Quarterly GDP and components - expenditure approach

Data - OECD

Author

François Geerolf

Info

source dataset Title .html .rData
oecd QNA_EXPENDITURE_CAPITA Quarterly National Accounts, GDP Per Capita 2026-03-23 2026-04-03
oecd QNA_EXPENDITURE_USD Quarterly GDP and components - expenditure approach 2026-03-23 2026-04-03

Last

obsTime Nobs
2025-Q4 655

REF_AREA

Code 
QNA_EXPENDITURE_USD %>%
  left_join(REF_AREA, by = "REF_AREA") %>%
  group_by(REF_AREA, Ref_area) %>%
  summarise(Nobs = n()) %>%
  arrange(-Nobs) %>%
  print_table_conditional()

UNIT_MEASURE

Code 
QNA_EXPENDITURE_USD %>%
  left_join(UNIT_MEASURE, by = "UNIT_MEASURE") %>%
  group_by(UNIT_MEASURE, Unit_measure) %>%
  summarise(Nobs = n()) %>%
  arrange(-Nobs) %>%
  print_table_conditional()
UNIT_MEASURE Unit_measure Nobs
USD_PPP US dollars, PPP converted 172158

SECTOR

Code 
QNA_EXPENDITURE_USD %>%
  left_join(SECTOR, by = "SECTOR") %>%
  group_by(SECTOR, Sector) %>%
  summarise(Nobs = n()) %>%
  arrange(-Nobs) %>%
  print_table_conditional()
SECTOR Sector Nobs
S1 Total economy 114907
S1M Households and non-profit institutions serving households (NPISH) 28648
S13 General government 28603

ADJUSTMENT

Code 
QNA_EXPENDITURE_USD %>%
  left_join(ADJUSTMENT, by = "ADJUSTMENT") %>%
  group_by(ADJUSTMENT, Adjustment) %>%
  summarise(Nobs = n()) %>%
  arrange(-Nobs) %>%
  print_table_conditional()
ADJUSTMENT Adjustment Nobs
Y Calendar and seasonally adjusted 172158

PRICE_BASE

Code 
QNA_EXPENDITURE_USD %>%
  left_join(PRICE_BASE, by = "PRICE_BASE") %>%
  group_by(PRICE_BASE, Price_base) %>%
  summarise(Nobs = n()) %>%
  arrange(-Nobs) %>%
  print_table_conditional()
PRICE_BASE Price_base Nobs
LR Chain linked volume (rebased) 86192
V Current prices 85966

FREQ

Code 
QNA_EXPENDITURE_USD %>%
  left_join(FREQ, by = "FREQ") %>%
  group_by(FREQ, Freq) %>%
  summarise(Nobs = n()) %>%
  arrange(-Nobs) %>%
  print_table_conditional()
FREQ Freq Nobs
Q Quarterly 137685
A Annual 34473

TRANSACTION

Code 
QNA_EXPENDITURE_USD %>%
  left_join(TRANSACTION, by = "TRANSACTION") %>%
  group_by(TRANSACTION, Transaction) %>%
  summarise(Nobs = n()) %>%
  arrange(-Nobs) %>%
  print_table_conditional()
TRANSACTION Transaction Nobs
P3 Final consumption expenditure 57251
B1GQ Gross domestic product 29018
P6 Exports of goods and services 28643
P7 Imports of goods and services 28643
P51G Gross fixed capital formation 28603

U.S., Europe, France, Germany

All

Code 
QNA_EXPENDITURE_USD %>%
  filter(REF_AREA %in% c("USA", "EA20", "FRA", "DEU"),
         FREQ == "Q",
         TRANSACTION == "B1GQ",
         PRICE_BASE == "LR",
         ADJUSTMENT == "Y",
         SECTOR == "S1") %>%
  quarter_to_date %>%
  arrange(desc(date)) %>%
  rename(LOCATION = REF_AREA) %>%
  left_join(QNA_var$LOCATION, by = "LOCATION") %>%
  mutate(Location = ifelse(LOCATION == "EA20", "Europe", Location)) %>%
  group_by(Location) %>%
  mutate(obsValue = 100 * obsValue / obsValue[date == as.Date("2007-04-01")]) %>%
  left_join(colors, by = c("Location" = "country")) %>%
  mutate(color = ifelse(LOCATION != "DEU", color2, color)) %>%
  ggplot(.) + theme_minimal() + xlab("") + ylab("") +
  geom_line(aes(x = date, y = obsValue, color = color)) + add_4flags +
  scale_color_identity() +
  scale_x_date(breaks = c(seq(1900, 2100, 5)) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  theme(legend.position = "none") +
  scale_y_log10(breaks = seq(5, 200, 5))

1995-

Code 
QNA_EXPENDITURE_USD %>%
  filter(REF_AREA %in% c("USA", "EA20", "FRA", "DEU"),
         FREQ == "Q",
         TRANSACTION == "B1GQ",
         PRICE_BASE == "LR",
         ADJUSTMENT == "Y",
         SECTOR == "S1") %>%
  quarter_to_date %>%
  arrange(desc(date)) %>%
  rename(LOCATION = REF_AREA) %>%
  left_join(QNA_var$LOCATION, by = "LOCATION") %>%
  filter(date >= as.Date("1995-01-01")) %>%
  mutate(Location = ifelse(LOCATION == "EA20", "Europe", Location)) %>%
  group_by(Location) %>%
  mutate(obsValue = 100 * obsValue / obsValue[date == as.Date("2007-04-01")]) %>%
  left_join(colors, by = c("Location" = "country")) %>%
  mutate(color = ifelse(LOCATION != "DEU", color2, color)) %>%
  ggplot(.) + theme_minimal() + xlab("") + ylab("") +
  geom_line(aes(x = date, y = obsValue, color = color)) + add_4flags +
  scale_color_identity() +
  scale_x_date(breaks = c(seq(1995, 2100, 5)) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  theme(legend.position = "none") +
  scale_y_log10(breaks = seq(50, 200, 5))

1999-

Tous

Code 
QNA_EXPENDITURE_USD %>%
  filter(REF_AREA %in% c("USA", "EA20", "FRA", "DEU"),
         FREQ == "Q",
         TRANSACTION == "B1GQ",
         PRICE_BASE == "LR",
         ADJUSTMENT == "Y",
         SECTOR == "S1") %>%
  quarter_to_date %>%
  rename(LOCATION = REF_AREA) %>%
  left_join(QNA_var$LOCATION, by = "LOCATION") %>%
  filter(date >= as.Date("1999-01-01")) %>%
  mutate(Location = ifelse(LOCATION == "EA20", "Europe", Location)) %>%
  group_by(Location) %>%
  mutate(obsValue = 100 * obsValue / obsValue[date == as.Date("2007-04-01")]) %>%
  left_join(colors, by = c("Location" = "country")) %>%
  mutate(color = ifelse(LOCATION != "DEU", color2, color)) %>%
  ggplot(.) + theme_minimal() + xlab("") + ylab("") +
  geom_line(aes(x = date, y = obsValue, color = color)) + add_4flags +
  scale_color_identity() +
  scale_x_date(breaks = c(seq(1999, 2100, 5), seq(1997, 2100, 5)) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  theme(legend.position = "none") +
  scale_y_log10(breaks = seq(50, 200, 5))

Base 100 = 1999

Code 
QNA_EXPENDITURE_USD %>%
  filter(REF_AREA %in% c("USA", "EA20", "FRA", "DEU"),
         FREQ == "Q",
         TRANSACTION == "B1GQ",
         PRICE_BASE == "LR",
         ADJUSTMENT == "Y",
         SECTOR == "S1") %>%
  quarter_to_date %>%
  rename(LOCATION = REF_AREA) %>%
  left_join(QNA_var$LOCATION, by = "LOCATION") %>%
  filter(date >= as.Date("1999-01-01")) %>%
  mutate(Location = ifelse(LOCATION == "EA20", "Europe", Location)) %>%
  group_by(Location) %>%
  mutate(obsValue = 100 * obsValue / obsValue[date == as.Date("1999-01-01")]) %>%
  left_join(colors, by = c("Location" = "country")) %>%
  mutate(color = ifelse(LOCATION != "DEU", color2, color)) %>%
  ggplot(.) + theme_minimal() + xlab("") + ylab("") +
  geom_line(aes(x = date, y = obsValue, color = color)) + add_4flags +
  scale_color_identity() +
  scale_x_date(breaks = c(seq(1999, 2100, 5), seq(1997, 2100, 5)) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  theme(legend.position = "none") +
  scale_y_log10(breaks = seq(50, 200, 5))