 
ig_b("wdi", "NY.GDP.MKTP.KD_ex1-75")

Javascript

Code 
NY.GDP.MKTP.KD %>%
  right_join(iso2c %>%
              filter((region != "Aggregates") & (region != "NA")), 
             by = "iso2c") %>%
  group_by(iso2c, Iso2c) %>%
  mutate(value = round(value/(10^9))) %>%
  summarise(`Year` = last(year),
            `GDP (Bn)` = last(value)) %>%
  arrange(-`GDP (Bn)`) %>%
  mutate(`GDP (Bn)` = paste0("$ ", format(`GDP (Bn)`, big.mark = ","), " Bn")) %>%
  mutate(Flag = gsub(" ", "-", str_to_lower(Iso2c)),
         Flag = paste0('<img src="../../bib/flags/vsmall/', Flag, '.png" alt="Flag">')) %>%
  select(Flag, everything()) %>%
  {if (is_html_output()) datatable(., filter = 'top', rownames = F, escape = F) else .}

Euro Area vs. US

Base 100

Code 
NY.GDP.MKTP.KD %>%
  left_join(iso2c, by = "iso2c") %>%
  year_to_date %>%
  filter(iso2c %in% c("XC", "US"),
         date >= as.Date("2008-01-01")) %>%
  group_by(iso2c) %>%
  arrange(date) %>%
  mutate(value = 100*value/value[1]) %>%
  mutate(Iso2c = ifelse(iso2c == "XC", "Europe", Iso2c)) %>%
  left_join(colors, by = c("Iso2c" = "country")) %>%
  mutate(color = ifelse(iso2c == "US", color2, color)) %>%
  ggplot(.) + theme_minimal() + scale_color_identity() +
  geom_line(aes(x = date, y = value, color = color)) +
  add_2flags +
  scale_x_date(breaks = seq(1950, 2100, 2) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  scale_y_log10(breaks = seq(70, 200, 5)) + 
  xlab("") + ylab("PIB en $ (100 = 2008)")

Avec dollars

Code 
NY.GDP.MKTP.KD %>%
  left_join(iso2c, by = "iso2c") %>%
  year_to_date %>%
  filter(iso2c %in% c("XC", "US"),
         date >= as.Date("2008-01-01")) %>%
  group_by(iso2c) %>%
  arrange(date) %>%
  mutate(Iso2c = ifelse(iso2c == "XC", "Europe", Iso2c)) %>%
  left_join(colors, by = c("Iso2c" = "country")) %>%
  mutate(color = ifelse(iso2c == "US", color2, color)) %>%
  ungroup %>%
  mutate(dollar = value/10^9,
         value = 100*value/value[2]) %>%
  ggplot(.) + theme_minimal() + scale_color_identity() +
  geom_line(aes(x = date, y = value, color = color)) +
  add_2flags +
  scale_x_date(breaks = seq(1950, 2100, 2) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  scale_y_log10(breaks = seq(10, 200, 5)) + 
  xlab("") + ylab("PIB en $ (100 = Zone Euro, 2008)") + 
  geom_text_repel(data = . %>% filter(year(date) %in% seq(2008, 2022, 2)),
                                      aes(x = date, y = value, label = paste0("$", round(dollar, digits = -2), " Md")))