Quarterly GDP and components - expenditure approach - QNA_EXPENDITURE_NATIO_CURR

Data - OECD

Last observation: Q4 2026 (N = 1) · 2025 (N = 3758)

First observation: 1947 (N = 37) · Q1 1947 (N = 37)

Last data update: 19 oct. 2026, 00:05

Last compile: 19 oct. 2026, 01:50

Structure

FREQ

ADJUSTMENT

REF_AREA

SECTOR

COUNTERPART_SECTOR

TRANSACTION

INSTR_ASSET

ACTIVITY

EXPENDITURE

UNIT_MEASURE

PRICE_BASE

TRANSFORMATION

TABLE_IDENTIFIER

REF_YEAR_PRICE

BASE_PER

CONF_STATUS

DECIMALS

OBS_STATUS

UNIT_MULT

CURRENCY

U.S. VS Europe

Table

QNA_EXPENDITURE_NATIO_CURR |>
  filter(REF_AREA %in% c("USA", "EA"),
         FREQ == "Q",
         # L = Chain linked volume
         PRICE_BASE == "L",
         ADJUSTMENT == "Y",
         SECTOR == "S1",
         obsTime %in% c("1999-Q1", "2023-Q4")) |>
  
     left_join(QNA_POP_EMPNC_extract, by = c("obsTime", "REF_AREA", "Ref_area")) |>
  mutate(obsValue = 4*obsValue/population) %>%
  select_if(~ n_distinct(.) > 1) |>
  select(REF_AREA, TRANSACTION, Transaction, obsTime, obsValue) |>
  spread(obsTime, obsValue) |>
  mutate(change = `2023-Q4`-`1999-Q1`) |>
  select(REF_AREA, TRANSACTION, Transaction, change) |>
  spread(REF_AREA, change) |>
  print_table_conditional()

TRANSACTION	Transaction	EA	USA
B11	External balance of goods and services	1.474618	-1.7378148
B1GQ	Gross domestic product	8.377472	20.2074013
P3	Final consumption expenditure	5.510767	16.8948925
P3_P51G	Final domestic demand excluding inventories	NA	21.9994582
P3T5	Domestic demand	NA	21.9679111
P5	Gross capital formation	1.356397	5.1868178
P51G	Gross fixed capital formation	1.772888	5.1786035
P52	Changes in inventories	NA	-0.2501752
P5M	Changes in inventories and acquisitions less disposals of valuables	NA	-0.2501752
P6	Exports of goods and services	10.006765	3.3551333
P61	Exports of goods	6.190253	2.2330462
P62	Exports of services	3.868858	1.1491870
P7	Imports of goods and services	8.532146	5.0929524
P71	Imports of goods	5.114545	4.2016938
P72	Imports of services	3.570004	0.8957242
YA0	Statistical discrepancy (expenditure approach)	NA	-0.2375656

P3 - Final consumption expenditure / capita

plot <- QNA_EXPENDITURE_NATIO_CURR |>
  filter(REF_AREA %in% c("USA", "EA"),
         FREQ == "Q",
         TRANSACTION == "P3",
         # L = Chain linked volume
         PRICE_BASE == "L",
         ADJUSTMENT == "Y",
         SECTOR == "S1") |>
  left_join(QNA_POP_EMPNC_extract, by = c("obsTime", "REF_AREA", "Ref_area")) |>
  mutate(obsValue = obsValue/population) |>
  quarter_to_date() |>
  
  filter(date >= as.Date("1999-01-01")) |>
  mutate(Ref_area = ifelse(REF_AREA == "EA", "Zone euro", Ref_area)) |>
  group_by(Ref_area) |>
  arrange(date) |>
  mutate(obsValue = 100 * obsValue / obsValue[1]) |>
  ggplot() + theme_minimal() + xlab("") + ylab("Consommation par habitant (1999T1 = 100)") +
  geom_line(aes(x = date, y = obsValue, color = Ref_area)) +
  scale_color_manual(values = c("#B22234", "#003399")) +
  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 = c(0.26, 0.8),
        legend.title = element_blank()) +
  scale_y_log10(breaks = seq(50, 200, 5)) +
  geom_rect(data = nber_recessions |>
              filter(Peak > as.Date("1999-01-01")), 
            aes(xmin = Peak, xmax = Trough, ymin = 0, ymax = +Inf),
            fill = '#B22234', alpha = 0.1)  +
  geom_rect(data = cepr_recessions |>
              filter(Peak > as.Date("1999-01-01")), 
            aes(xmin = Peak, xmax = Trough, ymin = 0, ymax = +Inf), 
            fill = '#003399', alpha = 0.1) +
  labs(caption = "Source: OCDE, Comptes Trimestriels, Volumes chaînés")

plot

save(plot, file = "QNA_EXPENDITURE_NATIO_CURR_files/figure-html/USA-EA-1999-percapita-1.RData")

U.S., Europe, France, Germany

B1GQ - GDP

1995-

QNA_EXPENDITURE_NATIO_CURR |>
  filter(REF_AREA %in% c("USA", "EA", "FRA", "DEU"),
         FREQ == "Q",
         TRANSACTION == "B1GQ",
         PRICE_BASE == "L",
         ADJUSTMENT == "Y",
         SECTOR == "S1") |>
  quarter_to_date() |>
  arrange(desc(date)) |>
  
  filter(date >= as.Date("1995-01-01")) |>
  mutate(Ref_area = ifelse(REF_AREA == "EA", "Europe", Ref_area)) |>
  group_by(Ref_area) |>
  arrange(date) |>
  mutate(obsValue = 100 * obsValue / obsValue[1]) |>
  add_flag_color("Ref_area") |>
  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)) + 
  geom_label_repel(data = . %>% filter(date == max(date)),
             aes(x = date, y = obsValue, label = round(obsValue, 1), color = color))

1999-

Total

QNA_EXPENDITURE_NATIO_CURR |>
  filter(REF_AREA %in% c("USA", "EA", "FRA", "DEU"),
         FREQ == "Q",
         TRANSACTION == "B1GQ",
         PRICE_BASE == "L",
         ADJUSTMENT == "Y",
         SECTOR == "S1") |>
  quarter_to_date() |>
  
  filter(date >= as.Date("1999-01-01")) |>
  mutate(Ref_area = ifelse(REF_AREA == "EA", "Europe", Ref_area)) |>
  group_by(Ref_area) |>
  arrange(date) |>
  mutate(obsValue = 100 * obsValue / obsValue[1]) |>
  add_flag_color("Ref_area") |>
  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)) + 
  geom_label_repel(data = . %>% filter(date == max(date)),
             aes(x = date, y = obsValue, label = round(obsValue, 1), color = color))

Per Capita

QNA_EXPENDITURE_NATIO_CURR |>
  filter(REF_AREA %in% c("USA", "EA", "FRA", "DEU"),
         FREQ == "Q",
         TRANSACTION == "B1GQ",
         # L = Chain linked volume
         PRICE_BASE == "L",
         ADJUSTMENT == "Y",
         SECTOR == "S1") |>
  left_join(QNA_POP_EMPNC_extract, by = c("obsTime", "REF_AREA", "Ref_area")) |>
  mutate(obsValue = obsValue/population) |>
  quarter_to_date() |>
  
  
  filter(date >= as.Date("1999-01-01")) |>
  mutate(Ref_area = ifelse(REF_AREA == "EA", "Europe", Ref_area)) |>
  group_by(Ref_area) |>
  arrange(date) |>
  mutate(obsValue = 100 * obsValue / obsValue[1]) |>
  add_flag_color("Ref_area") |>
  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)) + 
  geom_label_repel(data = . %>% filter(date == max(date)),
             aes(x = date, y = obsValue, label = round(obsValue, 1), color = color))

P51G - Gross fixed capital formation

1995-

QNA_EXPENDITURE_NATIO_CURR |>
  filter(REF_AREA %in% c("USA", "EA", "FRA", "DEU"),
         FREQ == "Q",
         TRANSACTION == "P51G",
         PRICE_BASE == "L",
         ADJUSTMENT == "Y",
         SECTOR == "S1") |>
  quarter_to_date() |>
  arrange(desc(date)) |>
  
  
  filter(date >= as.Date("1995-01-01")) |>
  mutate(Ref_area = ifelse(REF_AREA == "EA", "Europe", Ref_area)) |>
  group_by(Ref_area) |>
  arrange(date) |>
  mutate(obsValue = 100 * obsValue / obsValue[1]) |>
  add_flag_color("Ref_area") |>
  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, 500, 5)) + 
  geom_label_repel(data = . %>% filter(date == max(date)),
             aes(x = date, y = obsValue, label = round(obsValue, 1), color = color))

1999-

Total

QNA_EXPENDITURE_NATIO_CURR |>
  filter(REF_AREA %in% c("USA", "EA", "FRA", "DEU"),
         FREQ == "Q",
         TRANSACTION == "P51G",
         PRICE_BASE == "L",
         ADJUSTMENT == "Y",
         SECTOR == "S1") |>
  quarter_to_date() |>
  
  
  filter(date >= as.Date("1999-01-01")) |>
  mutate(Ref_area = ifelse(REF_AREA == "EA", "Europe", Ref_area)) |>
  group_by(Ref_area) |>
  arrange(date) |>
  mutate(obsValue = 100 * obsValue / obsValue[1]) |>
  add_flag_color("Ref_area") |>
  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)) + 
  geom_label_repel(data = . %>% filter(date == max(date)),
             aes(x = date, y = obsValue, label = round(obsValue, 1), color = color))

Per Capita

QNA_EXPENDITURE_NATIO_CURR |>
  filter(REF_AREA %in% c("USA", "EA", "FRA", "DEU"),
         FREQ == "Q",
         TRANSACTION == "P51G",
         # L = Chain linked volume
         PRICE_BASE == "L",
         ADJUSTMENT == "Y",
         SECTOR == "S1") |>
  left_join(QNA_POP_EMPNC_extract, by = c("obsTime", "REF_AREA", "Ref_area")) |>
  mutate(obsValue = obsValue/population) |>
  quarter_to_date() |>
  
  
  filter(date >= as.Date("1999-01-01")) |>
  mutate(Ref_area = ifelse(REF_AREA == "EA", "Europe", Ref_area)) |>
  group_by(Ref_area) |>
  arrange(date) |>
  mutate(obsValue = 100 * obsValue / obsValue[1]) |>
  add_flag_color("Ref_area") |>
  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)) + 
  geom_label_repel(data = . %>% filter(date == max(date)),
             aes(x = date, y = obsValue, label = round(obsValue, 1), color = color))

P3 - Final consumption expenditure

All

QNA_EXPENDITURE_NATIO_CURR |>
  filter(REF_AREA %in% c("USA", "EA", "FRA", "DEU"),
         FREQ == "Q",
         TRANSACTION == "P3",
         PRICE_BASE == "L",
         ADJUSTMENT == "Y",
         SECTOR == "S1") |>
  quarter_to_date() |>
  arrange(desc(date)) |>
  
  
  mutate(Ref_area = ifelse(REF_AREA == "EA", "Europe", Ref_area)) |>
  group_by(Ref_area) |>
  arrange(date) |>
  mutate(obsValue = 100 * obsValue / obsValue[1]) |>
  add_flag_color("Ref_area") |>
  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(20, 600, 20))

1995-

QNA_EXPENDITURE_NATIO_CURR |>
  filter(REF_AREA %in% c("USA", "EA", "FRA", "DEU"),
         FREQ == "Q",
         TRANSACTION == "P3",
         PRICE_BASE == "L",
         ADJUSTMENT == "Y",
         SECTOR == "S1") |>
  quarter_to_date() |>
  arrange(desc(date)) |>
  
  
  filter(date >= as.Date("1995-01-01")) |>
  mutate(Ref_area = ifelse(REF_AREA == "EA", "Europe", Ref_area)) |>
  group_by(Ref_area) |>
  arrange(date) |>
  mutate(obsValue = 100 * obsValue / obsValue[1]) |>
  add_flag_color("Ref_area") |>
  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)) + 
  geom_label_repel(data = . %>% filter(date == max(date)),
             aes(x = date, y = obsValue, label = round(obsValue, 1), color = color))

1999-

Total

QNA_EXPENDITURE_NATIO_CURR |>
  filter(REF_AREA %in% c("USA", "EA", "FRA", "DEU"),
         FREQ == "Q",
         TRANSACTION == "P3",
         # L = Chain linked volume
         PRICE_BASE == "L",
         ADJUSTMENT == "Y",
         SECTOR == "S1") |>
  quarter_to_date() |>
  
  
  filter(date >= as.Date("1999-01-01")) |>
  mutate(Ref_area = ifelse(REF_AREA == "EA", "Europe", Ref_area)) |>
  group_by(Ref_area) |>
  arrange(date) |>
  mutate(obsValue = 100 * obsValue / obsValue[1]) |>
  add_flag_color("Ref_area") |>
  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 = c(0.26, 0.8),
        legend.title = element_blank()) +
  scale_y_log10(breaks = seq(50, 200, 5)) + 
  geom_label_repel(data = . %>% filter(date == max(date)),
             aes(x = date, y = obsValue, label = round(obsValue, 1), color = color))

Per Capita

plot <- QNA_EXPENDITURE_NATIO_CURR |>
  filter(REF_AREA %in% c("USA", "EA", "FRA", "DEU"),
         FREQ == "Q",
         TRANSACTION == "P3",
         # L = Chain linked volume
         PRICE_BASE == "L",
         ADJUSTMENT == "Y",
         SECTOR == "S1") |>
  left_join(QNA_POP_EMPNC_extract, by = c("obsTime", "REF_AREA", "Ref_area")) |>
  mutate(obsValue = obsValue/population) |>
  quarter_to_date() |>
  
  filter(date >= as.Date("1999-01-01")) |>
  mutate(Ref_area = ifelse(REF_AREA == "EA", "Zone euro", Ref_area)) |>
  group_by(Ref_area) |>
  arrange(date) |>
  mutate(obsValue = 100 * obsValue / obsValue[1]) |>
  ggplot() + theme_minimal() + xlab("") + ylab("Consommation par habitant (1999T1 = 100)") +
  geom_line(aes(x = date, y = obsValue, color = Ref_area)) +
  scale_color_manual(values = c("#000000", "#B22234", "#808080", "#003399")) +
  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 = c(0.26, 0.8),
        legend.title = element_blank()) +
  scale_y_log10(breaks = seq(50, 200, 5))  +
  geom_rect(data = nber_recessions |>
              filter(Peak > as.Date("1999-01-01")), 
            aes(xmin = Peak, xmax = Trough, ymin = 0, ymax = +Inf),
            fill = '#B22234', alpha = 0.1)  +
  geom_rect(data = cepr_recessions |>
              filter(Peak > as.Date("1999-01-01")), 
            aes(xmin = Peak, xmax = Trough, ymin = 0, ymax = +Inf), 
            fill = '#003399', alpha = 0.1)  +
  labs(caption = "Source: OCDE, Comptes Trimestriels, Volumes chaînés")


save(plot, file = "QNA_EXPENDITURE_NATIO_CURR_files/figure-html/USA-EA-FRA-DEU-1999-percapita-1.RData")

plot

P31 - Individual consumption expenditure

QNA_EXPENDITURE_NATIO_CURR |>
  filter(REF_AREA %in% c("USA", "EA", "FRA", "DEU"),
         FREQ == "Q",
         TRANSACTION == "P31",
         # L = Chain linked volume
         PRICE_BASE == "L",
         ADJUSTMENT == "Y") |>
  left_join(QNA_POP_EMPNC_extract, by = c("obsTime", "REF_AREA", "Ref_area")) |>
  mutate(obsValue = obsValue/population) |>
  quarter_to_date() |>
  
  
  filter(date >= as.Date("1999-01-01")) |>
  mutate(Ref_area = ifelse(REF_AREA == "EA", "Europe", Ref_area)) |>
  group_by(Ref_area) |>
  arrange(date) |>
  mutate(obsValue = 100 * obsValue / obsValue[1]) |>
  add_flag_color("Ref_area") |>
  ggplot() + theme_minimal() + xlab("") + ylab("") +
  geom_line(aes(x = date, y = obsValue, color = color)) + add_3flags +
  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)) + 
  geom_label_repel(data = . %>% filter(date == max(date)),
             aes(x = date, y = obsValue, label = round(obsValue, 1), color = color))

P32 - Collective consumption expenditure

QNA_EXPENDITURE_NATIO_CURR |>
  filter(REF_AREA %in% c("USA", "EA", "FRA", "DEU"),
         FREQ == "Q",
         TRANSACTION == "P32",
         # L = Chain linked volume
         PRICE_BASE == "L",
         ADJUSTMENT == "Y") |>
  left_join(QNA_POP_EMPNC_extract, by = c("obsTime", "REF_AREA", "Ref_area")) |>
  mutate(obsValue = obsValue/population) |>
  quarter_to_date() |>
  
  
  filter(date >= as.Date("1999-01-01")) |>
  mutate(Ref_area = ifelse(REF_AREA == "EA", "Europe", Ref_area)) |>
  group_by(Ref_area) |>
  arrange(date) |>
  mutate(obsValue = 100 * obsValue / obsValue[1]) |>
  add_flag_color("Ref_area") |>
  ggplot() + theme_minimal() + xlab("") + ylab("") +
  geom_line(aes(x = date, y = obsValue, color = color)) + add_3flags +
  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)) + 
  geom_label_repel(data = . %>% filter(date == max(date)),
             aes(x = date, y = obsValue, label = round(obsValue, 1), color = color))

P41 - Actual individual consumption

QNA_EXPENDITURE_NATIO_CURR |>
  filter(REF_AREA %in% c("USA", "EA", "FRA", "DEU"),
         FREQ == "Q",
         TRANSACTION == "P41",
         # L = Chain linked volume
         PRICE_BASE == "L",
         ADJUSTMENT == "Y") |>
     left_join(QNA_POP_EMPNC_extract, by = c("obsTime", "REF_AREA", "Ref_area")) |>
  mutate(obsValue = obsValue/population) |>
  quarter_to_date() |>
  
  
  filter(date >= as.Date("1999-01-01")) |>
  mutate(Ref_area = ifelse(REF_AREA == "EA", "Europe", Ref_area)) |>
  group_by(Ref_area) |>
  arrange(date) |>
  mutate(obsValue = 100 * obsValue / obsValue[1]) |>
  add_flag_color("Ref_area") |>
  ggplot() + theme_minimal() + xlab("") + ylab("") +
  geom_line(aes(x = date, y = obsValue, color = color)) + add_2flags +
  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)) + 
  geom_label_repel(data = . %>% filter(date == max(date)),
             aes(x = date, y = obsValue, label = round(obsValue, 1), color = color))

P3T5 - Domestic demand

QNA_EXPENDITURE_NATIO_CURR |>
  filter(REF_AREA %in% c("USA", "EA", "FRA", "DEU"),
         FREQ == "Q",
         TRANSACTION == "P3T5",
         # L = Chain linked volume
         PRICE_BASE == "L",
         ADJUSTMENT == "Y") |>
     left_join(QNA_POP_EMPNC_extract, by = c("obsTime", "REF_AREA", "Ref_area")) |>
  mutate(obsValue = obsValue/population) |>
  quarter_to_date() |>
  
  
  filter(date >= as.Date("1999-01-01")) |>
  mutate(Ref_area = ifelse(REF_AREA == "EA", "Europe", Ref_area)) |>
  group_by(Ref_area) |>
  arrange(date) |>
  mutate(obsValue = 100 * obsValue / obsValue[1]) |>
  add_flag_color("Ref_area") |>
  ggplot() + theme_minimal() + xlab("") + ylab("") +
  geom_line(aes(x = date, y = obsValue, color = color)) + add_3flags +
  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)) + 
  geom_label_repel(data = . %>% filter(date == max(date)),
             aes(x = date, y = obsValue, label = round(obsValue, 1), color = color))

Net exports

Eurozone, US, France, Germany

Tous

QNA_EXPENDITURE_NATIO_CURR |>
  filter(REF_AREA %in% c("EA", "USA", "DEU", "FRA"),
         FREQ == "Q",
         TRANSACTION %in% c("B11", "B1GQ"),
         # L = Chain linked volume
         PRICE_BASE == "V",
         ADJUSTMENT == "Y") |>
  
  mutate(Ref_area = ifelse(REF_AREA == "EA", "Europe", Ref_area)) |>
  group_by(Ref_area) %>%
  select_if(~ n_distinct(.) > 1) |>
  select(-OBS_STATUS) |>
  spread(TRANSACTION, obsValue) |>
  quarter_to_date() |>
  filter(date >= as.Date("1970-01-01")) |>
  transmute(date, Ref_area, obsValue = B11/B1GQ) |>
  add_flag_color("Ref_area") |>
  ggplot() + geom_line(aes(x = date, y = obsValue, color = color)) +
  theme_minimal() + xlab("") + ylab("") + scale_color_identity() + add_4flags +
  scale_x_date(breaks = c(seq(1970, 2100, 5)) |> paste0("-01-01") |> as.Date(),
               labels = date_format("%Y")) +
  scale_y_continuous(breaks = 0.01*seq(-100, 100, 1),
                labels = percent_format(acc = 1)) +
  theme(legend.position = c(0.2, 0.8),
        legend.title = element_blank()) +
  geom_hline(yintercept = 0, linetype = "dashed")

1990-

QNA_EXPENDITURE_NATIO_CURR |>
  filter(REF_AREA %in% c("EA", "USA", "DEU", "FRA"),
         FREQ == "Q",
         TRANSACTION %in% c("B11", "B1GQ"),
         # L = Chain linked volume
         PRICE_BASE == "V",
         ADJUSTMENT == "Y") |>
  
     
  mutate(Ref_area = ifelse(REF_AREA == "EA", "Europe", Ref_area)) |>
  group_by(Ref_area) %>%
  select_if(~ n_distinct(.) > 1) |>
  select(-OBS_STATUS) |>
  spread(TRANSACTION, obsValue) |>
  quarter_to_date() |>
  filter(date >= as.Date("1990-01-01")) |>
  transmute(date, Ref_area, obsValue = B11/B1GQ) |>
  add_flag_color("Ref_area") |>
  ggplot() + geom_line(aes(x = date, y = obsValue, color = color)) +
  theme_minimal() + xlab("") + ylab("") + scale_color_identity() + add_4flags +
  scale_x_date(breaks = c(seq(1970, 2100, 5)) |> paste0("-01-01") |> as.Date(),
               labels = date_format("%Y")) +
  scale_y_continuous(breaks = 0.01*seq(-100, 100, 1),
                labels = percent_format(acc = 1)) +
  theme(legend.position = c(0.2, 0.8),
        legend.title = element_blank()) +
  geom_hline(yintercept = 0, linetype = "dashed")

Eurozone, US

Tous

QNA_EXPENDITURE_NATIO_CURR |>
  filter(REF_AREA %in% c("EA", "USA"),
         FREQ == "Q",
         TRANSACTION %in% c("B11", "B1GQ"),
         # L = Chain linked volume
         PRICE_BASE == "V",
         ADJUSTMENT == "Y") |>
  
  mutate(Ref_area = ifelse(REF_AREA == "EA", "Europe", Ref_area)) |>
  group_by(Ref_area) %>%
  select_if(~ n_distinct(.) > 1) |>
  spread(TRANSACTION, obsValue) |>
  quarter_to_date() |>
  transmute(date, REF_AREA, Ref_area, obsValue = B11/B1GQ) |>
  add_flag_color("Ref_area") |>
  ggplot() + geom_line(aes(x = date, y = obsValue, color = color)) +
  theme_minimal() + xlab("") + ylab("") + scale_color_identity() + add_2flags +
  scale_x_date(breaks = c(seq(1920, 2100, 5)) |> paste0("-01-01") |> as.Date(),
               labels = date_format("%Y")) +
  scale_y_continuous(breaks = 0.01*seq(-100, 100, 1),
                labels = percent_format(acc = 1)) +
  theme(legend.position = c(0.2, 0.8),
        legend.title = element_blank()) +
  geom_hline(yintercept = 0, linetype = "dashed")

1970-

QNA_EXPENDITURE_NATIO_CURR |>
  filter(REF_AREA %in% c("EA", "USA"),
         FREQ == "Q",
         TRANSACTION %in% c("B11", "B1GQ"),
         # L = Chain linked volume
         PRICE_BASE == "V",
         ADJUSTMENT == "Y") |>
  
  mutate(Ref_area = ifelse(REF_AREA == "EA", "Europe", Ref_area)) |>
  group_by(Ref_area) %>%
  select_if(~ n_distinct(.) > 1) |>
  spread(TRANSACTION, obsValue) |>
  quarter_to_date() |>
  filter(date >= as.Date("1970-01-01")) |>
  transmute(date, REF_AREA, Ref_area, obsValue = B11/B1GQ) |>
  add_flag_color("Ref_area") |>
  ggplot() + geom_line(aes(x = date, y = obsValue, color = color)) +
  theme_minimal() + xlab("") + ylab("") + scale_color_identity() + add_2flags +
  scale_x_date(breaks = c(seq(1970, 2100, 5)) |> paste0("-01-01") |> as.Date(),
               labels = date_format("%Y")) +
  scale_y_continuous(breaks = 0.01*seq(-100, 100, 1),
                labels = percent_format(acc = 1)) +
  theme(legend.position = c(0.2, 0.8),
        legend.title = element_blank()) +
  geom_hline(yintercept = 0, linetype = "dashed")

1990-

QNA_EXPENDITURE_NATIO_CURR |>
  filter(REF_AREA %in% c("EA", "USA"),
         FREQ == "Q",
         TRANSACTION %in% c("B11", "B1GQ"),
         # L = Chain linked volume
         PRICE_BASE == "V",
         ADJUSTMENT == "Y") |>
  
  mutate(Ref_area = ifelse(REF_AREA == "EA", "Europe", Ref_area)) |>
  group_by(Ref_area) %>%
  select_if(~ n_distinct(.) > 1) |>
  spread(TRANSACTION, obsValue) |>
  quarter_to_date() |>
  filter(date >= as.Date("1995-01-01")) |>
  transmute(date, REF_AREA, Ref_area, obsValue = B11/B1GQ) |>
  add_flag_color("Ref_area") |>
  ggplot() + geom_line(aes(x = date, y = obsValue, color = color)) +
  theme_minimal() + xlab("") + ylab("") + scale_color_identity() + add_2flags +
  scale_x_date(breaks = c(seq(1970, 2100, 5)) |> paste0("-01-01") |> as.Date(),
               labels = date_format("%Y")) +
  scale_y_continuous(breaks = 0.01*seq(-100, 100, 1),
                labels = percent_format(acc = 1)) +
  theme(legend.position = c(0.2, 0.8),
        legend.title = element_blank()) +
  geom_hline(yintercept = 0, linetype = "dashed")

Exports and Imports as a % of GDP

EA 20

QNA_EXPENDITURE_NATIO_CURR |>
  filter(REF_AREA %in% c("EA"),
         FREQ == "Q",
         TRANSACTION %in% c("P6", "P7", "B1GQ"),
         # L = Chain linked volume
         PRICE_BASE == "L",
         ADJUSTMENT == "Y") %>%
  select_if(~ n_distinct(.) > 1) |>
  select(-OBS_STATUS) |>
  spread(TRANSACTION, obsValue) |>
  quarter_to_date() |>
  transmute(date, `Exports (% of GDP)` = P6/B1GQ, `Imports (% of GDP)` = P7/B1GQ) |>
  gather(variable, value, -date) |>
  ggplot() + geom_line(aes(x = date, y = value, color = variable)) +
  theme_minimal() + xlab("") + ylab("") +
  scale_x_date(breaks = c(seq(1999, 2100, 5), seq(1997, 2100, 5)) |> paste0("-01-01") |> as.Date(),
               labels = date_format("%Y")) +
  scale_y_continuous(breaks = 0.01*seq(-100, 100, 5),
                labels = percent_format(acc = 1)) +
  theme(legend.position = c(0.2, 0.8),
        legend.title = element_blank())

USA

QNA_EXPENDITURE_NATIO_CURR |>
  filter(REF_AREA %in% c("USA"),
         FREQ == "Q",
         TRANSACTION %in% c("P6", "P7", "B1GQ"),
         # L = Chain linked volume
         PRICE_BASE == "V",
         ADJUSTMENT == "Y") %>%
  select_if(~ n_distinct(.) > 1) |>
  spread(TRANSACTION, obsValue) |>
  quarter_to_date() |>
  transmute(date, `Exports (% of GDP)` = P6/B1GQ, `Imports (% of GDP)` = P7/B1GQ) |>
  gather(variable, value, -date) |>
  ggplot() + geom_line(aes(x = date, y = value, color = variable)) +
  theme_minimal() + xlab("") + ylab("") +
  scale_x_date(breaks = c(seq(1940, 2100, 5)) |> paste0("-01-01") |> as.Date(),
               labels = date_format("%Y")) +
  scale_y_continuous(breaks = 0.01*seq(-100, 100, 2),
                labels = percent_format(acc = 1)) +
  theme(legend.position = c(0.2, 0.8),
        legend.title = element_blank())

Germany

QNA_EXPENDITURE_NATIO_CURR |>
  filter(REF_AREA %in% c("DEU"),
         FREQ == "Q",
         TRANSACTION %in% c("P6", "P7", "B1GQ"),
         # L = Chain linked volume
         PRICE_BASE == "V",
         ADJUSTMENT == "Y") %>%
  select_if(~ n_distinct(.) > 1) |>
  spread(TRANSACTION, obsValue) |>
  quarter_to_date() |>
  transmute(date, `Exports (% of GDP)` = P6/B1GQ, `Imports (% of GDP)` = P7/B1GQ) |>
  gather(variable, value, -date) |>
  ggplot() + geom_line(aes(x = date, y = value, color = variable)) +
  theme_minimal() + xlab("") + ylab("") +
  scale_x_date(breaks = c(seq(1940, 2100, 5)) |> paste0("-01-01") |> as.Date(),
               labels = date_format("%Y")) +
  scale_y_continuous(breaks = 0.01*seq(-100, 100, 2),
                labels = percent_format(acc = 1)) +
  theme(legend.position = c(0.2, 0.8),
        legend.title = element_blank())

France

QNA_EXPENDITURE_NATIO_CURR |>
  filter(REF_AREA %in% c("FRA"),
         FREQ == "Q",
         TRANSACTION %in% c("P6", "P7", "B1GQ"),
         # L = Chain linked volume
         PRICE_BASE == "V",
         ADJUSTMENT == "Y") %>%
  select_if(~ n_distinct(.) > 1) |>
  spread(TRANSACTION, obsValue) |>
  quarter_to_date() |>
  transmute(date, `Exports (% of GDP)` = P6/B1GQ, `Imports (% of GDP)` = P7/B1GQ) |>
  gather(variable, value, -date) |>
  ggplot() + geom_line(aes(x = date, y = value, color = variable)) +
  theme_minimal() + xlab("") + ylab("") +
  scale_x_date(breaks = c(seq(1940, 2100, 5)) |> paste0("-01-01") |> as.Date(),
               labels = date_format("%Y")) +
  scale_y_continuous(breaks = 0.01*seq(-100, 100, 2),
                labels = percent_format(acc = 1)) +
  theme(legend.position = c(0.2, 0.8),
        legend.title = element_blank())

Related Data

Germany by the Numbers

source	dataset	Title	Updated
eurostat	bop_c6_q	Balance of payments by country - quarterly data (BPM6) - bop_c6_q	2026-04-13
eurostat	ilc_di01	Distribution of income by quantiles - EU-SILC and ECHP surveys - ilc_di01	2026-10-06
eurostat	ilc_li02	At-risk-of-poverty rate by poverty threshold, age and sex - EU-SILC and ECHP surveys - ilc_li02	2026-10-06
eurostat	sts_intvnd_m	Turnover in industry, non domestic market - monthly data - sts_intvnd_m	2026-10-15
oecd	7HA_A_Q	Households’ financial and non-financial assets and liabilities - 7HA_A_Q	2026-08-02
oecd	ANHRS	Average annual hours actually worked per worker - ANHRS	2026-08-02
oecd	AV_AN_WAGE	Average annual wages - AV_AN_WAGE	2026-10-15
oecd	CTS_CIT	Statutory Corporate Income Tax Rates - CTS_CIT	2026-08-02
oecd	DP_LIVE	OECD Data Live dataset - DP_LIVE	2026-08-02
oecd	EO	Economic Outlook	2026-10-15
oecd	GENDER_EMP	Employment by Sex and by Age - GENDER_EMP	2026-08-02
oecd	IDD	Income Distribution Database - IDD	2026-10-15
oecd	LFS_SEXAGE_I_R	LFS by sex and age - indicators - LFS_SEXAGE_I_R	2026-08-02
oecd	MEI_BOP6	Balance of Payments BPM6 - MEI_BOP6	2026-08-02
oecd	METR	Effective tax rate on increasing working hours - METR	2026-08-02
oecd	NAAG	National Accounts at a Glance - NAAG	2026-08-02
oecd	NRR	Net Replacement Rates in unemployment - NRR	2026-08-02
oecd	PAG	Pensions at A Glance - PAG	2026-08-02
oecd	QASA_TABLE720R	Non-consolidated financial balance sheets by economic sector (Quarterly table 0720) - SNA 2008 - QASA_TABLE720R	2026-08-02
oecd	QNA	Quarterly National Accounts - QNA	2026-10-15
oecd	QNA_EXPENDITURE_NATIO_CURR	Quarterly GDP and components - expenditure approach - QNA_EXPENDITURE_NATIO_CURR	2026-10-15
oecd	REV	Revenue Statistics - OECD countries; Comparative tables - REV	2026-08-02
oecd	SNA_TABLE14A	Non-financial accounts by sectors - SNA_TABLE14A	2026-08-02
oecd	SNA_TABLE8A	Capital formation by activity ISIC rev4 - SNA_TABLE8A	2026-08-02
oecd	SOCX_AGG	Social Expenditure - Aggregated data - SOCX_AGG	2026-08-02
oecd	TABLE_I1	Central government personal income tax rates and thresholds - TABLE_I1	2026-08-02
oecd	TABLE_I7	Top statutory personal income tax rate and top marginal tax rates for employees - TABLE_I7	2026-08-02
oecd	TABLE_II4	Overall statutory tax rates on dividend income - TABLE_II4	2026-08-02
oecd	TABLE_III1	Employee social security contribution rates - TABLE_III1	2026-08-02
oecd	ULC_ANN	Unit Labour Costs - Annual Indicators - ULC_ANN	2026-08-02
wdi	NY.GDP.PCAP.PP.KD	GDP per capita, PPP (constant 2011 international D) - NY.GDP.PCAP.PP.KD	2026-10-15

Global Trade Data

source	dataset	Title	Updated
oecd	QNA_EXPENDITURE_NATIO_CURR	Quarterly GDP and components - expenditure approach - QNA_EXPENDITURE_NATIO_CURR	2026-10-15

Comparing the United States and Europe

source	dataset	Title	Updated
bis	CBPOL_D	Policy Rates, Daily - CBPOL_D	2026-10-18
fred	money	Money - money	2026-10-15
imf	GGXCNL_G01_GDP_PT	Net lending/borrowing (also referred as overall balance) (% of GDP) - GGXCNL_G01_GDP_PT	2026-10-15
imf	G_XWDG_G01_GDP_PT	Gross debt (% of GDP) - G_XWDG_G01_GDP_PT	2026-10-15
imf	IFR_BP6_USD	Net International Investment Position (With Fund Record), US Dollars - IFR_BP6_USD	2026-08-01
oecd	BOP	Balance of payments - BOP	2026-10-15
oecd	QNA_EXPENDITURE_CAPITA	Quarterly National Accounts, GDP Per Capita - QNA_EXPENDITURE_CAPITA	2026-10-15
oecd	QNA_EXPENDITURE_NATIO_CURR	Quarterly GDP and components - expenditure approach - QNA_EXPENDITURE_NATIO_CURR	2026-10-15
wdi	NY.GDP.MKTP.PP.CD	GDP, PPP (current international D) - NY.GDP.MKTP.PP.CD	2026-10-15
wdi	NY.GDP.PCAP.CD	GDP per capita (current USD) - NY.GDP.PCAP.CD	2026-10-15
wdi	NY.GDP.PCAP.PP.CD	GDP per capita, PPP (current international D) - NY.GDP.PCAP.PP.CD	2026-10-15