Gross domestic product (GDP)

Data - OECD

~/data/oecd/

Info

source	dataset	.html	.RData
oecd	SNA_TABLE1	2024-04-15	2024-04-15

Data on main macro

source	dataset	.html	.RData
eurostat	nama_10_a10	2024-04-15	2024-04-09
eurostat	nama_10_a10_e	2024-04-15	2024-04-09
eurostat	nama_10_gdp	2024-04-15	2024-04-09
eurostat	nama_10_lp_ulc	2024-04-15	2024-04-15
eurostat	namq_10_a10	2024-04-15	2024-04-15
eurostat	namq_10_a10_e	2024-04-15	2024-04-15
eurostat	namq_10_gdp	2024-04-15	2024-04-09
eurostat	namq_10_lp_ulc	2024-04-15	2024-04-09
eurostat	namq_10_pc	2024-04-15	2024-04-09
eurostat	nasa_10_nf_tr	2024-04-15	2024-04-09
eurostat	nasq_10_nf_tr	2024-04-15	2024-04-15
fred	gdp	2024-04-15	2024-04-15
oecd	QNA	2024-04-16	2024-04-15
oecd	SNA_TABLE1	2024-04-15	2024-04-15
oecd	SNA_TABLE14A	2024-04-15	2024-04-15
oecd	SNA_TABLE2	2024-04-15	2024-04-11
oecd	SNA_TABLE6A	2024-04-15	2024-04-15
wdi	NE.RSB.GNFS.ZS	2024-04-14	2024-04-14
wdi	NY.GDP.MKTP.CD	2024-04-14	2024-04-14
wdi	NY.GDP.MKTP.PP.CD	2024-04-14	2024-04-14
wdi	NY.GDP.PCAP.CD	2024-04-14	2024-04-14
wdi	NY.GDP.PCAP.KD	2024-04-14	2024-04-14
wdi	NY.GDP.PCAP.PP.CD	2024-04-14	2024-04-14
wdi	NY.GDP.PCAP.PP.KD	2024-04-14	2024-04-14

LAST_COMPILE

LAST_COMPILE
2024-04-16

Last

obsTime	Nobs
2023	1340

Layout - By country

• OECD Website. html

Nobs - Javascript

SNA_TABLE1 %>%
  left_join(SNA_TABLE1_var$TRANSACT, by = "TRANSACT") %>%
  left_join(SNA_TABLE1_var$MEASURE, by = "MEASURE") %>%
  group_by(TRANSACT, Transact, MEASURE, Measure) %>%
  summarise(nobs = n()) %>%
  arrange(-nobs) %>%
  {if (is_html_output()) datatable(., filter = 'top', rownames = F) else .}

TRANSACT

SNA_TABLE1 %>%
  left_join(SNA_TABLE1_var$TRANSACT, by = "TRANSACT") %>%
  group_by(TRANSACT, Transact) %>%
  summarise(Nobs = n()) %>%
  arrange(-Nobs) %>%
  print_table_conditional()

MEASURE

SNA_TABLE1 %>%
  left_join(SNA_TABLE1_var$MEASURE, by = "MEASURE") %>%
  group_by(MEASURE, Measure) %>%
  summarise(Nobs = n()) %>%
  print_table_conditional()

LOCATION

SNA_TABLE1 %>%
  left_join(SNA_TABLE1_var$LOCATION, by = "LOCATION") %>%
  group_by(LOCATION, Location) %>%
  summarise(Nobs = n()) %>%
  arrange(-Nobs) %>%
  mutate(Flag = gsub(" ", "-", str_to_lower(gsub(" ", "-", Location))),
         Flag = paste0('<img src="../../icon/flag/vsmall/', Flag, '.png" alt="Flag">')) %>%
  select(Flag, everything()) %>%
  {if (is_html_output()) datatable(., filter = 'top', rownames = F, escape = F) else .}

Goods Net Exports

NX, Total, Goods, Services

SNA_TABLE1 %>%
  filter(obsTime == "2018",
         MEASURE == "C",
         TRANSACT %in% c("B1_GE", "P61", "P71", "P62", "P72", "P6", "P7")) %>%
  left_join(SNA_TABLE1_var$LOCATION, by = "LOCATION") %>%
  group_by(Location) %>%
  mutate(obsValue = 100*obsValue / obsValue[TRANSACT == "B1_GE"]) %>%
  filter(TRANSACT != "B1_GE") %>%
  select(LOCATION, Location, TRANSACT, obsValue) %>%
  na.omit %>%
  spread(TRANSACT, obsValue) %>%
  mutate(NX = P6 - P7,
         `Goods NX` = P61 - P71,
         `Services NX` = P62 - P72) %>%
  select(LOCATION, Location, NX, `Goods NX`, `Services NX`) %>%
  arrange(-`Goods NX`) %>%
  mutate_at(vars(-1, -2), funs(ifelse(is.na(.), NA, paste0(round(., 1), "%")))) %>%
  mutate(Flag = gsub(" ", "-", str_to_lower(gsub(" ", "-", Location))),
         Flag = paste0('<img src="../../icon/flag/vsmall/', Flag, '.png" alt="Flag">')) %>%
  select(Flag, everything()) %>%
  {if (is_html_output()) datatable(., filter = 'top', rownames = F, escape = F) else .}

France, Germany, United Kingdom

SNA_TABLE1 %>%
  filter(LOCATION %in% c("DEU", "FRA", "GBR"),
         MEASURE == "C",
         TRANSACT %in% c("B1_GE", "P61", "P71")) %>%
  year_to_date %>%
  left_join(SNA_TABLE1_var$LOCATION, by = "LOCATION") %>%
  select(date, Location, TRANSACT, obsValue) %>%
  spread(TRANSACT, obsValue) %>%
  mutate(obsValue = (P61 - P71)/B1_GE) %>%
  left_join(colors, by = c("Location" = "country")) %>%
  ggplot() + theme_minimal() + ylab("Goods Net Exports (% of GDP)") + xlab("") +
  geom_line(aes(x = date, y = obsValue, color = color)) +
  scale_color_identity() + add_3flags +
  scale_x_date(breaks = seq(1920, 2025, 10) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  scale_y_continuous(breaks = 0.01*seq(-10, 100, 1),
                     labels = scales::percent_format(accuracy = 1)) +
  geom_hline(yintercept = 0, linetype = "dashed")

Italy, Portugal, Spain

SNA_TABLE1 %>%
  filter(LOCATION %in% c("ITA", "PRT", "ESP"),
         MEASURE == "C",
         TRANSACT %in% c("B1_GE", "P61", "P71")) %>%
  year_to_date %>%
  left_join(SNA_TABLE1_var$LOCATION, by = "LOCATION") %>%
  select(date, Location, TRANSACT, obsValue) %>%
  spread(TRANSACT, obsValue) %>%
  mutate(obsValue = (P61 - P71)/B1_GE) %>%
  left_join(colors, by = c("Location" = "country")) %>%
  ggplot() + theme_minimal() + ylab("Goods Net Exports (% of GDP)") + xlab("") +
  geom_line(aes(x = date, y = obsValue, color = color)) +
  scale_color_identity() + add_3flags +
  scale_x_date(breaks = seq(1920, 2025, 10) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  scale_y_continuous(breaks = 0.01*seq(-30, 100, 1),
                     labels = scales::percent_format(accuracy = 1)) +
  geom_hline(yintercept = 0, linetype = "dashed")

Netherlands, Russia, Switzerland

SNA_TABLE1 %>%
  filter(LOCATION %in% c("RUS", "NLD", "CHE"),
         MEASURE == "C",
         TRANSACT %in% c("B1_GE", "P61", "P71")) %>%
  year_to_date %>%
  left_join(SNA_TABLE1_var$LOCATION, by = "LOCATION") %>%
  select(date, Location, TRANSACT, obsValue) %>%
  spread(TRANSACT, obsValue) %>%
  mutate(obsValue = (P61 - P71)/B1_GE) %>%
  left_join(colors, by = c("Location" = "country")) %>%
  ggplot() + theme_minimal() + ylab("Goods Net Exports (% of GDP)") + xlab("") +
  geom_line(aes(x = date, y = obsValue, color = color)) +
  scale_color_identity() + add_3flags +
  scale_x_date(breaks = seq(1920, 2025, 10) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  scale_y_continuous(breaks = 0.01*seq(-10, 100, 1),
                     labels = scales::percent_format(accuracy = 1)) +
  geom_hline(yintercept = 0, linetype = "dashed")

Industry/ Manufacturing Decline and Trade Deficits

Industry

SNA_TABLE1 %>%
  mutate(date = paste0(obsTime, "-12-31") %>% as.Date) %>%
  filter(MEASURE == "C",
         TRANSACT %in% c("B1_GE", "B1GVB_E", "B11", "P61", "P71"),
         date >= as.Date("2016-01-01"),
         date <= as.Date("2020-01-01")) %>%
  select(date, LOCATION, TRANSACT, obsValue) %>%
  spread(TRANSACT, obsValue) %>%
  group_by(LOCATION) %>%
  summarise(NX_mean = mean((P61-P71) / B1_GE, na.rm = T),
            IND_mean = mean(B1GVB_E / B1_GE, na.rm = T)) %>%
  left_join(SNA_TABLE1_var$LOCATION, by = "LOCATION") %>%
  ggplot(.) + theme_minimal() + geom_point(aes(x = NX_mean, y = IND_mean)) +
  xlab("Goods Trade Balance 2015-2020 (% of GDP)") + 
  ylab("Industry VA 2015-2020 (% of GDP)") +
  scale_x_continuous(breaks = 0.01*seq(-100, 100, 5),
                     labels = percent_format(accuracy = 1)) +
  scale_y_continuous(breaks = 0.01*seq(-100, 100, 5),
                     labels = percent_format(accuracy = 1)) +
  stat_smooth(aes(x = NX_mean, y = IND_mean), 
              linetype = 2, method = "lm", color = viridis(3)[2]) +
  geom_text_repel(aes(x = NX_mean, y = IND_mean, label = Location), vjust = 0)

Industry

SNA_TABLE1 %>%
  filter(MEASURE == "C",
         TRANSACT %in% c("B1_GE", "B1GVC", "B11", "P61", "P71")) %>%
  year_to_date %>%
  filter(date >= as.Date("2000-01-01"),
         date <= as.Date("2004-01-01")) %>%
  select(date, LOCATION, TRANSACT, obsValue) %>%
  spread(TRANSACT, obsValue) %>%
  mutate(IND = B1GVC / B1_GE,
         NX = (P61-P71) / B1_GE) %>%
  group_by(LOCATION) %>%
  summarise(NX_mean = mean(NX, na.rm = T),
            IND_mean = mean(IND, na.rm = T)) %>%
  left_join(SNA_TABLE1_var$LOCATION, by = "LOCATION") %>%
  lm(IND_mean ~ NX_mean, data = .) %>%
  summary

# 
# Call:
# lm(formula = IND_mean ~ NX_mean, data = .)
# 
# Residuals:
#       Min        1Q    Median        3Q       Max 
# -0.144795 -0.028173  0.001434  0.023485  0.090271 
# 
# Coefficients:
#             Estimate Std. Error t value Pr(>|t|)    
# (Intercept) 0.162048   0.007204  22.494   <2e-16 ***
# NX_mean     0.141768   0.065857   2.153   0.0366 *  
# ---
# Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
# 
# Residual standard error: 0.04696 on 46 degrees of freedom
#   (10 observations effacées parce que manquantes)
# Multiple R-squared:  0.09152, Adjusted R-squared:  0.07177 
# F-statistic: 4.634 on 1 and 46 DF,  p-value: 0.03662

SNA_TABLE1 %>%
  mutate(date = paste0(obsTime, "-12-31") %>% as.Date) %>%
  filter(MEASURE == "C",
         TRANSACT %in% c("B1_GE", "B1GVB_E", "B11", "P61", "P71"),
         date >= as.Date("2016-01-01"),
         date <= as.Date("2020-01-01"),
         LOCATION != "IRL") %>%
  select(date, LOCATION, TRANSACT, obsValue) %>%
  spread(TRANSACT, obsValue) %>%
  group_by(LOCATION) %>%
  summarise(NX_mean = mean((P61-P71) / B1_GE, na.rm = T),
            IND_mean = mean(B1GVB_E / B1_GE, na.rm = T)) %>%
  left_join(SNA_TABLE1_var$LOCATION, by = "LOCATION") %>%
  ggplot(.) + theme_minimal() + geom_point(aes(x = NX_mean, y = IND_mean)) +
  xlab("Goods Trade Balance 2015-2020 (% of GDP)") + 
  ylab("Industry VA 2015-2020 (% of GDP)") +
  scale_x_continuous(breaks = 0.01*seq(-100, 100, 5),
                     labels = percent_format(accuracy = 1)) +
  scale_y_continuous(breaks = 0.01*seq(-100, 100, 5),
                     labels = percent_format(accuracy = 1)) +
  stat_smooth(aes(x = NX_mean, y = IND_mean), 
              linetype = 2, method = "lm", color = viridis(3)[2]) +
  geom_text_repel(aes(x = NX_mean, y = IND_mean, label = Location), vjust = 0)

var1 <- SNA_TABLE1 %>%
  mutate(date = paste0(obsTime, "-12-31") %>% as.Date) %>%
  filter(MEASURE == "C",
         TRANSACT %in% c("B1_GE", "B1GVB_E", "B11", "P61", "P71"),
         date >= as.Date("2000-01-01"),
         date <= as.Date("2018-01-01"),
         LOCATION != "IRL") %>%
  select(date, LOCATION, TRANSACT, obsValue) %>%
  spread(TRANSACT, obsValue) %>%
  group_by(LOCATION) %>%
  summarise(NX_mean = mean((P61-P71) / B1_GE, na.rm = T),
            IND_mean = mean(B1GVB_E / B1_GE, na.rm = T))

var2 <- SNA_TABLE1 %>%
  mutate(date = paste0(obsTime, "-12-31") %>% as.Date) %>%
  filter(MEASURE == "C",
         TRANSACT %in% c("B1_GE", "B1GVB_E"),
         date %in% c(as.Date("2018-12-31"), as.Date("2000-12-31")),
         LOCATION != "IRL") %>%
  select(date, LOCATION, TRANSACT, obsValue) %>%
  spread(TRANSACT, obsValue) %>%
  mutate(B1GVB_E_share = B1GVB_E/B1_GE) %>%
  group_by(LOCATION) %>%
  filter(n() == 2) %>%
  summarise(D1_B1GVB_E_share = B1GVB_E_share[date == as.Date("2018-12-31")]
            - B1GVB_E_share[date == as.Date("2000-12-31")])

var2 <- SNA_TABLE1 %>%
  mutate(date = paste0(obsTime, "-12-31") %>% as.Date) %>%
  filter(MEASURE == "C",
         TRANSACT %in% c("B1_GE", "B1GVB_E"),
         date %in% c(as.Date("2018-12-31"), as.Date("2000-12-31")),
         LOCATION != "IRL") %>%
  select(date, LOCATION, TRANSACT, obsValue) %>%
  spread(TRANSACT, obsValue) %>%
  mutate(B1GVB_E_share = B1GVB_E/B1_GE) %>%
  group_by(LOCATION) %>%
  filter(n() == 2) %>%
  summarise(D1_B1GVB_E_share = B1GVB_E_share[date == as.Date("2018-12-31")]
            - B1GVB_E_share[date == as.Date("2000-12-31")])

var1 %>%
  inner_join(var2, by = "LOCATION") %>%
  left_join(SNA_TABLE1_var$LOCATION, by = "LOCATION") %>%
  ggplot(.) + theme_minimal() + geom_point(aes(x = NX_mean, y = D1_B1GVB_E_share)) +
  xlab("Goods Trade Balance 2000-2018 (% of GDP)") + 
  ylab("Industry VA 2018 (% of GDP)") +
  scale_x_continuous(breaks = 0.01*seq(-100, 100, 5),
                     labels = percent_format(accuracy = 1)) +
  scale_y_continuous(breaks = 0.01*seq(-100, 100, 5),
                     labels = percent_format(accuracy = 1)) +
  stat_smooth(aes(x = NX_mean, y = D1_B1GVB_E_share), 
              linetype = 2, method = "lm", color = viridis(3)[2]) +
  geom_text_repel(aes(x = NX_mean, y = D1_B1GVB_E_share, label = LOCATION), vjust = 0)

Industry (without Ireland)

SNA_TABLE1 %>%
  filter(MEASURE == "C",
         TRANSACT %in% c("B1_GE", "B1GVB_E", "B11", "P61", "P71"),
         !(LOCATION == "IRL")) %>%
  year_to_date %>%
  filter(date >= as.Date("2016-01-01"),
         date <= as.Date("2020-01-01")) %>%
  select(date, LOCATION, TRANSACT, obsValue) %>%
  spread(TRANSACT, obsValue) %>%
  mutate(IND = B1GVB_E / B1_GE,
         NX = (P61-P71) / B1_GE) %>%
  group_by(LOCATION) %>%
  summarise(NX_mean = mean(NX, na.rm = T),
            IND_mean = mean(IND, na.rm = T)) %>%
  left_join(SNA_TABLE1_var$LOCATION, by = "LOCATION") %>%
  ggplot(.) + theme_minimal() + geom_point(aes(x = NX_mean, y = IND_mean)) +
  xlab("Goods Trade Balance 2015-2020 (% of GDP)") + ylab("Industry VA 2015-2020 (% of GDP)") +
  scale_x_continuous(breaks = 0.01*seq(-100, 100, 5),
                     labels = percent_format(accuracy = 1)) +
  geom_text_repel(aes(x = NX_mean, y = IND_mean, label = Location), vjust = 0) +
  scale_y_continuous(breaks = 0.01*seq(-100, 100, 5),
                     labels = percent_format(accuracy = 1)) +
  stat_smooth(aes(x = NX_mean, y = IND_mean), linetype = 2, 
              method = "lm", color = viridis(3)[2])

Manufacturing

SNA_TABLE1 %>%
  filter(MEASURE == "C",
         TRANSACT %in% c("B1_GE", "B1GVC", "B11", "P61", "P71")) %>%
  year_to_date %>%
  filter(date >= as.Date("2016-01-01"),
         date <= as.Date("2020-01-01")) %>%
  select(date, LOCATION, TRANSACT, obsValue) %>%
  spread(TRANSACT, obsValue) %>%
  mutate(IND = B1GVC / B1_GE,
         NX = (P61-P71) / B1_GE) %>%
  group_by(LOCATION) %>%
  summarise(NX_mean = mean(NX, na.rm = T),
            IND_mean = mean(IND, na.rm = T)) %>%
  left_join(SNA_TABLE1_var$LOCATION, by = "LOCATION") %>%
  ggplot(.) + theme_minimal() + geom_point(aes(x = NX_mean, y = IND_mean)) +
  xlab("Goods Trade Balance 2015-2020 (% of GDP)") + ylab("Manufacturing VA 2015-2020 (% of GDP)") +
  scale_x_continuous(breaks = 0.01*seq(-100, 100, 5),
                     labels = percent_format(accuracy = 1)) +
  geom_text_repel(aes(x = NX_mean, y = IND_mean, label = Location), vjust = 0) +
  scale_y_continuous(breaks = 0.01*seq(-100, 100, 5),
                     labels = percent_format(accuracy = 1)) +
  stat_smooth(aes(x = NX_mean, y = IND_mean), linetype = 2, 
              method = "lm", color = viridis(3)[2])

Manufacturing (without Ireland)

SNA_TABLE1 %>%
  filter(MEASURE == "C",
         TRANSACT %in% c("B1_GE", "B1GVC", "B11", "P61", "P71"),
         !(LOCATION == "IRL")) %>%
  year_to_date %>%
  filter(date >= as.Date("2016-01-01"),
         date <= as.Date("2020-01-01")) %>%
  select(date, LOCATION, TRANSACT, obsValue) %>%
  spread(TRANSACT, obsValue) %>%
  mutate(IND = B1GVC / B1_GE,
         NX = (P61-P71) / B1_GE) %>%
  group_by(LOCATION) %>%
  summarise(NX_mean = mean(NX, na.rm = T),
            IND_mean = mean(IND, na.rm = T)) %>%
  left_join(SNA_TABLE1_var$LOCATION, by = "LOCATION") %>%
  ggplot(.) + theme_minimal() + geom_point(aes(x = NX_mean, y = IND_mean)) +
  xlab("Goods Trade Balance 2015-2020 (% of GDP)") + 
  ylab("Manufacturing VA 2015-2020 (% of GDP)") +
  scale_x_continuous(breaks = 0.01*seq(-100, 100, 5),
                     labels = percent_format(accuracy = 1)) +
  geom_text_repel(aes(x = NX_mean, y = IND_mean, label = Location), vjust = 0) +
  scale_y_continuous(breaks = 0.01*seq(-100, 100, 5),
                     labels = percent_format(accuracy = 1)) +
  stat_smooth(aes(x = NX_mean, y = IND_mean), linetype = 2, 
              method = "lm", color = viridis(3)[2])

Manufacturing (without Ireland)

SNA_TABLE1 %>%
  filter(MEASURE == "C",
         TRANSACT %in% c("B1_GE", "B1GVC", "B11", "P61", "P71"),
         !(LOCATION == "IRL")) %>%
  year_to_date %>%
  filter(date >= as.Date("2002-01-01"),
         date <= as.Date("2007-01-01")) %>%
  select(date, LOCATION, TRANSACT, obsValue) %>%
  spread(TRANSACT, obsValue) %>%
  mutate(IND = B1GVC / B1_GE,
         NX = (P61-P71) / B1_GE) %>%
  group_by(LOCATION) %>%
  summarise(NX_mean = mean(NX, na.rm = T),
            IND_mean = mean(IND, na.rm = T)) %>%
  left_join(SNA_TABLE1_var$LOCATION, by = "LOCATION") %>%
  ggplot(.) + theme_minimal() + geom_point(aes(x = NX_mean, y = IND_mean)) +
  xlab("Goods Trade Balance 2002-2007 (% of GDP)") + 
  ylab("Manufacturing VA 2002-2007 (% of GDP)") +
  scale_x_continuous(breaks = 0.01*seq(-100, 100, 5),
                     labels = percent_format(accuracy = 1)) +
  geom_text_repel(aes(x = NX_mean, y = IND_mean, label = Location), vjust = 0) +
  scale_y_continuous(breaks = 0.01*seq(-100, 100, 5),
                     labels = percent_format(accuracy = 1)) +
  stat_smooth(aes(x = NX_mean, y = IND_mean), linetype = 2, 
              method = "lm", color = viridis(3)[2])

Industry: Change 1991-2018

SNA_TABLE1 %>%
  year_to_date %>%
  filter(MEASURE == "C",
         TRANSACT %in% c("B1_GE", "B1GVB_E", "B11", "P61", "P71")) %>%
  filter(date %in% (paste0(c(1995, 2018), "-01-01") %>% as.Date)) %>%
  select(date, LOCATION, TRANSACT, obsValue) %>%
  spread(TRANSACT, obsValue) %>%
  mutate(IND = B1GVB_E / B1_GE,
         NX = (P61-P71) / B1_GE) %>%
  group_by(LOCATION) %>%
  summarise(NX_mean = NX[2] - NX[1],
            IND_mean = IND[2] - IND[1]) %>%
  na.omit %>%
  left_join(SNA_TABLE1_var$LOCATION, by = "LOCATION") %>%
  ggplot(.) + theme_minimal() + geom_point(aes(x = NX_mean, y = IND_mean)) +
  xlab(expression(Delta~"Goods Trade Balance 1995-2018 (% of GDP)")) + 
  ylab(expression(Delta~"Industry VA 1995-2018 (% of GDP)")) +
  scale_x_continuous(breaks = 0.01*seq(-100, 100, 5),
                     labels = percent_format(accuracy = 1)) +
  geom_text_repel(aes(x = NX_mean, y = IND_mean, label = Location), vjust = 0) +
  scale_y_continuous(breaks = 0.01*seq(-100, 100, 5),
                     labels = percent_format(accuracy = 1)) +
  stat_smooth(aes(x = NX_mean, y = IND_mean), linetype = 2, 
              method = "lm", color = viridis(3)[2])

B1_GE - Gross domestic product

“P” Decomposition

SNA_TABLE1 %>%
  filter(LOCATION %in% c("FRA", "DEU", "USA", "GBR"),
         obsTime == "2018",
         MEASURE == "C",
         grepl("^P", TRANSACT) | TRANSACT == "B1_GE") %>%
  select(LOCATION, TRANSACT, obsValue) %>%
  left_join(SNA_TABLE1_var$TRANSACT, by = "TRANSACT") %>%
  spread(LOCATION, obsValue) %>%
  mutate_at(vars(-1, -2), funs(ifelse(is.na(.), "", paste0(round(100*./.[TRANSACT == "B1_GE"], 1), " %")))) %>%
  {if (is_html_output()) datatable(., filter = 'top', rownames = F) else .}

“B” Decomposition

SNA_TABLE1 %>%
  filter(LOCATION %in% c("FRA", "DEU", "USA", "GBR"),
         obsTime == "2018",
         MEASURE == "C",
         grepl("^B", TRANSACT) | TRANSACT == "B1_GE") %>%
  select(LOCATION, TRANSACT, obsValue) %>%
  left_join(SNA_TABLE1_var$TRANSACT, by = "TRANSACT") %>%
  spread(LOCATION, obsValue) %>%
  mutate_at(vars(-1, -2), funs(ifelse(is.na(.), "", paste0(round(100*./.[TRANSACT == "B1_GE"], 1), " %")))) %>%
  {if (is_html_output()) datatable(., filter = 'top', rownames = F) else .}

“D” Decomposition

SNA_TABLE1 %>%
  filter(LOCATION %in% c("FRA", "DEU", "USA", "GBR"),
         obsTime == "2018",
         MEASURE == "C",
         grepl("^D", TRANSACT) | TRANSACT == "B1_GE") %>%
  select(LOCATION, TRANSACT, obsValue) %>%
  left_join(SNA_TABLE1_var$TRANSACT, by = "TRANSACT") %>%
  spread(LOCATION, obsValue) %>%
  mutate_at(vars(-1, -2), funs(ifelse(is.na(.), "", paste0(round(100*./.[TRANSACT == "B1_GE"], 1), " %")))) %>%
  {if (is_html_output()) datatable(., filter = 'top', rownames = F) else .}

All Decomposition

SNA_TABLE1 %>%
  filter(LOCATION %in% c("FRA", "DEU", "USA", "GBR"),
         obsTime == "2018",
         MEASURE == "C") %>%
  select(LOCATION, TRANSACT, obsValue) %>%
  left_join(SNA_TABLE1_var$TRANSACT, by = "TRANSACT") %>%
  spread(LOCATION, obsValue) %>%
  mutate_at(vars(-1, -2), funs(ifelse(is.na(.), "", paste0(round(100*./.[TRANSACT == "B1_GE"], 1), " %")))) %>%
  {if (is_html_output()) datatable(., filter = 'top', rownames = F) else .}

How much data

SNA_TABLE1 %>%
  filter(TRANSACT == "B1_GE",
         MEASURE == "C") %>%
  left_join(SNA_TABLE1_var$LOCATION, by = "LOCATION") %>%
  group_by(LOCATION, UNIT, Location) %>%
  summarise(year_first = first(obsTime),
            year_last = last(obsTime),
            value_last = last(round(obsValue))) %>%
  mutate(Flag = gsub(" ", "-", str_to_lower(gsub(" ", "-", Location))),
         Flag = paste0('<img src="../../icon/flag/vsmall/', Flag, '.png" alt="Flag">')) %>%
  select(Flag, everything()) %>%
  {if (is_html_output()) datatable(., filter = 'top', rownames = F, escape = F) else .}

Compare US and France

SNA_TABLE1 %>%
  filter(LOCATION %in% c("FRA", "USA"),
         obsTime == "2018",
         MEASURE == "C",
         grepl("^P", TRANSACT) | TRANSACT == "B1_GE",
         !grepl("^P51N", TRANSACT)) %>%
  select(LOCATION, TRANSACT, obsValue) %>%
  left_join(SNA_TABLE1_var$TRANSACT, by = "TRANSACT") %>%
  spread(LOCATION, obsValue) %>%
  mutate_at(vars(-1, -2), funs(paste0(round(100*./.[TRANSACT == "B1_GE"], 1), " %"))) %>%
  {if (is_html_output()) print_table(.) else .}

TRANSACT	Transact	FRA	USA
B1_GE	Gross domestic product (expenditure approach)	100 %	100 %
P3	Final consumption expenditure	77.2 %	81.9 %
P3_P5	Domestic demand	101 %	103 %
P31S13	Individual consumption expenditure of general government	15.2 %	6 %
P31S14	Final consumption expenditure of households	51.8 %	65.8 %
P31S14_S15	Households and Non-profit institutions serving households	53.9 %	67.9 %
P31S15	Final consumption expenditure of non-profit institutions serving households	2.1 %	2.1 %
P32S13	Collective consumption expenditure of general government	8.1 %	8 %
P3S13	Final consumption expenditure of general government	23.3 %	14 %
P41	of which: Actual individual consumption	69.1 %	73.9 %
P5	Gross capital formation	23.9 %	21 %
P51	Gross fixed capital formation	22.9 %	20.8 %
P52	Changes in inventories	0.9 %	0.3 %
P52_P53	Changes in inventories and acquisitions less disposals of valuables	1 %	0.3 %
P53	Acquisitions less disposals of valuables	0 %	NA %
P6	Exports of goods and services	31.7 %	12.3 %
P61	Exports of goods	22.1 %	8.1 %
P62	Exports of services	9.6 %	4.2 %
P7	Imports of goods and services	32.8 %	15.2 %
P71	Imports of goods	23.7 %	12.4 %
P72	Imports of services	9.1 %	2.8 %

France

Net Exports - Total, Goods, Services

SNA_TABLE1 %>%
  filter(LOCATION == "FRA",
         MEASURE == "C",
         TRANSACT %in% c("B1_GE", "P61", "P71", "P62", "P72", "P6", "P7")) %>%
  left_join(SNA_TABLE1_var$TRANSACT, by = "TRANSACT") %>%
  left_join(SNA_TABLE1_var$LOCATION, by = "LOCATION") %>%
  year_to_date %>%
  group_by(date) %>%
  mutate(obsValue = obsValue / obsValue[TRANSACT == "B1_GE"]) %>%
  filter(TRANSACT != "B1_GE") %>%
  select(date, TRANSACT, obsValue) %>%
  na.omit %>%
  spread(TRANSACT, obsValue) %>%
  transmute(date,
            `Tous` = P6 - P7,
            `Biens` = P61 - P71,
            `Services` = P62 - P72) %>%
  gather(variable, value, -date) %>%
  ggplot() + geom_line(aes(x = date, y = value, color = variable)) +
  scale_color_manual(values = c("brown", "orange", "black")) + theme_minimal() +
  scale_x_date(breaks = seq(1920, 2025, 5) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  theme(legend.position = c(0.85, 0.9),
        legend.title = element_blank()) +
  scale_y_continuous(breaks = 0.01*seq(-60, 60, 1),
                     labels = scales::percent_format(accuracy = 1)) +
  ylab("Exportations Nettes (% du PIB)") + xlab("") +
  geom_hline(yintercept = 0, linetype = "dotted")

Exports, Imports - Goods, Services

SNA_TABLE1 %>%
  filter(LOCATION == "FRA",
         MEASURE == "C",
         TRANSACT %in% c("B1_GE", "P61", "P71", "P62", "P72")) %>%
  left_join(SNA_TABLE1_var$TRANSACT, by = "TRANSACT") %>%
  year_to_date %>%
  group_by(date) %>%
  mutate(obsValue = obsValue / obsValue[TRANSACT == "B1_GE"]) %>%
  filter(TRANSACT != "B1_GE") %>%
  ggplot() + geom_line() + theme_minimal() +
  aes(x = date, y = obsValue, color = Transact, linetype = Transact) +
  scale_color_manual(values = c(viridis(3)[1], viridis(3)[2], viridis(3)[1], viridis(3)[2])) +
  scale_linetype_manual(values = c("solid", "solid", "dashed", "dashed")) +
  ylab("% of GDP") + xlab("") +
  scale_x_date(breaks = seq(1920, 2025, 5) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  theme(legend.position = c(0.15, 0.9),
        legend.title = element_blank()) +
  scale_y_continuous(breaks = 0.01*seq(-60, 60, 1),
                     labels = scales::percent_format(accuracy = 1))

Manufacturing (Bn€)

France, Germany, Italy

SNA_TABLE1 %>%
  filter(LOCATION %in% c("FRA", "ITA", "DEU"),
         MEASURE == "V",
         TRANSACT %in% c("B1GVC")) %>%
  year_to_date %>%
  filter(date >= as.Date("1980-01-01")) %>%
  left_join(SNA_TABLE1_var$LOCATION, by = "LOCATION") %>%
  select(date, Location, TRANSACT, obsValue) %>%
  spread(TRANSACT, obsValue) %>%
  mutate(obsValue = B1GVC/1000) %>%
  left_join(colors, by = c("Location" = "country")) %>%
  ggplot() + geom_line(aes(x = date, y = obsValue, color = color)) +
  scale_color_identity() + theme_minimal() + add_3flags +
  scale_x_date(breaks = seq(1920, 2025, 5) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  scale_y_continuous(breaks = seq(0, 4000, 100),
                     labels = scales::dollar_format(accuracy = 1, su = "Bn€", p = "")) +
  ylab("Manufacturing Value Added (Bn)") + xlab("")

Manufacturing, Industry, Industry without Manufacturing (% of GDP)

Australia

SNA_TABLE1 %>%
  filter(LOCATION %in% c("AUS"),
         MEASURE == "C",
         TRANSACT %in% c("B1_GE", "B1GVC", "B1GVB_E")) %>%
  year_to_date %>%
  select(date, TRANSACT, obsValue) %>%
  spread(TRANSACT, obsValue) %>%
  transmute(date,
            `Manufacturing` = B1GVC / B1_GE,
            `Industry` = B1GVB_E / B1_GE,
            `Industry excl. Manufacturing` = (B1GVB_E - B1GVC) / B1_GE) %>%
  gather(Transact, value, -date) %>%
  na.omit %>%
  ggplot() + geom_line() + theme_minimal() +
  aes(x = date, y = value, color = Transact, linetype = Transact) +
  scale_color_manual(values = viridis(4)[1:3]) +  ylab("% of GDP") + xlab("") +
  scale_x_date(breaks = seq(1920, 2025, 5) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  theme(legend.position = c(0.2, 0.7),
        legend.title = element_blank()) +
  scale_y_continuous(breaks = 0.01*seq(-60, 60, 1),
                     labels = scales::percent_format(accuracy = 1))

France

SNA_TABLE1 %>%
  filter(LOCATION %in% c("FRA"),
         MEASURE == "C",
         TRANSACT %in% c("B1_GE", "B1GVC", "B1GVB_E")) %>%
  year_to_date %>%
  select(date, TRANSACT, obsValue) %>%
  spread(TRANSACT, obsValue) %>%
  transmute(date,
            `Manufacturing` = B1GVC / B1_GE,
            `Industry` = B1GVB_E / B1_GE,
            `Industry excl. Manufacturing` = (B1GVB_E - B1GVC) / B1_GE) %>%
  gather(Transact, value, -date) %>%
  na.omit %>%
  ggplot() + geom_line() + theme_minimal() +
  aes(x = date, y = value, color = Transact, linetype = Transact) +
  scale_color_manual(values = viridis(4)[1:3]) +  ylab("% of GDP") + xlab("") +
  scale_x_date(breaks = seq(1920, 2025, 5) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  theme(legend.position = c(0.2, 0.7),
        legend.title = element_blank()) +
  scale_y_continuous(breaks = 0.01*seq(-60, 60, 1),
                     labels = scales::percent_format(accuracy = 1))

Germany

SNA_TABLE1 %>%
  filter(LOCATION %in% c("DEU"),
         MEASURE == "C",
         TRANSACT %in% c("B1_GE", "B1GVC", "B1GVB_E")) %>%
  year_to_date %>%
  select(date, TRANSACT, obsValue) %>%
  spread(TRANSACT, obsValue) %>%
  transmute(date,
            `Manufacturing` = B1GVC / B1_GE,
            `Industry` = B1GVB_E / B1_GE,
            `Industry excl. Manufacturing` = (B1GVB_E - B1GVC) / B1_GE) %>%
  gather(Transact, value, -date) %>%
  na.omit %>%
  ggplot() + geom_line() + theme_minimal() +
  aes(x = date, y = value, color = Transact, linetype = Transact) +
  scale_color_manual(values = viridis(4)[1:3]) +  ylab("% of GDP") + xlab("") +
  scale_x_date(breaks = seq(1920, 2025, 5) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  theme(legend.position = c(0.2, 0.7),
        legend.title = element_blank()) +
  scale_y_continuous(breaks = 0.01*seq(-60, 60, 1),
                     labels = scales::percent_format(accuracy = 1))

Netherlands

SNA_TABLE1 %>%
  filter(LOCATION %in% c("NLD"),
         MEASURE == "C",
         TRANSACT %in% c("B1_GE", "B1GVC", "B1GVB_E")) %>%
  year_to_date %>%
  select(date, TRANSACT, obsValue) %>%
  spread(TRANSACT, obsValue) %>%
  transmute(date,
            `Manufacturing` = B1GVC / B1_GE,
            `Industry` = B1GVB_E / B1_GE,
            `Industry excl. Manufacturing` = (B1GVB_E - B1GVC) / B1_GE) %>%
  gather(Transact, value, -date) %>%
  na.omit %>%
  ggplot() + geom_line() + theme_minimal() +
  aes(x = date, y = value, color = Transact, linetype = Transact) +
  scale_color_manual(values = viridis(4)[1:3]) +  ylab("% of GDP") + xlab("") +
  scale_x_date(breaks = seq(1920, 2025, 5) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  theme(legend.position = c(0.2, 0.7),
        legend.title = element_blank()) +
  scale_y_continuous(breaks = 0.01*seq(-60, 60, 1),
                     labels = scales::percent_format(accuracy = 1))

NOR

SNA_TABLE1 %>%
  filter(LOCATION %in% c("NOR"),
         MEASURE == "C",
         TRANSACT %in% c("B1_GE", "B1GVC", "B1GVB_E")) %>%
  year_to_date %>%
  select(date, TRANSACT, obsValue) %>%
  spread(TRANSACT, obsValue) %>%
  transmute(date,
            `Manufacturing` = B1GVC / B1_GE,
            `Industry` = B1GVB_E / B1_GE,
            `Industry excl. Manufacturing` = (B1GVB_E - B1GVC) / B1_GE) %>%
  gather(Transact, value, -date) %>%
  na.omit %>%
  ggplot() + geom_line() + theme_minimal() +
  aes(x = date, y = value, color = Transact, linetype = Transact) +
  scale_color_manual(values = viridis(4)[1:3]) +  ylab("% of GDP") + xlab("") +
  scale_x_date(breaks = seq(1920, 2025, 5) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  theme(legend.position = c(0.2, 0.7),
        legend.title = element_blank()) +
  scale_y_continuous(breaks = 0.01*seq(-60, 60, 1),
                     labels = scales::percent_format(accuracy = 1))

Manufacturing, Industry (% of GDP)

Australia

SNA_TABLE1 %>%
  filter(LOCATION %in% c("AUS"),
         MEASURE == "C",
         TRANSACT %in% c("B1_GE", "B1GVC", "B1GVB_E")) %>%
  year_to_date %>%
  select(date, TRANSACT, obsValue) %>%
  spread(TRANSACT, obsValue) %>%
  transmute(date,
            `Manufacturing` = B1GVC / B1_GE,
            `Industry` = B1GVB_E / B1_GE) %>%
  gather(Transact, value, -date) %>%
  na.omit %>%
  ggplot() + geom_line() + theme_minimal() +
  aes(x = date, y = value, color = Transact, linetype = Transact) +
  scale_color_manual(values = viridis(3)[1:2]) +  ylab("% of GDP") + xlab("") +
  scale_x_date(breaks = seq(1920, 2025, 5) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  theme(legend.position = c(0.2, 0.2),
        legend.title = element_blank()) +
  scale_y_continuous(breaks = 0.01*seq(-60, 60, 1),
                     labels = scales::percent_format(accuracy = 1))

France

SNA_TABLE1 %>%
  filter(LOCATION %in% c("FRA"),
         MEASURE == "C",
         TRANSACT %in% c("B1_GE", "B1GVC", "B1GVB_E")) %>%
  year_to_date %>%
  select(date, TRANSACT, obsValue) %>%
  spread(TRANSACT, obsValue) %>%
  transmute(date,
            `Manufacturing` = B1GVC / B1_GE,
            `Industry` = B1GVB_E / B1_GE) %>%
  gather(Transact, value, -date) %>%
  na.omit %>%
  ggplot() + geom_line() + theme_minimal() +
  aes(x = date, y = value, color = Transact, linetype = Transact) +
  scale_color_manual(values = viridis(3)[1:2]) +  ylab("% of GDP") + xlab("") +
  scale_x_date(breaks = seq(1920, 2025, 5) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  theme(legend.position = c(0.8, 0.8),
        legend.title = element_blank()) +
  scale_y_continuous(breaks = 0.01*seq(-60, 60, 1),
                     labels = scales::percent_format(accuracy = 1))

Germany

SNA_TABLE1 %>%
  filter(LOCATION %in% c("DEU"),
         MEASURE == "C",
         TRANSACT %in% c("B1_GE", "B1GVC", "B1GVB_E")) %>%
  year_to_date %>%
  select(date, TRANSACT, obsValue) %>%
  spread(TRANSACT, obsValue) %>%
  transmute(date,
            `Manufacturing` = B1GVC / B1_GE,
            `Industry` = B1GVB_E / B1_GE) %>%
  gather(Transact, value, -date) %>%
  na.omit %>%
  ggplot() + geom_line() + theme_minimal() +
  aes(x = date, y = value, color = Transact, linetype = Transact) +
  scale_color_manual(values = viridis(3)[1:2]) +  ylab("% of GDP") + xlab("") +
  scale_x_date(breaks = seq(1920, 2025, 5) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  theme(legend.position = c(0.8, 0.8),
        legend.title = element_blank()) +
  scale_y_continuous(breaks = 0.01*seq(-60, 60, 1),
                     labels = scales::percent_format(accuracy = 1))

Greece

SNA_TABLE1 %>%
  filter(LOCATION %in% c("GRC"),
         MEASURE == "C",
         TRANSACT %in% c("B1_GE", "B1GVC", "B1GVB_E")) %>%
  year_to_date %>%
  select(date, TRANSACT, obsValue) %>%
  spread(TRANSACT, obsValue) %>%
  transmute(date,
            `Manufacturing` = B1GVC / B1_GE,
            `Industry` = B1GVB_E / B1_GE) %>%
  gather(Transact, value, -date) %>%
  na.omit %>%
  ggplot() + geom_line() + theme_minimal() +
  aes(x = date, y = value, color = Transact, linetype = Transact) +
  scale_color_manual(values = viridis(3)[1:2]) +  ylab("% of GDP") + xlab("") +
  scale_x_date(breaks = seq(1920, 2025, 5) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  theme(legend.position = c(0.8, 0.8),
        legend.title = element_blank()) +
  scale_y_continuous(breaks = 0.01*seq(-60, 60, 1),
                     labels = scales::percent_format(accuracy = 1))

# Industry (% of GDP)

Number of observations

SNA_TABLE1 %>%
  filter(MEASURE == "C",
         TRANSACT %in% c("B1GVB_E", "B1_GE")) %>%
  year_to_date %>%
  left_join(SNA_TABLE1_var$LOCATION, by = "LOCATION") %>%
  group_by(LOCATION, Location) %>%
  select(date, Location, LOCATION, TRANSACT, obsValue) %>%
  spread(TRANSACT, obsValue) %>%
  mutate(obsValue = B1GVB_E / B1_GE) %>%
  summarise(first = first(date),
            last = last(date),
            last_value = last(obsValue),
            Nobs = n()) %>%
  arrange(last_value) %>%
  mutate(Flag = gsub(" ", "-", str_to_lower(gsub(" ", "-", Location))),
         Flag = paste0('<img src="../../icon/flag/vsmall/', Flag, '.png" alt="Flag">')) %>%
  select(Flag, everything()) %>%
  {if (is_html_output()) datatable(., filter = 'top', rownames = F, escape = F) else .}

Spain, France, Germany, Italy, Greece

1980-

SNA_TABLE1 %>%
  filter(LOCATION %in% c("FRA", "ITA", "DEU", "ESP", "GRC", "EA19"),
         MEASURE == "C",
         TRANSACT %in% c("B1_GE", "B1GVB_E")) %>%
  year_to_date %>%
  filter(date >= as.Date("1980-01-01")) %>%
  left_join(SNA_TABLE1_var$LOCATION, by = "LOCATION") %>%
  select(date, Location, LOCATION, TRANSACT, obsValue) %>%
  spread(TRANSACT, obsValue) %>%
  mutate(obsValue = B1GVB_E / B1_GE) %>%
  mutate(Location = ifelse(LOCATION == "EA19", "Europe", Location)) %>%
  left_join(colors, by = c("Location" = "country")) %>%
  ggplot() + geom_line(aes(x = date, y = obsValue, color = color)) + 
  ylab("Industry Value Added (% of GDP)") + xlab("") +
  scale_color_identity() + theme_minimal() + add_6flags +
  scale_x_date(breaks = seq(1920, 2025, 5) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  scale_y_continuous(breaks = 0.01*seq(-60, 60, 1),
                     labels = scales::percent_format(accuracy = 1))

1995-

SNA_TABLE1 %>%
  filter(LOCATION %in% c("FRA", "ITA", "DEU", "ESP", "GRC", "EA19", "USA"),
         MEASURE == "C",
         TRANSACT %in% c("B1_GE", "B1GVB_E")) %>%
  year_to_date %>%
  filter(date >= as.Date("1995-01-01")) %>%
  left_join(SNA_TABLE1_var$LOCATION, by = "LOCATION") %>%
  select(date, Location, LOCATION, TRANSACT, obsValue) %>%
  spread(TRANSACT, obsValue) %>%
  mutate(obsValue = B1GVB_E / B1_GE) %>%
  mutate(Location = ifelse(LOCATION == "EA19", "Europe", Location)) %>%
  left_join(colors, by = c("Location" = "country")) %>%
  mutate(color = ifelse(LOCATION == "USA", color2, color)) %>%
  ggplot() + geom_line(aes(x = date, y = obsValue, color = color)) + 
  ylab("Valeur ajoutée dans l'industrie (% du PIB)") + xlab("") +
  scale_color_identity() + theme_minimal() + add_7flags +
  scale_x_date(breaks = seq(1995, 2025, 2) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  scale_y_continuous(breaks = 0.01*seq(-60, 60, 1),
                     labels = scales::percent_format(accuracy = 1))

1995-

SNA_TABLE1 %>%
  filter(LOCATION %in% c("FRA", "ITA", "DEU", "ESP", "GRC", "EA19", "USA", "GBR"),
         MEASURE == "C",
         TRANSACT %in% c("B1_GE", "B1GVB_E")) %>%
  year_to_date %>%
  filter(date >= as.Date("1995-01-01")) %>%
  left_join(SNA_TABLE1_var$LOCATION, by = "LOCATION") %>%
  select(date, Location, LOCATION, TRANSACT, obsValue) %>%
  spread(TRANSACT, obsValue) %>%
  mutate(obsValue = B1GVB_E / B1_GE) %>%
  mutate(Location = ifelse(LOCATION == "EA19", "Europe", Location)) %>%
  left_join(colors, by = c("Location" = "country")) %>%
  mutate(color = ifelse(LOCATION == "USA", color2, color)) %>%
  ggplot() + geom_line(aes(x = date, y = obsValue, color = color)) + 
  ylab("Valeur ajoutée dans l'industrie (% du PIB)") + xlab("") +
  scale_color_identity() + theme_minimal() + add_8flags +
  scale_x_date(breaks = seq(1995, 2025, 2) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  scale_y_continuous(breaks = 0.01*seq(-60, 60, 1),
                     labels = scales::percent_format(accuracy = 1))

1995-

% of GDP

SNA_TABLE1 %>%
  filter(LOCATION %in% c("FRA", "USA", "GBR", "EA19"),
         MEASURE == "C",
         TRANSACT %in% c("B1_GE", "B1GVB_E")) %>%
  year_to_date %>%
  filter(date >= as.Date("1995-01-01")) %>%
  left_join(SNA_TABLE1_var$LOCATION, by = "LOCATION") %>%
  select(date, Location, LOCATION, TRANSACT, obsValue) %>%
  spread(TRANSACT, obsValue) %>%
  mutate(obsValue = B1GVB_E / B1_GE) %>%
  mutate(Location = ifelse(LOCATION == "EA19", "Europe", Location)) %>%
  left_join(colors, by = c("Location" = "country")) %>%
  mutate(color = ifelse(LOCATION == "FRA", color2, color),
         color = ifelse(LOCATION == "EA19", color2, color)) %>%
  ggplot() + geom_line(aes(x = date, y = obsValue, color = color)) + 
  ylab("Valeur ajoutée dans l'industrie (% du PIB)") + xlab("") +
  scale_color_identity() + theme_minimal() + add_4flags +
  scale_x_date(breaks = seq(1995, 2025, 2) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  scale_y_continuous(breaks = 0.01*seq(-60, 60, 1),
                     labels = scales::percent_format(accuracy = 1))

Valeur Ajoutée

SNA_TABLE1 %>%
  filter(LOCATION %in% c("FRA", "USA", "GBR", "EA19"),
         MEASURE == "C",
         TRANSACT %in% c("B1GVB_E")) %>%
  year_to_date %>%
  filter(date >= as.Date("1997-01-01")) %>%
  left_join(SNA_TABLE1_var$LOCATION, by = "LOCATION") %>%
  select(date, Location, LOCATION, TRANSACT, obsValue) %>%
  group_by(Location) %>%
  arrange(date) %>%
  mutate(obsValue = 100*obsValue/obsValue[date == as.Date("1997-01-01")]) %>%
  mutate(Location = ifelse(LOCATION == "EA19", "Europe", Location)) %>%
  left_join(colors, by = c("Location" = "country")) %>%
  mutate(color = ifelse(LOCATION == "FRA", color2, color),
         color = ifelse(LOCATION == "EA19", color2, color)) %>%
  ggplot() + geom_line(aes(x = date, y = obsValue, color = color)) + 
  ylab("Valeur ajoutée dans l'industrie (Indice)") + xlab("") +
  scale_color_identity() + theme_minimal() + add_4flags +
  scale_x_date(breaks = seq(1995, 2025, 2) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  scale_y_continuous(breaks = seq(100, 400, 10))

1995-

SNA_TABLE1 %>%
  filter(LOCATION %in% c("FRA", "DEU", "EA19", "USA", "GBR"),
         MEASURE == "C",
         TRANSACT %in% c("B1_GE", "B1GVB_E")) %>%
  year_to_date %>%
  filter(date >= as.Date("1995-01-01")) %>%
  left_join(SNA_TABLE1_var$LOCATION, by = "LOCATION") %>%
  select(date, Location, LOCATION, TRANSACT, obsValue) %>%
  spread(TRANSACT, obsValue) %>%
  mutate(obsValue = B1GVB_E / B1_GE) %>%
  mutate(Location = ifelse(LOCATION == "EA19", "Europe", Location)) %>%
  left_join(colors, by = c("Location" = "country")) %>%
  mutate(color = ifelse(LOCATION == "USA", color2, color),
         color = ifelse(LOCATION == "FRA", color2, color)) %>%
  ggplot() + geom_line(aes(x = date, y = obsValue, color = color)) + 
  ylab("Valeur ajoutée dans l'industrie (% du PIB)") + xlab("") +
  scale_color_identity() + theme_minimal() + add_5flags +
  scale_x_date(breaks = seq(1995, 2025, 2) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  scale_y_continuous(breaks = 0.01*seq(-60, 60, 1),
                     labels = scales::percent_format(accuracy = 1))

1995-

SNA_TABLE1 %>%
  filter(LOCATION %in% c("FRA", "ITA", "DEU", "ESP", "GRC", "EA19", "USA"),
         MEASURE == "C",
         TRANSACT %in% c("B1_GE", "B1GVC")) %>%
  year_to_date %>%
  filter(date >= as.Date("1995-01-01")) %>%
  left_join(SNA_TABLE1_var$LOCATION, by = "LOCATION") %>%
  select(date, Location, LOCATION, TRANSACT, obsValue) %>%
  spread(TRANSACT, obsValue) %>%
  mutate(obsValue = B1GVC / B1_GE) %>%
  mutate(Location = ifelse(LOCATION == "EA19", "Europe", Location)) %>%
  left_join(colors, by = c("Location" = "country")) %>%
  mutate(color = ifelse(LOCATION == "USA", color2, color)) %>%
  ggplot() + geom_line(aes(x = date, y = obsValue, color = color)) + 
  ylab("Valeur ajoutée dans l'industrie manufacturière (% du PIB)") + xlab("") +
  scale_color_identity() + theme_minimal() + add_7flags +
  scale_x_date(breaks = seq(1995, 2025, 2) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  scale_y_continuous(breaks = 0.01*seq(-60, 60, 1),
                     labels = scales::percent_format(accuracy = 1))

Spain, France, Germany, Italy, Greece

SNA_TABLE1 %>%
  filter(LOCATION %in% c("FRA", "GRC", "EA19"),
         MEASURE == "C",
         TRANSACT %in% c("B1_GE", "B1GVB_E")) %>%
  year_to_date %>%
  left_join(SNA_TABLE1_var$LOCATION, by = "LOCATION") %>%
  select(date, Location, LOCATION, TRANSACT, obsValue) %>%
  spread(TRANSACT, obsValue) %>%
  mutate(obsValue = B1GVB_E / B1_GE) %>%
  mutate(Location = ifelse(LOCATION == "EA19", "Europe", Location)) %>%
  left_join(colors, by = c("Location" = "country")) %>%
  ggplot() + geom_line(aes(x = date, y = obsValue, color = color)) + 
  ylab("Industry Value Added (% of GDP)") + xlab("") +
  scale_color_identity() + theme_minimal() + add_3flags +
  geom_rect(data = data_frame(start = as.Date("1945-01-01"), 
                                end = as.Date("1975-01-01")), 
              aes(xmin = start, xmax = end, ymin = -Inf, ymax = +Inf), 
              fill = viridis(4)[4], alpha = 0.2) +
  scale_x_date(breaks = seq(1920, 2025, 5) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  scale_y_continuous(breaks = 0.01*seq(-60, 60, 1),
                     labels = scales::percent_format(accuracy = 1))

Manufacturing (% of GDP)

Number of observations

SNA_TABLE1 %>%
  filter(MEASURE == "C",
         TRANSACT %in% c("B1GVC")) %>%
  year_to_date %>%
  left_join(SNA_TABLE1_var$LOCATION, by = "LOCATION") %>%
  group_by(LOCATION, Location) %>%
  summarise(first = first(date),
            last = last(date),
            Nobs = n()) %>%
  arrange(first) %>%
  mutate(Flag = gsub(" ", "-", str_to_lower(gsub(" ", "-", Location))),
         Flag = paste0('<img src="../../icon/flag/vsmall/', Flag, '.png" alt="Flag">')) %>%
  select(Flag, everything()) %>%
  {if (is_html_output()) datatable(., filter = 'top', rownames = F, escape = F) else .}

B1GVC - Manufacturing Value Added

United States, United Kingdom, France

All-

SNA_TABLE1 %>%
  filter(LOCATION %in% c("FRA", "USA", "GBR"),
         MEASURE == "C",
         TRANSACT %in% c("B1_GE", "B1GVC")) %>%
  year_to_date %>%
  left_join(SNA_TABLE1_var$LOCATION, by = "LOCATION") %>%
  select(date, Location, TRANSACT, obsValue) %>%
  spread(TRANSACT, obsValue) %>%
  mutate(obsValue = B1GVC / B1_GE) %>%
  left_join(colors, by = c("Location" = "country")) %>%
  ggplot() + geom_line(aes(x = date, y = obsValue, color = color)) + 
  ylab("Manufacturing Value Added (% of GDP)") + xlab("") +
  scale_color_identity() + theme_minimal() + add_3flags +
  scale_x_date(breaks = seq(1920, 2025, 5) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  scale_y_continuous(breaks = 0.01*seq(-60, 60, 1),
                     labels = scales::percent_format(accuracy = 1))

1995-

SNA_TABLE1 %>%
  filter(LOCATION %in% c("FRA", "USA", "GBR"),
         MEASURE == "C",
         TRANSACT %in% c("B1_GE", "B1GVC")) %>%
  year_to_date %>%
  filter(date >= as.Date("1995-01-01")) %>%
  left_join(SNA_TABLE1_var$LOCATION, by = "LOCATION") %>%
  select(date, Location, TRANSACT, obsValue) %>%
  spread(TRANSACT, obsValue) %>%
  mutate(obsValue = B1GVC / B1_GE) %>%
  left_join(colors, by = c("Location" = "country")) %>%
  ggplot() + geom_line(aes(x = date, y = obsValue, color = color)) + 
  ylab("Manufacturing Value Added (% of GDP)") + xlab("") +
  scale_color_identity() + theme_minimal() + add_3flags +
  scale_x_date(breaks = seq(1920, 2025, 5) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  scale_y_continuous(breaks = 0.01*seq(-60, 60, 1),
                     labels = scales::percent_format(accuracy = 1))

Korea, Singapore

SNA_TABLE1 %>%
  filter(LOCATION %in% c("KOR", "SGP", "DNK"),
         MEASURE == "C",
         TRANSACT %in% c("B1_GE", "B1GVC")) %>%
  year_to_date %>%
  #filter(date >= as.Date("1995-01-01")) %>%
  left_join(SNA_TABLE1_var$LOCATION, by = "LOCATION") %>%
  select(date, Location, TRANSACT, obsValue) %>%
  spread(TRANSACT, obsValue) %>%
  mutate(obsValue = B1GVC / B1_GE) %>%
  left_join(colors, by = c("Location" = "country")) %>%
  ggplot() + geom_line(aes(x = date, y = obsValue, color = color)) + 
  ylab("Manufacturing Value Added (% of GDP)") + xlab("") +
  scale_color_identity() + theme_minimal() + add_3flags +
  scale_x_date(breaks = seq(1920, 2025, 5) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  scale_y_continuous(breaks = 0.01*seq(-60, 60, 1),
                     labels = scales::percent_format(accuracy = 1))

Australia, Eurozone, Japan

%

SNA_TABLE1 %>%
  filter(LOCATION %in% c("AUS", "JPN", "EA19"),
         MEASURE == "C",
         TRANSACT %in% c("B1_GE", "B1GVC")) %>%
  year_to_date %>%
  filter(date >= as.Date("1980-01-01")) %>%
  left_join(SNA_TABLE1_var$LOCATION, by = "LOCATION") %>%
  select(date, Location, LOCATION, TRANSACT, obsValue) %>%
  spread(TRANSACT, obsValue) %>%
  mutate(obsValue = B1GVC / B1_GE) %>%
  mutate(Location = ifelse(LOCATION == "EA19", "Europe", Location)) %>%
  left_join(colors, by = c("Location" = "country")) %>%
  ggplot() + geom_line(aes(x = date, y = obsValue, color = color)) + 
  ylab("Manufacturing Value Added (% of GDP)") + xlab("") +
  scale_color_identity() + theme_minimal() + add_3flags +
  scale_x_date(breaks = seq(1920, 2025, 5) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  scale_y_continuous(breaks = 0.01*seq(-60, 60, 1),
                     labels = scales::percent_format(accuracy = 1))

Log

SNA_TABLE1 %>%
  filter(LOCATION %in% c("AUS", "JPN", "EA19"),
         MEASURE == "C",
         TRANSACT %in% c("B1_GE", "B1GVC")) %>%
  year_to_date %>%
  filter(date >= as.Date("1980-01-01")) %>%
  left_join(SNA_TABLE1_var$LOCATION, by = "LOCATION") %>%
  select(date, Location, LOCATION, TRANSACT, obsValue) %>%
  spread(TRANSACT, obsValue) %>%
  mutate(obsValue = B1GVC / B1_GE) %>%
  mutate(Location = ifelse(LOCATION == "EA19", "Europe", Location)) %>%
  left_join(colors, by = c("Location" = "country")) %>%
  ggplot() + geom_line(aes(x = date, y = obsValue, color = color)) + 
  ylab("Manufacturing Value Added (% of GDP)") + xlab("") +
  scale_color_identity() + theme_minimal() + add_3flags +
  scale_x_date(breaks = seq(1920, 2025, 5) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  scale_y_log10(breaks = 0.01*seq(-60, 60, 1),
                     labels = scales::percent_format(accuracy = 1))

France, Germany, Italy

Viridis

SNA_TABLE1 %>%
  filter(LOCATION %in% c("FRA", "ITA", "DEU"),
         MEASURE == "C",
         TRANSACT %in% c("B1_GE", "B1GVC")) %>%
  year_to_date %>%
  filter(date >= as.Date("1980-01-01")) %>%
  left_join(SNA_TABLE1_var$LOCATION, by = "LOCATION") %>%
  select(date, Location, TRANSACT, obsValue) %>%
  spread(TRANSACT, obsValue) %>%
  mutate(B1GVC_B1_GE = B1GVC / B1_GE) %>%
  ggplot() + geom_line() +
  aes(x = date, y = B1GVC_B1_GE, color = Location) +
  scale_color_manual(values = viridis(4)[1:3]) +
  theme_minimal() +
  scale_x_date(breaks = seq(1920, 2025, 5) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  theme(legend.position = c(0.15, 0.2),
        legend.title = element_blank()) +
  scale_y_continuous(breaks = 0.01*seq(-60, 60, 1),
                     labels = scales::percent_format(accuracy = 1)) +
  ylab("Manufacturing Value Added (% of GDP)") + xlab("")

Flags

All

SNA_TABLE1 %>%
  filter(LOCATION %in% c("FRA", "ITA", "DEU", "USA"),
         MEASURE == "C",
         TRANSACT %in% c("B1_GE", "B1GVC")) %>%
  year_to_date %>%
  #filter(date >= as.Date("1980-01-01")) %>%
  left_join(SNA_TABLE1_var$LOCATION, by = "LOCATION") %>%
  select(date, Location, TRANSACT, obsValue) %>%
  spread(TRANSACT, obsValue) %>%
  mutate(obsValue = B1GVC / B1_GE) %>%
  left_join(colors, by = c("Location" = "country")) %>%
  ggplot() + geom_line(aes(x = date, y = obsValue, color = color)) + 
  ylab("Manufacturing Value Added (% of GDP)") + xlab("") +
  scale_color_identity() + theme_minimal() + add_4flags +
  scale_x_date(breaks = seq(1920, 2025, 5) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  scale_y_continuous(breaks = 0.01*seq(-60, 60, 1),
                     labels = scales::percent_format(accuracy = 1))

1990-

SNA_TABLE1 %>%
  filter(LOCATION %in% c("FRA", "ITA", "DEU", "USA"),
         MEASURE == "C",
         TRANSACT %in% c("B1_GE", "B1GVC")) %>%
  year_to_date %>%
  filter(date >= as.Date("1990-01-01")) %>%
  left_join(SNA_TABLE1_var$LOCATION, by = "LOCATION") %>%
  select(date, Location, TRANSACT, obsValue) %>%
  spread(TRANSACT, obsValue) %>%
  mutate(obsValue = B1GVC / B1_GE) %>%
  left_join(colors, by = c("Location" = "country")) %>%
  filter(date >= as.Date("1990-01-01")) %>%
  ggplot() + geom_line(aes(x = date, y = obsValue, color = color)) + 
  ylab("Manufacturing Value Added (% of GDP)") + xlab("") +
  scale_color_identity() + theme_minimal() + add_4flags +
  scale_x_date(breaks = seq(1920, 2025, 5) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  scale_y_continuous(breaks = 0.01*seq(-60, 60, 1),
                     labels = scales::percent_format(accuracy = 1))

1995-

SNA_TABLE1 %>%
  filter(LOCATION %in% c("FRA", "ITA", "DEU", "USA"),
         MEASURE == "C",
         TRANSACT %in% c("B1_GE", "B1GVC")) %>%
  year_to_date %>%
  filter(date >= as.Date("1980-01-01")) %>%
  left_join(SNA_TABLE1_var$LOCATION, by = "LOCATION") %>%
  select(date, Location, TRANSACT, obsValue) %>%
  spread(TRANSACT, obsValue) %>%
  mutate(obsValue = B1GVC / B1_GE) %>%
  left_join(colors, by = c("Location" = "country")) %>%
  filter(date >= as.Date("1995-01-01")) %>%
  ggplot() + geom_line(aes(x = date, y = obsValue, color = color)) + 
  ylab("Manufacturing Value Added (% of GDP)") + xlab("") +
  scale_color_identity() + theme_minimal() + add_4flags +
  scale_x_date(breaks = seq(1920, 2025, 5) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  scale_y_continuous(breaks = 0.01*seq(-60, 60, 1),
                     labels = scales::percent_format(accuracy = 1))

Spain, France, Germany, Italy

SNA_TABLE1 %>%
  filter(LOCATION %in% c("FRA", "ITA", "DEU", "ESP"),
         MEASURE == "C",
         TRANSACT %in% c("B1_GE", "B1GVC")) %>%
  year_to_date %>%
  filter(date >= as.Date("1980-01-01")) %>%
  left_join(SNA_TABLE1_var$LOCATION, by = "LOCATION") %>%
  select(date, Location, TRANSACT, obsValue) %>%
  spread(TRANSACT, obsValue) %>%
  mutate(obsValue = B1GVC / B1_GE) %>%
  left_join(colors, by = c("Location" = "country")) %>%
  ggplot() + geom_line(aes(x = date, y = obsValue, color = color)) + 
  ylab("Manufacturing Value Added (% of GDP)") + xlab("") +
  scale_color_identity() + theme_minimal() + add_4flags +
  scale_x_date(breaks = seq(1920, 2025, 5) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  scale_y_continuous(breaks = 0.01*seq(-60, 60, 1),
                     labels = scales::percent_format(accuracy = 1))

Greece, Portugal, Spain

SNA_TABLE1 %>%
  filter(LOCATION %in% c("GRC", "PRT", "ESP"),
         MEASURE == "C",
         TRANSACT %in% c("B1_GE", "B1GVC")) %>%
  year_to_date %>%
  filter(date >= as.Date("1995-01-01")) %>%
  left_join(SNA_TABLE1_var$LOCATION, by = "LOCATION") %>%
  select(date, Location, TRANSACT, obsValue) %>%
  spread(TRANSACT, obsValue) %>%
  mutate(obsValue = B1GVC / B1_GE) %>%
  left_join(colors, by = c("Location" = "country")) %>%
  ggplot() + geom_line(aes(x = date, y = obsValue, color = color)) + 
  ylab("Manufacturing Value Added (% of GDP)") + xlab("") +
  scale_color_identity() + theme_minimal() + add_3flags +
  scale_x_date(breaks = seq(1920, 2025, 5) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  scale_y_continuous(breaks = 0.01*seq(-60, 60, 1),
                     labels = scales::percent_format(accuracy = 1))

Greece, Portugal, Spain, Italy

SNA_TABLE1 %>%
  filter(LOCATION %in% c("GRC", "PRT", "ESP", "ITA"),
         MEASURE == "C",
         TRANSACT %in% c("B1_GE", "B1GVC")) %>%
  year_to_date %>%
  filter(date >= as.Date("1995-01-01")) %>%
  left_join(SNA_TABLE1_var$LOCATION, by = "LOCATION") %>%
  select(date, Location, TRANSACT, obsValue) %>%
  spread(TRANSACT, obsValue) %>%
  mutate(obsValue = B1GVC / B1_GE) %>%
  left_join(colors, by = c("Location" = "country")) %>%
  ggplot() + geom_line(aes(x = date, y = obsValue, color = color)) + 
  ylab("Manufacturing Value Added (% of GDP)") + xlab("") +
  scale_color_identity() + theme_minimal() + add_4flags +
  scale_x_date(breaks = seq(1920, 2025, 5) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  scale_y_continuous(breaks = 0.01*seq(-60, 60, 1),
                     labels = scales::percent_format(accuracy = 1))

France, Germany, United States

SNA_TABLE1 %>%
  filter(LOCATION %in% c("FRA", "USA", "DEU"),
         MEASURE == "C",
         TRANSACT %in% c("B1_GE", "B1GVC")) %>%
  year_to_date %>%
  filter(date >= as.Date("1980-01-01")) %>%
  left_join(SNA_TABLE1_var$LOCATION, by = "LOCATION") %>%
  select(date, Location, TRANSACT, obsValue) %>%
  spread(TRANSACT, obsValue) %>%
  mutate(obsValue = B1GVC / B1_GE) %>%
  left_join(colors, by = c("Location" = "country")) %>%
  ggplot() + geom_line(aes(x = date, y = obsValue, color = color)) + 
  ylab("Manufacturing Value Added (% of GDP)") + xlab("") +
  scale_color_identity() + theme_minimal() + add_3flags +
  scale_x_date(breaks = seq(1920, 2025, 5) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  scale_y_continuous(breaks = 0.01*seq(-60, 60, 1),
                     labels = scales::percent_format(accuracy = 1))

France, Germany, OECD

SNA_TABLE1 %>%
  filter(LOCATION %in% c("FRA", "OECD", "DEU"),
         MEASURE == "C",
         TRANSACT %in% c("B1_GE", "B1GVC")) %>%
  year_to_date %>%
  filter(date >= as.Date("1980-01-01")) %>%
  left_join(SNA_TABLE1_var$LOCATION, by = "LOCATION") %>%
  select(date, Location, LOCATION, TRANSACT, obsValue) %>%
  spread(TRANSACT, obsValue) %>%
  mutate(obsValue = B1GVC / B1_GE) %>%
  mutate(Location = ifelse(LOCATION == "OECD", "OECD members", Location)) %>%
  left_join(colors, by = c("Location" = "country")) %>%
  ggplot() + geom_line(aes(x = date, y = obsValue, color = color)) + 
  ylab("Manufacturing Value Added (% of GDP)") + xlab("") +
  scale_color_identity() + theme_minimal() + add_3flags +
  scale_x_date(breaks = seq(1920, 2025, 5) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  scale_y_continuous(breaks = 0.01*seq(-60, 60, 1),
                     labels = scales::percent_format(accuracy = 1))

France, Denmark, Germany, Austria

SNA_TABLE1 %>%
  filter(LOCATION %in% c("FRA", "DNK", "DEU", "AUT"),
         MEASURE == "C",
         TRANSACT %in% c("B1_GE", "B1GVC")) %>%
  year_to_date %>%
  filter(date >= as.Date("1980-01-01")) %>%
  left_join(SNA_TABLE1_var$LOCATION, by = "LOCATION") %>%
  select(date, Location, TRANSACT, obsValue) %>%
  spread(TRANSACT, obsValue) %>%
  mutate(obsValue = B1GVC / B1_GE) %>%
  left_join(colors, by = c("Location" = "country")) %>%
  ggplot() + geom_line(aes(x = date, y = obsValue, color = color)) + 
  ylab("Manufacturing Value Added (% of GDP)") + xlab("") +
  scale_color_identity() + theme_minimal() + add_4flags +
  scale_x_date(breaks = seq(1920, 2025, 5) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  scale_y_continuous(breaks = 0.01*seq(-60, 60, 1),
                     labels = scales::percent_format(accuracy = 1))

France, Germany, Italy

SNA_TABLE1 %>%
  filter(LOCATION %in% c("FRA", "ITA", "DEU"),
         MEASURE == "C",
         TRANSACT %in% c("B1_GE", "B1GVC")) %>%
  year_to_date %>%
  filter(date >= as.Date("1994-01-01")) %>%
  left_join(SNA_TABLE1_var$LOCATION, by = "LOCATION") %>%
  select(date, Location, TRANSACT, obsValue) %>%
  spread(TRANSACT, obsValue) %>%
  mutate(obsValue = B1GVC / B1_GE) %>%
  left_join(colors, by = c("Location" = "country")) %>%
  ggplot() + geom_line(aes(x = date, y = obsValue, color = color)) + 
  ylab("Manufacturing Value Added (% of GDP)") + xlab("") +
  scale_color_identity() + theme_minimal() + add_3flags +
  scale_x_date(breaks = seq(1920, 2025, 5) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  scale_y_continuous(breaks = 0.01*seq(-60, 60, 1),
                     labels = scales::percent_format(accuracy = 1))

France, Germany, Euro Area (19 countries)

SNA_TABLE1 %>%
  filter(LOCATION %in% c("FRA", "DEU", "EA19"),
         MEASURE == "C",
         TRANSACT %in% c("B1_GE", "B1GVC")) %>%
  year_to_date %>%
  filter(date >= as.Date("1980-01-01")) %>%
  left_join(SNA_TABLE1_var$LOCATION, by = "LOCATION") %>%
  select(date, Location, LOCATION, TRANSACT, obsValue) %>%
  spread(TRANSACT, obsValue) %>%
  mutate(obsValue = B1GVC / B1_GE,
         Location = ifelse(LOCATION == "EA19", "Europe", Location)) %>%
  left_join(colors, by = c("Location" = "country")) %>%
  ggplot() + geom_line(aes(x = date, y = obsValue, color = color)) + 
  ylab("Manufacturing Value Added (% of GDP)") + xlab("") +
  scale_color_identity() + theme_minimal() + add_3flags +
  scale_x_date(breaks = seq(1920, 2025, 5) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  scale_y_continuous(breaks = 0.01*seq(-60, 60, 1),
                     labels = scales::percent_format(accuracy = 1))

Denmark, Netherlands, Norway

Viridis

SNA_TABLE1 %>%
  filter(LOCATION %in% c("DNK", "NLD", "NOR"),
         MEASURE == "C",
         TRANSACT %in% c("B1_GE", "B1GVC")) %>%
  year_to_date %>%
  filter(date >= as.Date("1980-01-01")) %>%
  left_join(SNA_TABLE1_var$LOCATION, by = "LOCATION") %>%
  select(date, Location, TRANSACT, obsValue) %>%
  spread(TRANSACT, obsValue) %>%
  mutate(obsValue = B1GVC / B1_GE) %>%
  left_join(colors, by = c("Location" = "country")) %>%
  ggplot() + geom_line(aes(x = date, y = obsValue, color = color)) + 
  ylab("Manufacturing Value Added (% of GDP)") + xlab("") +
  scale_color_identity() + theme_minimal() + add_3flags +
  scale_x_date(breaks = seq(1920, 2025, 5) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  scale_y_continuous(breaks = 0.01*seq(-60, 60, 1),
                     labels = scales::percent_format(accuracy = 1))

Flags

SNA_TABLE1 %>%
  filter(LOCATION %in% c("DNK", "NLD", "NOR"),
         MEASURE == "C",
         TRANSACT %in% c("B1_GE", "B1GVC")) %>%
  year_to_date %>%
  filter(date >= as.Date("1980-01-01")) %>%
  left_join(SNA_TABLE1_var$LOCATION, by = "LOCATION") %>%
  select(date, Location, TRANSACT, obsValue) %>%
  spread(TRANSACT, obsValue) %>%
  mutate(obsValue = B1GVC / B1_GE) %>%
  left_join(colors, by = c("Location" = "country")) %>%
  ggplot() + geom_line(aes(x = date, y = obsValue, color = color)) + 
  ylab("Manufacturing Value Added (% of GDP)") + xlab("") +
  scale_color_identity() + theme_minimal() + add_3flags +
  scale_x_date(breaks = seq(1920, 2025, 5) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  scale_y_continuous(breaks = 0.01*seq(-60, 60, 1),
                     labels = scales::percent_format(accuracy = 1))

Austria, Finland, New Zealand

SNA_TABLE1 %>%
  filter(LOCATION %in% c("NZL", "FIN", "AUT"),
         MEASURE == "C",
         TRANSACT %in% c("B1_GE", "B1GVC")) %>%
  year_to_date %>%
  filter(date >= as.Date("1980-01-01")) %>%
  left_join(SNA_TABLE1_var$LOCATION, by = "LOCATION") %>%
  select(date, Location, TRANSACT, obsValue) %>%
  spread(TRANSACT, obsValue) %>%
  mutate(obsValue = B1GVC / B1_GE) %>%
  left_join(colors, by = c("Location" = "country")) %>%
  ggplot() + geom_line(aes(x = date, y = obsValue, color = color)) + 
  ylab("Manufacturing Value Added (% of GDP)") + xlab("") +
  scale_color_identity() + theme_minimal() + add_3flags +
  scale_x_date(breaks = seq(1920, 2025, 5) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  scale_y_continuous(breaks = 0.01*seq(-60, 60, 1),
                     labels = scales::percent_format(accuracy = 1))

B1GVB_E - Industry Value Added

SNA_TABLE1 %>%
  filter(MEASURE == "C",
         obsTime %in% c("1995", "2000", "2018"),
         TRANSACT %in% c("B1_GE", "B1GVB_E")) %>%
  left_join(SNA_TABLE1_var$LOCATION, by = "LOCATION") %>%
  select(LOCATION, Location, TRANSACT, obsTime, obsValue) %>%
  spread(TRANSACT, obsValue) %>%
  mutate(B1GVB_E_B1_GE = round(100*B1GVB_E / B1_GE, 1)) %>%
  select(-B1GVB_E, -B1_GE) %>%
  spread(obsTime, B1GVB_E_B1_GE) %>%
  arrange(-`2018`) %>%
  mutate(Flag = gsub(" ", "-", str_to_lower(gsub(" ", "-", Location))),
         Flag = paste0('<img src="../../icon/flag/vsmall/', Flag, '.png" alt="Flag">')) %>%
  select(Flag, everything()) %>%
  {if (is_html_output()) datatable(., filter = 'top', rownames = F, escape = F) else .}

SNA_TABLE1 %>%
  filter(LOCATION %in% c("FRA", "ITA", "DEU"),
         MEASURE == "C",
         TRANSACT %in% c("B1_GE", "B1GVB_E")) %>%
  year_to_date %>%
  filter(date >= as.Date("1980-01-01")) %>%
  left_join(SNA_TABLE1_var$LOCATION, by = "LOCATION") %>%
  select(date, Location, TRANSACT, obsValue) %>%
  spread(TRANSACT, obsValue) %>%
  mutate(obsValue = B1GVB_E / B1_GE) %>%
  left_join(colors, by = c("Location" = "country")) %>%
  ggplot() + geom_line(aes(x = date, y = obsValue, color = color)) + 
  ylab("Industry Value Added (% of GDP)") + xlab("") +
  scale_color_identity() + theme_minimal() + add_3flags +
  scale_x_date(breaks = seq(1920, 2025, 5) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  scale_y_continuous(breaks = 0.01*seq(-60, 60, 1),
                     labels = scales::percent_format(accuracy = 1))

Australia, Eurozone, Japan

SNA_TABLE1 %>%
  filter(LOCATION %in% c("AUS", "JPN", "EA19"),
         MEASURE == "C",
         TRANSACT %in% c("B1_GE", "B1GVB_E")) %>%
  year_to_date %>%
  filter(date >= as.Date("1994-01-01"),
         date <= as.Date("2020-01-01")) %>%
  left_join(SNA_TABLE1_var$LOCATION, by = "LOCATION") %>%
  select(date, Location, LOCATION, TRANSACT, obsValue) %>%
  spread(TRANSACT, obsValue) %>%
  mutate(obsValue = B1GVB_E / B1_GE) %>%
  mutate(Location = ifelse(LOCATION == "EA19", "Europe", Location)) %>%
  left_join(colors, by = c("Location" = "country")) %>%
  ggplot() + geom_line(aes(x = date, y = obsValue, color = color)) + 
  ylab("Industry Value Added (% of GDP)") + xlab("") +
  scale_color_identity() + theme_minimal() + add_3flags +
  scale_x_date(breaks = seq(1920, 2025, 5) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  scale_y_continuous(breaks = 0.01*seq(-60, 60, 1),
                     labels = scales::percent_format(accuracy = 1))

France, Germany, United States

SNA_TABLE1 %>%
  filter(LOCATION %in% c("FRA", "DEU", "USA"),
         MEASURE == "C",
         TRANSACT %in% c("B1_GE", "B1GVB_E")) %>%
  year_to_date %>%
  filter(date >= as.Date("1994-01-01"),
         date <= as.Date("2020-01-01")) %>%
  left_join(SNA_TABLE1_var$LOCATION, by = "LOCATION") %>%
  select(date, Location, TRANSACT, obsValue) %>%
  spread(TRANSACT, obsValue) %>%
  mutate(B1GVB_E_B1_GE = B1GVB_E / B1_GE,
         Location = case_when(Location == "Germany" ~ "Allemagne",
                                   Location == "United States" ~ "Etats-Unis",
                                   Location == "France" ~ "France")) %>%
  ggplot() + geom_line() +
  aes(x = date, y = B1GVB_E_B1_GE, color = Location) +
  scale_color_manual(values = viridis(4)[1:3]) +
  theme_minimal() +
  scale_x_date(breaks = seq(1920, 2025, 2) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  theme(legend.position = c(0.15, 0.2),
        legend.title = element_blank()) +
  scale_y_continuous(breaks = 0.01*seq(-60, 60, 1),
                     labels = scales::percent_format(accuracy = 1)) +
  ylab("Valeur Ajoutée dans l'Industrie (% du PIB)") + xlab("")

France, Germany, EA19

SNA_TABLE1 %>%
  filter(LOCATION %in% c("FRA", "DEU", "EA19"),
         MEASURE == "C",
         TRANSACT %in% c("B1_GE", "B1GVB_E")) %>%
  year_to_date %>%
  filter(date >= as.Date("1994-01-01"),
         date <= as.Date("2020-01-01")) %>%
  left_join(SNA_TABLE1_var$LOCATION, by = "LOCATION") %>%
  select(date, Location, TRANSACT, obsValue) %>%
  spread(TRANSACT, obsValue) %>%
  mutate(B1GVB_E_B1_GE = B1GVB_E / B1_GE,
         Location = case_when(Location == "Germany" ~ "Allemagne",
                                   Location == "United States" ~ "Etats-Unis",
                                   Location == "France" ~ "France")) %>%
  ggplot() + geom_line() +
  aes(x = date, y = B1GVB_E_B1_GE, color = Location) +
  scale_color_manual(values = viridis(4)[1:3]) +
  theme_minimal() +
  scale_x_date(breaks = seq(1920, 2025, 2) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  theme(legend.position = c(0.15, 0.2),
        legend.title = element_blank()) +
  scale_y_continuous(breaks = 0.01*seq(-60, 60, 1),
                     labels = scales::percent_format(accuracy = 1)) +
  ylab("Valeur Ajoutée dans l'Industrie (% du PIB)") + xlab("")

France, Germany, Italy

SNA_TABLE1 %>%
  filter(LOCATION %in% c("FRA", "ITA", "DEU"),
         MEASURE == "C",
         TRANSACT %in% c("B1_GE", "B1GVB_E")) %>%
  year_to_date %>%
  filter(date >= as.Date("1994-01-01"),
         date <= as.Date("2020-01-01")) %>%
  left_join(SNA_TABLE1_var$LOCATION, by = "LOCATION") %>%
  select(date, Location, TRANSACT, obsValue) %>%
  spread(TRANSACT, obsValue) %>%
  mutate(obsValue = B1GVB_E / B1_GE) %>%
  left_join(colors, by = c("Location" = "country")) %>%
  ggplot() + geom_line(aes(x = date, y = obsValue, color = color)) + 
  ylab("Industry Value Added (% of GDP)") + xlab("") +
  scale_color_identity() + theme_minimal() + add_3flags +
  scale_x_date(breaks = seq(1920, 2025, 5) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  scale_y_continuous(breaks = 0.01*seq(-60, 60, 1),
                     labels = scales::percent_format(accuracy = 1))

France, Italy, Spain

SNA_TABLE1 %>%
  filter(LOCATION %in% c("FRA", "ITA", "ESP"),
         MEASURE == "C",
         TRANSACT %in% c("B1_GE", "B1GVB_E")) %>%
  year_to_date %>%
  filter(date >= as.Date("1994-01-01"),
         date <= as.Date("2020-01-01")) %>%
  left_join(SNA_TABLE1_var$LOCATION, by = "LOCATION") %>%
  select(date, Location, TRANSACT, obsValue) %>%
  spread(TRANSACT, obsValue) %>%
  mutate(obsValue = B1GVB_E / B1_GE) %>%
  left_join(colors, by = c("Location" = "country")) %>%
  ggplot() + geom_line(aes(x = date, y = obsValue, color = color)) + 
  ylab("Industry Value Added (% of GDP)") + xlab("") +
  scale_color_identity() + theme_minimal() + add_3flags +
  scale_x_date(breaks = seq(1920, 2025, 5) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  scale_y_continuous(breaks = 0.01*seq(-60, 60, 1),
                     labels = scales::percent_format(accuracy = 1))

France, Allemagne, Italie

SNA_TABLE1 %>%
  filter(LOCATION %in% c("FRA", "ITA", "DEU"),
         MEASURE == "C",
         TRANSACT %in% c("B1_GE", "B1GVC")) %>%
  year_to_date %>%
  filter(date >= as.Date("1994-01-01"),
         date <= as.Date("2020-01-01")) %>%
  left_join(SNA_TABLE1_var$LOCATION, by = "LOCATION") %>%
  select(date, Location, TRANSACT, obsValue) %>%
  spread(TRANSACT, obsValue) %>%
  mutate(B1GVC_B1_GE = B1GVC / B1_GE,
         Location = case_when(Location == "Germany" ~ "Allemagne",
                                   Location == "Italy" ~ "Italie",
                                   Location == "France" ~ "France")) %>%
  ggplot() + geom_line() +
  aes(x = date, y = B1GVC_B1_GE, color = Location) +
  scale_color_manual(values = viridis(4)[1:3]) +
  theme_minimal() +
  scale_x_date(breaks = seq(1920, 2025, 2) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  theme(legend.position = c(0.15, 0.2),
        legend.title = element_blank()) +
  scale_y_continuous(breaks = 0.01*seq(-60, 60, 1),
                     labels = scales::percent_format(accuracy = 1)) +
  ylab("Valeur Ajoutée dans l'Industrie (% du PIB)") + xlab("")

D11VC - Manufacturing Wages and Salaries (without contributions)

SNA_TABLE1 %>%
  filter(LOCATION %in% c("FRA", "ITA", "DEU"),
         MEASURE == "C",
         TRANSACT %in% c("B1_GE", "D11VC")) %>%
  year_to_date %>%
  filter(date >= as.Date("1980-01-01")) %>%
  left_join(SNA_TABLE1_var$LOCATION, by = "LOCATION") %>%
  select(date, Location, TRANSACT, obsValue) %>%
  spread(TRANSACT, obsValue) %>%
  mutate(D11VC_B1_GE = D11VC / B1_GE) %>%
  ggplot() + geom_line() +
  aes(x = date, y = D11VC_B1_GE, color = Location) +
  scale_color_manual(values = viridis(4)[1:3]) +
  theme_minimal() +
  scale_x_date(breaks = seq(1920, 2025, 5) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  theme(legend.position = c(0.15, 0.2),
        legend.title = element_blank()) +
  scale_y_continuous(breaks = 0.01*seq(-60, 60, 1),
                     labels = scales::percent_format(accuracy = 1)) +
  ylab("Manufacturing Wages (% of GDP)") + xlab("")

D1VC - Manufacturing Compensation of Employees (with contributions)

SNA_TABLE1 %>%
  filter(LOCATION %in% c("FRA", "ITA", "DEU"),
         MEASURE == "C",
         TRANSACT %in% c("B1_GE", "D1VC")) %>%
  year_to_date %>%
  filter(date >= as.Date("1980-01-01")) %>%
  left_join(SNA_TABLE1_var$LOCATION, by = "LOCATION") %>%
  select(date, Location, TRANSACT, obsValue) %>%
  spread(TRANSACT, obsValue) %>%
  mutate(value = D1VC / B1_GE) %>%
  ggplot() + geom_line() +
  aes(x = date, y = value, color = Location) +
  scale_color_manual(values = viridis(4)[1:3]) +
  theme_minimal() +
  scale_x_date(breaks = seq(1920, 2025, 5) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  theme(legend.position = c(0.15, 0.2),
        legend.title = element_blank()) +
  scale_y_continuous(breaks = 0.01*seq(-60, 60, 1),
                     labels = scales::percent_format(accuracy = 1)) +
  ylab("Manufacturing Compensation of Employees (% of GDP)") + xlab("")

B1_GA - Gross domestic product (output approach)

SNA_TABLE1 %>%
  filter(LOCATION %in% c("USA"),
         TRANSACT == "B1_GA",
         MEASURE %in% c("C", "V")) %>%
  mutate(date = paste0(obsTime, "-01-01") %>% as.Date,
         obsValue = obsValue / 10^6) %>%
  left_join(SNA_TABLE1_var$LOCATION, by = "LOCATION") %>%
  left_join(SNA_TABLE1_var$MEASURE, by = "MEASURE") %>%
  group_by(LOCATION) %>%
  arrange(date) %>%
  select(date, obsValue, Measure) %>%
  ggplot(.) + geom_line(aes(x = date, y = obsValue, linetype = Measure, color = Measure)) + 
  theme_minimal() + xlab("") + ylab("") +
  scale_color_manual(values = viridis(4)[1:3]) +
  geom_rect(data = nber_recessions, 
            aes(xmin = Peak, xmax = Trough, ymin = -Inf, ymax = +Inf), 
            fill = 'grey', alpha = 0.5) +
  scale_x_date(breaks = seq(1960, 2020, 5) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y"),
               limits = c(1970, 2019) %>% paste0("-01-01") %>% as.Date) +
  scale_y_continuous(breaks = seq(0, 30, 1),
                     labels = dollar_format(suffix = "Tn", prefix = "$", accuracy = 1)) +
  theme(legend.position = c(0.3, 0.90),
        legend.title = element_blank())

P3 - Final consumption expenditure

SNA_TABLE1 %>%
  filter(LOCATION %in% c("USA"),
         TRANSACT == "P3",
         MEASURE %in% c("C", "V")) %>%
  mutate(date = paste0(obsTime, "-01-01") %>% as.Date,
         obsValue = obsValue / 10^6) %>%
  left_join(SNA_TABLE1_var$LOCATION, by = "LOCATION") %>%
  left_join(SNA_TABLE1_var$MEASURE, by = "MEASURE") %>%
  group_by(LOCATION) %>%
  arrange(date) %>%
  select(date, obsValue, Measure) %>%
  ggplot(.) + 
  geom_line(aes(x = date, y = obsValue, linetype = Measure, color = Measure)) + 
  theme_minimal() + xlab("") + ylab("") +
  scale_color_manual(values = viridis(4)[1:3]) +
  geom_rect(data = nber_recessions, 
            aes(xmin = Peak, xmax = Trough, ymin = -Inf, ymax = +Inf), 
            fill = 'grey', alpha = 0.5) +
  scale_x_date(breaks = seq(1960, 2020, 5) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y"),
               limits = c(1970, 2019) %>% paste0("-01-01") %>% as.Date) +
  scale_y_continuous(breaks = seq(0, 30, 1),
                     labels = dollar_format(suffix = "Tn", prefix = "$", accuracy = 1)) +
  theme(legend.position = c(0.3, 0.90),
        legend.title = element_blank())

Individual Consumption / trend GDP

France, Germany, Italy

SNA_TABLE1 %>%
  filter(MEASURE == "VXVOB",
         LOCATION %in% c("FRA", "DEU", "ITA"),
         TRANSACT %in% c("B1_GE", "P41")) %>%
  year_to_date %>%
  left_join(SNA_TABLE1_var$LOCATION %>%
              setNames(c("LOCATION", "Location")), by = "LOCATION") %>%
  select(Location, date, TRANSACT, obsValue) %>%
  spread(TRANSACT, obsValue) %>%
  group_by(Location) %>%
  mutate(B1_GE_trend = log(B1_GE) %>% hpfilter(1000000) %>% pluck("trend") %>% exp,
         P41_B1_GE_trend = P41 / B1_GE_trend,
         P41_B1_GE = P41 / B1_GE) %>%
  select(Location, date, P41_B1_GE_trend) %>%
  na.omit %>%
  ggplot() + geom_line(aes(x = date, y = P41_B1_GE_trend, color = Location)) +
  scale_color_manual(values = c("#0055a4", "#000000", "#008c45")) +
  theme_minimal() +
  scale_x_date(breaks = seq(1920, 2025, 10) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  theme(legend.position = c(0.15, 0.9),
        legend.title = element_blank()) +
  scale_y_continuous(breaks = 0.01*seq(-10, 100, 5),
                     labels = scales::percent_format(accuracy = 1)) +
  ylab("Individual Consumption (% of trend GDP)") + xlab("")

United Kingdom, Japan, United States

SNA_TABLE1 %>%
  filter(MEASURE == "VXVOB",
         LOCATION %in% c("JPN", "GBR", "USA"),
         TRANSACT %in% c("B1_GE", "P41")) %>%
  year_to_date %>%
  left_join(SNA_TABLE1_var$LOCATION %>%
              setNames(c("LOCATION", "Location")), by = "LOCATION") %>%
  select(Location, date, TRANSACT, obsValue) %>%
  spread(TRANSACT, obsValue) %>%
  group_by(Location) %>%
  mutate(B1_GE_trend = log(B1_GE) %>% hpfilter(100000) %>% pluck("trend") %>% exp,
         P41_B1_GE_trend = P41 / B1_GE_trend,
         P41_B1_GE = P41 / B1_GE) %>%
  select(Location, date, P41_B1_GE_trend) %>%
  na.omit %>%
  ggplot() + geom_line(aes(x = date, y = P41_B1_GE_trend, color = Location)) +
  scale_color_manual(values = viridis(4)[1:3]) +
  theme_minimal() +
  scale_x_date(breaks = seq(1920, 2025, 10) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  theme(legend.position = c(0.15, 0.9),
        legend.title = element_blank()) +
  scale_y_continuous(breaks = 0.01*seq(-10, 100, 5),
                     labels = scales::percent_format(accuracy = 1)) +
  ylab("Individual Consumption (% of trend GDP)") + xlab("")

Switzerland, Germany, Netherlands

SNA_TABLE1 %>%
  filter(MEASURE == "VXVOB",
         LOCATION %in% c("NLD", "CHE", "DEU"),
         TRANSACT %in% c("B1_GE", "P41")) %>%
  year_to_date %>%
  left_join(SNA_TABLE1_var$LOCATION %>%
              setNames(c("LOCATION", "Location")), by = "LOCATION") %>%
  select(Location, date, TRANSACT, obsValue) %>%
  spread(TRANSACT, obsValue) %>%
  group_by(Location) %>%
  mutate(B1_GE_trend = log(B1_GE) %>% hpfilter(100000) %>% pluck("trend") %>% exp,
         P41_B1_GE_trend = P41 / B1_GE_trend,
         P41_B1_GE = P41 / B1_GE) %>%
  select(Location, date, P41_B1_GE_trend) %>%
  na.omit %>%
  ggplot() + geom_line(aes(x = date, y = P41_B1_GE_trend, color = Location)) +
  scale_color_manual(values = viridis(4)[1:3]) +
  theme_minimal() +
  scale_x_date(breaks = seq(1920, 2025, 10) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  theme(legend.position = c(0.85, 0.8),
        legend.title = element_blank()) +
  scale_y_continuous(breaks = 0.01*seq(-10, 100, 2),
                     labels = scales::percent_format(accuracy = 1)) +
  ylab("Individual Consumption (% of trend GDP)") + xlab("")

Net Exports / trend GDP

France, Germany, Italy

SNA_TABLE1 %>%
  filter(MEASURE == "VXVOB",
         LOCATION %in% c("FRA", "DEU", "ITA"),
         TRANSACT %in% c("B1_GE", "B11")) %>%
  year_to_date %>%
  left_join(SNA_TABLE1_var$LOCATION, by = "LOCATION") %>%
  select(Location, date, TRANSACT, obsValue) %>%
  spread(TRANSACT, obsValue) %>%
  group_by(Location) %>%
  mutate(B1_GE_trend = log(B1_GE) %>% hpfilter(1000000) %>% pluck("trend") %>% exp,
         B11_B1_GE_trend = B11 / B1_GE_trend,
         B11_B1_GE = B11 / B1_GE) %>%
  select(Location, date, B11_B1_GE_trend) %>%
  na.omit %>%
  ggplot() + geom_line() + theme_minimal() +
  aes(x = date, y = B11_B1_GE_trend, color = Location) +
  scale_color_manual(values = c("#0055a4", "#000000", "#008c45")) +
  scale_x_date(breaks = seq(1920, 2025, 10) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  theme(legend.position = c(0.15, 0.9),
        legend.title = element_blank()) +
  scale_y_continuous(breaks = 0.01*seq(-10, 100, 1),
                     labels = scales::percent_format(accuracy = 1)) +
  ylab("External balance (% of trend GDP)") + xlab("")

Austria, Finland, New Zealand

SNA_TABLE1 %>%
  filter(MEASURE == "VXVOB",
         LOCATION %in% c("AUS", "FIN", "NLZ"),
         TRANSACT %in% c("B1_GE", "B11")) %>%
  year_to_date %>%
  left_join(SNA_TABLE1_var$LOCATION %>%
              setNames(c("LOCATION", "Location")), by = "LOCATION") %>%
  select(Location, date, TRANSACT, obsValue) %>%
  spread(TRANSACT, obsValue) %>%
  group_by(Location) %>%
  mutate(B1_GE_trend = log(B1_GE) %>% hpfilter(1000000) %>% pluck("trend") %>% exp,
         B11_B1_GE_trend = B11 / B1_GE_trend,
         B11_B1_GE = B11 / B1_GE) %>%
  select(Location, date, B11_B1_GE_trend) %>%
  na.omit %>%
  ggplot() + geom_line(aes(x = date, y = B11_B1_GE_trend, color = Location)) +
  scale_color_manual(values = viridis(4)[1:3]) +
  theme_minimal() +
  scale_x_date(breaks = seq(1920, 2025, 10) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  theme(legend.position = c(0.15, 0.9),
        legend.title = element_blank()) +
  scale_y_continuous(breaks = 0.01*seq(-10, 100, 1),
                     labels = scales::percent_format(accuracy = 1)) +
  ylab("External balance (% of trend GDP)") + xlab("")

United Kingdom, Japan, United States

SNA_TABLE1 %>%
  filter(MEASURE == "VXVOB",
         LOCATION %in% c("JPN", "GBR", "USA"),
         TRANSACT %in% c("B1_GE", "B11")) %>%
  year_to_date %>%
  left_join(SNA_TABLE1_var$LOCATION %>%
              setNames(c("LOCATION", "Location")), by = "LOCATION") %>%
  select(Location, date, TRANSACT, obsValue) %>%
  spread(TRANSACT, obsValue) %>%
  group_by(Location) %>%
  mutate(B1_GE_trend = log(B1_GE) %>% hpfilter(100000) %>% pluck("trend") %>% exp,
         B11_B1_GE_trend = B11 / B1_GE_trend,
         B11_B1_GE = B11 / B1_GE) %>%
  select(Location, date, B11_B1_GE_trend) %>%
  na.omit %>%
  ggplot() + geom_line(aes(x = date, y = B11_B1_GE_trend, color = Location)) +
  scale_color_manual(values = viridis(4)[1:3]) +
  theme_minimal() +
  scale_x_date(breaks = seq(1920, 2025, 10) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  theme(legend.position = c(0.85, 0.9),
        legend.title = element_blank()) +
  scale_y_continuous(breaks = 0.01*seq(-10, 100, 1),
                     labels = scales::percent_format(accuracy = 1)) +
  ylab("External balance (% of trend GDP)") + xlab("")

Switzerland, Germany, Netherlands

SNA_TABLE1 %>%
  filter(MEASURE == "VXVOB",
         LOCATION %in% c("NLD", "CHE", "DEU"),
         TRANSACT %in% c("B1_GE", "B11")) %>%
  year_to_date %>%
  left_join(SNA_TABLE1_var$LOCATION %>%
              setNames(c("LOCATION", "Location")), by = "LOCATION") %>%
  select(Location, date, TRANSACT, obsValue) %>%
  spread(TRANSACT, obsValue) %>%
  group_by(Location) %>%
  mutate(B1_GE_trend = log(B1_GE) %>% hpfilter(100000) %>% pluck("trend") %>% exp,
         B11_B1_GE_trend = B11 / B1_GE_trend,
         B11_B1_GE = B11 / B1_GE) %>%
  select(Location, date, B11_B1_GE_trend) %>%
  na.omit %>%
  ggplot() + geom_line(aes(x = date, y = B11_B1_GE_trend, color = Location)) +
  scale_color_manual(values = viridis(4)[1:3]) +
  theme_minimal() +
  scale_x_date(breaks = seq(1920, 2025, 10) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  theme(legend.position = c(0.15, 0.8),
        legend.title = element_blank()) +
  scale_y_continuous(breaks = 0.01*seq(-10, 100, 2),
                     labels = scales::percent_format(accuracy = 1)) +
  ylab("External balance (% of trend GDP)") + xlab("")

Chile, Denmark, Sweden

SNA_TABLE1 %>%
  filter(MEASURE == "VXVOB",
         LOCATION %in% c("SWE", "DNK", "CHL"),
         TRANSACT %in% c("B1_GE", "B11")) %>%
  year_to_date %>%
  left_join(SNA_TABLE1_var$LOCATION %>%
              setNames(c("LOCATION", "Location")), by = "LOCATION") %>%
  select(Location, date, TRANSACT, obsValue) %>%
  spread(TRANSACT, obsValue) %>%
  group_by(Location) %>%
  mutate(B1_GE_trend = log(B1_GE) %>% hpfilter(1000000) %>% pluck("trend") %>% exp,
         B11_B1_GE_trend = B11 / B1_GE_trend,
         B11_B1_GE = B11 / B1_GE) %>%
  select(Location, date, B11_B1_GE_trend) %>%
  na.omit %>%
  ggplot() + geom_line(aes(x = date, y = B11_B1_GE_trend, color = Location)) +
  scale_color_manual(values = viridis(4)[1:3]) +
  theme_minimal() +
  scale_x_date(breaks = seq(1920, 2025, 10) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  theme(legend.position = c(0.15, 0.8),
        legend.title = element_blank()) +
  scale_y_continuous(breaks = 0.01*seq(-10, 100, 1),
                     labels = scales::percent_format(accuracy = 1)) +
  ylab("External balance (% of trend GDP)") + xlab("")

Argentina, Brazil, Chile

SNA_TABLE1 %>%
  filter(MEASURE == "VXVOB",
         LOCATION %in% c("ARG", "BRA", "CHL"),
         TRANSACT %in% c("B1_GE", "B11")) %>%
  year_to_date %>%
  left_join(SNA_TABLE1_var$LOCATION %>%
              setNames(c("LOCATION", "Location")), by = "LOCATION") %>%
  select(Location, date, TRANSACT, obsValue) %>%
  spread(TRANSACT, obsValue) %>%
  group_by(Location) %>%
  mutate(B1_GE_trend = log(B1_GE) %>% hpfilter(1000000) %>% pluck("trend") %>% exp,
         B11_B1_GE_trend = B11 / B1_GE_trend,
         B11_B1_GE = B11 / B1_GE) %>%
  select(Location, date, B11_B1_GE_trend) %>%
  na.omit %>%
  ggplot() + geom_line(aes(x = date, y = B11_B1_GE_trend, color = Location)) +
  scale_color_manual(values = viridis(4)[1:3]) +
  theme_minimal() +
  scale_x_date(breaks = seq(1920, 2025, 10) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  theme(legend.position = c(0.15, 0.2),
        legend.title = element_blank()) +
  scale_y_continuous(breaks = 0.01*seq(-10, 100, 1),
                     labels = scales::percent_format(accuracy = 1)) +
  ylab("External balance (% of trend GDP)") + xlab("")

Consumption / trend GDP

France, Germany, Italy

SNA_TABLE1 %>%
  filter(MEASURE == "VXVOB",
         LOCATION %in% c("FRA", "DEU", "ITA"),
         TRANSACT %in% c("B1_GE", "P31S14")) %>%
  year_to_date %>%
  left_join(SNA_TABLE1_var$LOCATION %>%
              setNames(c("LOCATION", "Location")), by = "LOCATION") %>%
  select(Location, date, TRANSACT, obsValue) %>%
  spread(TRANSACT, obsValue) %>%
  group_by(Location) %>%
  mutate(B1_GE_trend = log(B1_GE) %>% hpfilter(1000000) %>% pluck("trend") %>% exp,
         P31S14_B1_GE_trend = P31S14 / B1_GE_trend,
         P31S14_B1_GE = P31S14 / B1_GE) %>%
  select(Location, date, P31S14_B1_GE_trend) %>%
  na.omit %>%
  ggplot() + geom_line(aes(x = date, y = P31S14_B1_GE_trend, color = Location)) +
  scale_color_manual(values = viridis(4)[1:3]) +
  theme_minimal() +
  scale_x_date(breaks = seq(1920, 2025, 10) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  theme(legend.position = c(0.15, 0.9),
        legend.title = element_blank()) +
  scale_y_continuous(breaks = 0.01*seq(0, 100, 5),
                     labels = scales::percent_format(accuracy = 1)) +
  ylab("External balance (% of trend GDP)") + xlab("")

United Kingdom, Japan, United States

SNA_TABLE1 %>%
  filter(MEASURE == "VXVOB",
         LOCATION %in% c("JPN", "GBR", "USA"),
         TRANSACT %in% c("B1_GE", "P31S14")) %>%
  year_to_date %>%
  left_join(SNA_TABLE1_var$LOCATION %>%
              setNames(c("LOCATION", "Location")), by = "LOCATION") %>%
  select(Location, date, TRANSACT, obsValue) %>%
  spread(TRANSACT, obsValue) %>%
  group_by(Location) %>%
  mutate(B1_GE_trend = log(B1_GE) %>% hpfilter(100000) %>% pluck("trend") %>% exp,
         P31S14_B1_GE_trend = P31S14 / B1_GE_trend,
         P31S14_B1_GE = P31S14 / B1_GE) %>%
  select(Location, date, P31S14_B1_GE_trend) %>%
  na.omit %>%
  ggplot() + geom_line(aes(x = date, y = P31S14_B1_GE_trend, color = Location)) +
  scale_color_manual(values = viridis(4)[1:3]) +
  theme_minimal() +
  scale_x_date(breaks = seq(1920, 2025, 10) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  theme(legend.position = c(0.15, 0.9),
        legend.title = element_blank()) +
  scale_y_continuous(breaks = 0.01*seq(0, 100, 5),
                     labels = scales::percent_format(accuracy = 1)) +
  ylab("Consumption (% of trend GDP)") + xlab("")

Switzerland, Germany, Netherlands

SNA_TABLE1 %>%
  filter(MEASURE == "VXVOB",
         LOCATION %in% c("NLD", "CHE", "DEU"),
         TRANSACT %in% c("B1_GE", "P31S14")) %>%
  year_to_date %>%
  left_join(SNA_TABLE1_var$LOCATION %>%
              setNames(c("LOCATION", "Location")), by = "LOCATION") %>%
  select(Location, date, TRANSACT, obsValue) %>%
  spread(TRANSACT, obsValue) %>%
  group_by(Location) %>%
  mutate(B1_GE_trend = log(B1_GE) %>% hpfilter(100000) %>% pluck("trend") %>% exp,
         P31S14_B1_GE_trend = P31S14 / B1_GE_trend,
         P31S14_B1_GE = P31S14 / B1_GE) %>%
  select(Location, date, P31S14_B1_GE_trend) %>%
  na.omit %>%
  ggplot() + geom_line(aes(x = date, y = P31S14_B1_GE_trend, color = Location)) +
  scale_color_manual(values = viridis(4)[1:3]) +
  theme_minimal() +
  scale_x_date(breaks = seq(1920, 2025, 10) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  theme(legend.position = c(0.15, 0.3),
        legend.title = element_blank()) +
  scale_y_continuous(breaks = 0.01*seq(0, 100, 5),
                     labels = scales::percent_format(accuracy = 1)) +
  ylab("Consumption (% of trend GDP)") + xlab("")

Government Spending / trend GDP

France, Germany, Italy

SNA_TABLE1 %>%
  filter(MEASURE == "VXVOB",
         LOCATION %in% c("FRA", "DEU", "ITA"),
         TRANSACT %in% c("B1_GE", "P3S13")) %>%
  year_to_date %>%
  left_join(SNA_TABLE1_var$LOCATION %>%
              setNames(c("LOCATION", "Location")), by = "LOCATION") %>%
  select(Location, date, TRANSACT, obsValue) %>%
  spread(TRANSACT, obsValue) %>%
  group_by(Location) %>%
  mutate(B1_GE_trend = log(B1_GE) %>% hpfilter(1000000) %>% pluck("trend") %>% exp,
         P3S13_B1_GE_trend = P3S13 / B1_GE_trend,
         P3S13_B1_GE = P3S13 / B1_GE) %>%
  select(Location, date, P3S13_B1_GE_trend) %>%
  na.omit %>%
  ggplot() + geom_line(aes(x = date, y = P3S13_B1_GE_trend, color = Location)) +
  scale_color_manual(values = viridis(4)[1:3]) +
  theme_minimal() +
  scale_x_date(breaks = seq(1920, 2025, 10) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  theme(legend.position = c(0.15, 0.9),
        legend.title = element_blank()) +
  scale_y_continuous(breaks = 0.01*seq(0, 100, 1),
                     labels = scales::percent_format(accuracy = 1)) +
  ylab("Spending (% of trend GDP)") + xlab("")

United Kingdom, Japan, United States

SNA_TABLE1 %>%
  filter(MEASURE == "VXVOB",
         LOCATION %in% c("JPN", "GBR", "USA"),
         TRANSACT %in% c("B1_GE", "P3S13")) %>%
  year_to_date %>%
  left_join(SNA_TABLE1_var$LOCATION %>%
              setNames(c("LOCATION", "Location")), by = "LOCATION") %>%
  select(Location, date, TRANSACT, obsValue) %>%
  spread(TRANSACT, obsValue) %>%
  group_by(Location) %>%
  mutate(B1_GE_trend = log(B1_GE) %>% hpfilter(100000) %>% pluck("trend") %>% exp,
         P3S13_B1_GE_trend = P3S13 / B1_GE_trend,
         P3S13_B1_GE = P3S13 / B1_GE) %>%
  select(Location, date, P3S13_B1_GE_trend) %>%
  na.omit %>%
  ggplot() + geom_line(aes(x = date, y = P3S13_B1_GE_trend, color = Location)) +
  scale_color_manual(values = viridis(4)[1:3]) +
  theme_minimal() +
  scale_x_date(breaks = seq(1920, 2025, 10) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  theme(legend.position = c(0.85, 0.9),
        legend.title = element_blank()) +
  scale_y_continuous(breaks = 0.01*seq(0, 100, 1),
                     labels = scales::percent_format(accuracy = 1)) +
  ylab("Government Spending (% of trend GDP)") + xlab("")

Switzerland, Germany, Netherlands

SNA_TABLE1 %>%
  filter(MEASURE == "VXVOB",
         LOCATION %in% c("NLD", "CHE", "DEU"),
         TRANSACT %in% c("B1_GE", "P3S13")) %>%
  year_to_date %>%
  left_join(SNA_TABLE1_var$LOCATION %>%
              setNames(c("LOCATION", "Location")), by = "LOCATION") %>%
  select(Location, date, TRANSACT, obsValue) %>%
  spread(TRANSACT, obsValue) %>%
  group_by(Location) %>%
  mutate(B1_GE_trend = log(B1_GE) %>% hpfilter(100000) %>% pluck("trend") %>% exp,
         P3S13_B1_GE_trend = P3S13 / B1_GE_trend,
         P3S13_B1_GE = P3S13 / B1_GE) %>%
  select(Location, date, P3S13_B1_GE_trend) %>%
  na.omit %>%
  ggplot() + geom_line(aes(x = date, y = P3S13_B1_GE_trend, color = Location)) +
  scale_color_manual(values = viridis(4)[1:3]) +
  theme_minimal() +
  scale_x_date(breaks = seq(1920, 2025, 10) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  theme(legend.position = c(0.15, 0.8),
        legend.title = element_blank()) +
  scale_y_continuous(breaks = 0.01*seq(0, 100, 1),
                     labels = scales::percent_format(accuracy = 1)) +
  ylab("Government Spending (% of trend GDP)") + xlab("")

Individual Government Spending / trend GDP

France, Germany, Italy

SNA_TABLE1 %>%
  filter(MEASURE == "VXVOB",
         LOCATION %in% c("FRA", "DEU", "ITA"),
         TRANSACT %in% c("B1_GE", "P31S13")) %>%
  year_to_date %>%
  left_join(SNA_TABLE1_var$LOCATION %>%
              setNames(c("LOCATION", "Location")), by = "LOCATION") %>%
  select(Location, date, TRANSACT, obsValue) %>%
  spread(TRANSACT, obsValue) %>%
  group_by(Location) %>%
  mutate(B1_GE_trend = log(B1_GE) %>% hpfilter(1000000) %>% pluck("trend") %>% exp,
         P31S13_B1_GE_trend = P31S13 / B1_GE_trend,
         P31S13_B1_GE = P31S13 / B1_GE) %>%
  select(Location, date, P31S13_B1_GE_trend) %>%
  na.omit %>%
  ggplot() + geom_line(aes(x = date, y = P31S13_B1_GE_trend, color = Location)) +
  scale_color_manual(values = viridis(4)[1:3]) +
  theme_minimal() +
  scale_x_date(breaks = seq(1920, 2025, 10) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  theme(legend.position = c(0.15, 0.9),
        legend.title = element_blank()) +
  scale_y_continuous(breaks = 0.01*seq(0, 100, 1),
                     labels = scales::percent_format(accuracy = 1)) +
  ylab("Individual Government Spending (% of trend GDP)") + xlab("")

United Kingdom, Japan, United States

SNA_TABLE1 %>%
  filter(MEASURE == "VXVOB",
         LOCATION %in% c("JPN", "GBR", "USA"),
         TRANSACT %in% c("B1_GE", "P31S13")) %>%
  year_to_date %>%
  left_join(SNA_TABLE1_var$LOCATION %>%
              setNames(c("LOCATION", "Location")), by = "LOCATION") %>%
  select(Location, date, TRANSACT, obsValue) %>%
  spread(TRANSACT, obsValue) %>%
  group_by(Location) %>%
  mutate(B1_GE_trend = log(B1_GE) %>% hpfilter(100000) %>% pluck("trend") %>% exp,
         P31S13_B1_GE_trend = P31S13 / B1_GE_trend,
         P31S13_B1_GE = P31S13 / B1_GE) %>%
  select(Location, date, P31S13_B1_GE_trend) %>%
  na.omit %>%
  ggplot() + geom_line(aes(x = date, y = P31S13_B1_GE_trend, color = Location)) +
  scale_color_manual(values = viridis(4)[1:3]) +
  theme_minimal() +
  scale_x_date(breaks = seq(1920, 2025, 10) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  theme(legend.position = c(0.85, 0.9),
        legend.title = element_blank()) +
  scale_y_continuous(breaks = 0.01*seq(0, 100, 1),
                     labels = scales::percent_format(accuracy = 1)) +
  ylab("Individual Government Spending (% of trend GDP)") + xlab("")

Switzerland, Germany, Netherlands

SNA_TABLE1 %>%
  filter(MEASURE == "VXVOB",
         LOCATION %in% c("NLD", "CHE", "DEU"),
         TRANSACT %in% c("B1_GE", "P31S13")) %>%
  year_to_date %>%
  left_join(SNA_TABLE1_var$LOCATION %>%
              setNames(c("LOCATION", "Location")), by = "LOCATION") %>%
  select(Location, date, TRANSACT, obsValue) %>%
  spread(TRANSACT, obsValue) %>%
  group_by(Location) %>%
  mutate(B1_GE_trend = log(B1_GE) %>% hpfilter(100000) %>% pluck("trend") %>% exp,
         P31S13_B1_GE_trend = P31S13 / B1_GE_trend,
         P31S13_B1_GE = P31S13 / B1_GE) %>%
  select(Location, date, P31S13_B1_GE_trend) %>%
  na.omit %>%
  ggplot() + geom_line(aes(x = date, y = P31S13_B1_GE_trend, color = Location)) +
  scale_color_manual(values = viridis(4)[1:3]) +
  theme_minimal() +
  scale_x_date(breaks = seq(1920, 2025, 10) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  theme(legend.position = c(0.15, 0.8),
        legend.title = element_blank()) +
  scale_y_continuous(breaks = 0.01*seq(0, 100, 1),
                     labels = scales::percent_format(accuracy = 1)) +
  ylab("Individual Government Spending (% of trend GDP)") + xlab("")

Collective Government Spending / trend GDP

France, Germany, Italy

SNA_TABLE1 %>%
  filter(MEASURE == "VXVOB",
         LOCATION %in% c("FRA", "DEU", "ITA"),
         TRANSACT %in% c("B1_GE", "P32S13")) %>%
  year_to_date %>%
  left_join(SNA_TABLE1_var$LOCATION %>%
              setNames(c("LOCATION", "Location")), by = "LOCATION") %>%
  select(Location, date, TRANSACT, obsValue) %>%
  spread(TRANSACT, obsValue) %>%
  group_by(Location) %>%
  mutate(B1_GE_trend = log(B1_GE) %>% hpfilter(1000000) %>% pluck("trend") %>% exp,
         P32S13_B1_GE_trend = P32S13 / B1_GE_trend,
         P32S13_B1_GE = P32S13 / B1_GE) %>%
  select(Location, date, P32S13_B1_GE_trend) %>%
  na.omit %>%
  ggplot() + geom_line(aes(x = date, y = P32S13_B1_GE_trend, color = Location)) +
  scale_color_manual(values = viridis(4)[1:3]) +
  theme_minimal() +
  scale_x_date(breaks = seq(1920, 2025, 10) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  theme(legend.position = c(0.15, 0.9),
        legend.title = element_blank()) +
  scale_y_continuous(breaks = 0.01*seq(0, 100, 1),
                     labels = scales::percent_format(accuracy = 1)) +
  ylab("Collective Government Spending (% of trend GDP)") + xlab("")

United Kingdom, Japan, United States

SNA_TABLE1 %>%
  filter(MEASURE == "VXVOB",
         LOCATION %in% c("JPN", "GBR", "USA"),
         TRANSACT %in% c("B1_GE", "P32S13")) %>%
  year_to_date %>%
  left_join(SNA_TABLE1_var$LOCATION %>%
              setNames(c("LOCATION", "Location")), by = "LOCATION") %>%
  select(Location, date, TRANSACT, obsValue) %>%
  spread(TRANSACT, obsValue) %>%
  group_by(Location) %>%
  mutate(B1_GE_trend = log(B1_GE) %>% hpfilter(100000) %>% pluck("trend") %>% exp,
         P32S13_B1_GE_trend = P32S13 / B1_GE_trend,
         P32S13_B1_GE = P32S13 / B1_GE) %>%
  select(Location, date, P32S13_B1_GE_trend) %>%
  na.omit %>%
  ggplot() + geom_line(aes(x = date, y = P32S13_B1_GE_trend, color = Location)) +
  scale_color_manual(values = viridis(4)[1:3]) +
  theme_minimal() +
  scale_x_date(breaks = seq(1920, 2025, 10) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  theme(legend.position = c(0.85, 0.9),
        legend.title = element_blank()) +
  scale_y_continuous(breaks = 0.01*seq(0, 100, 1),
                     labels = scales::percent_format(accuracy = 1)) +
  ylab("Collective Government Spending (% of trend GDP)") + xlab("")

Switzerland, Germany, Netherlands

SNA_TABLE1 %>%
  filter(MEASURE == "VXVOB",
         LOCATION %in% c("NLD", "CHE", "DEU"),
         TRANSACT %in% c("B1_GE", "P32S13")) %>%
  year_to_date %>%
  left_join(SNA_TABLE1_var$LOCATION %>%
              setNames(c("LOCATION", "Location")), by = "LOCATION") %>%
  select(Location, date, TRANSACT, obsValue) %>%
  spread(TRANSACT, obsValue) %>%
  group_by(Location) %>%
  mutate(B1_GE_trend = log(B1_GE) %>% hpfilter(100000) %>% pluck("trend") %>% exp,
         P32S13_B1_GE_trend = P32S13 / B1_GE_trend,
         P32S13_B1_GE = P32S13 / B1_GE) %>%
  select(Location, date, P32S13_B1_GE_trend) %>%
  na.omit %>%
  ggplot() + geom_line(aes(x = date, y = P32S13_B1_GE_trend, color = Location)) +
  scale_color_manual(values = viridis(4)[1:3]) +
  theme_minimal() +
  scale_x_date(breaks = seq(1920, 2025, 10) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  theme(legend.position = c(0.15, 0.8),
        legend.title = element_blank()) +
  scale_y_continuous(breaks = 0.01*seq(0, 100, 1),
                     labels = scales::percent_format(accuracy = 1)) +
  ylab("Collective Government Spending (% of trend GDP)") + xlab("")

GDP / trend GDP

France, Germany, Italy

SNA_TABLE1 %>%
  filter(MEASURE == "VXVOB",
         LOCATION %in% c("FRA", "DEU", "ITA"),
         TRANSACT %in% c("B1_GE", "B1_GE")) %>%
  year_to_date %>%
  left_join(SNA_TABLE1_var$LOCATION %>%
              setNames(c("LOCATION", "Location")), by = "LOCATION") %>%
  select(Location, date, TRANSACT, obsValue) %>%
  spread(TRANSACT, obsValue) %>%
  group_by(Location) %>%
  mutate(B1_GE_trend = log(B1_GE) %>% hpfilter(1000000) %>% pluck("trend") %>% exp,
         B1_GE_B1_GE_trend = B1_GE / B1_GE_trend,
         B1_GE_B1_GE = B1_GE / B1_GE) %>%
  select(Location, date, B1_GE_B1_GE_trend) %>%
  na.omit %>%
  ggplot() + geom_line(aes(x = date, y = B1_GE_B1_GE_trend, color = Location)) +
  scale_color_manual(values = viridis(4)[1:3]) +
  theme_minimal() +
  scale_x_date(breaks = seq(1920, 2025, 10) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  theme(legend.position = c(0.85, 0.9),
        legend.title = element_blank()) +
  scale_y_continuous(breaks = 0.01*seq(0, 150, 5),
                     labels = scales::percent_format(accuracy = 1)) +
  ylab("GDP (% of trend GDP)") + xlab("")

United Kingdom, Japan, United States

SNA_TABLE1 %>%
  filter(MEASURE == "VXVOB",
         LOCATION %in% c("JPN", "GBR", "USA"),
         TRANSACT %in% c("B1_GE", "B1_GE")) %>%
  year_to_date %>%
  left_join(SNA_TABLE1_var$LOCATION %>%
              setNames(c("LOCATION", "Location")), by = "LOCATION") %>%
  select(Location, date, TRANSACT, obsValue) %>%
  spread(TRANSACT, obsValue) %>%
  group_by(Location) %>%
  mutate(B1_GE_trend = log(B1_GE) %>% hpfilter(100000) %>% pluck("trend") %>% exp,
         B1_GE_B1_GE_trend = B1_GE / B1_GE_trend,
         B1_GE_B1_GE = B1_GE / B1_GE) %>%
  select(Location, date, B1_GE_B1_GE_trend) %>%
  na.omit %>%
  ggplot() + geom_line(aes(x = date, y = B1_GE_B1_GE_trend, color = Location)) +
  scale_color_manual(values = viridis(4)[1:3]) +
  theme_minimal() +
  scale_x_date(breaks = seq(1920, 2025, 10) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  theme(legend.position = c(0.85, 0.9),
        legend.title = element_blank()) +
  scale_y_continuous(breaks = 0.01*seq(0, 200, 10),
                     labels = scales::percent_format(accuracy = 1)) +
  ylab("GDP (% of trend GDP)") + xlab("")

Switzerland, Germany, Netherlands

SNA_TABLE1 %>%
  filter(MEASURE == "VXVOB",
         LOCATION %in% c("NLD", "CHE", "DEU"),
         TRANSACT %in% c("B1_GE", "B1_GE")) %>%
  year_to_date %>%
  left_join(SNA_TABLE1_var$LOCATION %>%
              setNames(c("LOCATION", "Location")), by = "LOCATION") %>%
  select(Location, date, TRANSACT, obsValue) %>%
  spread(TRANSACT, obsValue) %>%
  group_by(Location) %>%
  mutate(B1_GE_trend = log(B1_GE) %>% hpfilter(100000) %>% pluck("trend") %>% exp,
         B1_GE_B1_GE_trend = B1_GE / B1_GE_trend,
         B1_GE_B1_GE = B1_GE / B1_GE) %>%
  select(Location, date, B1_GE_B1_GE_trend) %>%
  na.omit %>%
  ggplot() + geom_line(aes(x = date, y = B1_GE_B1_GE_trend, color = Location)) +
  scale_color_manual(values = viridis(4)[1:3]) +
  theme_minimal() +
  scale_x_date(breaks = seq(1920, 2025, 10) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  theme(legend.position = c(0.15, 0.3),
        legend.title = element_blank()) +
  scale_y_continuous(breaks = 0.01*seq(0, 200, 5),
                     labels = scales::percent_format(accuracy = 1)) +
  ylab("GDP (% of trend GDP)") + xlab("")

Japan

GDP: Nominal and Real

SNA_TABLE1 %>%
  filter(LOCATION %in% c("JPN"),
         TRANSACT == "B1_GE",
         MEASURE %in% c("C", "V")) %>%
  mutate(date = paste0(obsTime, "-01-01") %>% as.Date) %>%
  left_join(SNA_TABLE1_var$LOCATION, by = "LOCATION") %>%
  left_join(SNA_TABLE1_var$MEASURE, by = "MEASURE") %>%
  group_by(LOCATION) %>%
  arrange(date) %>%
  select(date, obsValue, Measure) %>%
  ggplot(.) + 
  geom_line(aes(x = date, y = obsValue / 10^6, linetype = Measure, color = Measure)) + 
  theme_minimal() + xlab("") + ylab("") +
  scale_color_manual(values = viridis(4)[1:3]) +
  geom_rect(data = nber_recessions, 
            aes(xmin = Peak, xmax = Trough, ymin = -Inf, ymax = +Inf), 
            fill = 'grey', alpha = 0.5) +
  scale_x_date(breaks = seq(1960, 2020, 5) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y"),
               limits = c(1970, 2019) %>% paste0("-01-01") %>% as.Date) +
  scale_y_continuous(breaks = seq(0, 600, 50),
                     labels = dollar_format(suffix = " Tn", prefix = "¥", accuracy = 1)) +
  theme(legend.position = c(0.6, 0.20),
        legend.title = element_blank())

X and M

SNA_TABLE1 %>%
  year_to_date %>%
  filter(TRANSACT %in% c("P6", "P7", "B1_GE"),
         MEASURE == "V",
         LOCATION == "JPN",
         date >= as.Date("1980-01-01"),
         date <= as.Date("1995-01-01")) %>%
  select(date, TRANSACT, obsValue) %>%
  spread(TRANSACT, obsValue) %>%
  mutate(`Real Exports (% of Real GDP)` = P6 / B1_GE,
         `Real Imports (% of Real GDP)` = P7 / B1_GE) %>%
  na.omit %>%
  select(-P6, -P7, -B1_GE) %>%
  gather(variable, value, - date) %>%
  ggplot() + geom_line(aes(x = date, y = value, color = variable)) +
  scale_color_manual(values = viridis(4)[1:3]) +
  theme_minimal() +
  scale_x_date(breaks = seq(1920, 2025, 1) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  theme(legend.position = c(0.15, 0.9),
        legend.title = element_blank()) +
  scale_y_continuous(breaks = 0.01*seq(0, 60, 1),
                     labels = scales::percent_format(accuracy = 1)) +
  geom_vline(xintercept = as.Date("1985-09-22"), linetype = "dashed", color = viridis(4)[3]) +
  ylab("% of GDP (Japan)") + xlab("")

Nominal X and M

SNA_TABLE1 %>%
  year_to_date %>%
  filter(TRANSACT %in% c("P6", "P7", "B1_GE"),
         MEASURE == "C",
         LOCATION == "JPN",
         date >= as.Date("1980-01-01"),
         date <= as.Date("1995-01-01")) %>%
  select(date, TRANSACT, obsValue) %>%
  spread(TRANSACT, obsValue) %>%
  mutate(`Exports (% of GDP)` = P6 / B1_GE,
         `Imports (% of GDP)` = P7 / B1_GE) %>%
  na.omit %>%
  select(-P6, -P7, -B1_GE) %>%
  gather(variable, value, - date) %>%
  ggplot() + geom_line(aes(x = date, y = value, color = variable)) +
  scale_color_manual(values = viridis(4)[1:3]) +
  theme_minimal() +
  scale_x_date(breaks = seq(1920, 2025, 1) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  theme(legend.position = c(0.75, 0.9),
        legend.title = element_blank()) +
  scale_y_continuous(breaks = 0.01*seq(0, 60, 1),
                     labels = scales::percent_format(accuracy = 1)) +
  geom_vline(xintercept = as.Date("1985-09-22"), linetype = "dashed", color = viridis(4)[3]) +
  ylab("% of GDP (Japan)") + xlab("")

Around 1990

SNA_TABLE1 %>%
  filter(LOCATION %in% c("JPN"),
         TRANSACT %in% c("B1_GE", "P31S14", "P5"),
         MEASURE == "C") %>%
  year_to_date %>%
  left_join(SNA_TABLE1_var$TRANSACT, by = "TRANSACT") %>%
  group_by(LOCATION) %>%
  arrange(date) %>%
  select(date, obsValue, Transact) %>%
  ggplot(.) + 
  geom_line(aes(x = date, y = obsValue / 10^6, linetype = Transact, color = Transact)) + 
  theme_minimal() + xlab("") + ylab("") +
  scale_color_manual(values = viridis(4)[1:3]) +
  geom_rect(data = nber_recessions, 
            aes(xmin = Peak, xmax = Trough, ymin = -Inf, ymax = +Inf), 
            fill = 'grey', alpha = 0.5) +
  scale_x_date(breaks = seq(1960, 2020, 5) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y"),
               limits = c(1970, 2019) %>% paste0("-01-01") %>% as.Date) +
  scale_y_continuous(breaks = seq(0, 1000, 100),
                     labels = dollar_format(suffix = " Tn", prefix = "¥", accuracy = 1),
                     limits = c(0, 800)) +
  theme(legend.position = c(0.4, 0.9),
        legend.title = element_blank())

Investment

Investment Rate - Compare

SNA_TABLE1 %>%
  filter(obsTime == "2018",
         MEASURE == "C",
         grepl("^P51", TRANSACT) | TRANSACT == "B1_GE") %>%
  left_join(SNA_TABLE1_var$TRANSACT, by = "TRANSACT") %>%
  left_join(SNA_TABLE1_var$LOCATION, by = "LOCATION") %>%
  group_by(Location) %>%
  mutate(obsValue = 100*obsValue / obsValue[TRANSACT == "B1_GE"]) %>%
  filter(TRANSACT != "B1_GE") %>%
  select(Location, Transact, obsValue) %>%
  na.omit %>%
  spread(Transact, obsValue) %>%
  arrange(-`Gross fixed capital formation`) %>%
  mutate_at(vars(-1), funs(ifelse(is.na(.), NA, paste0(round(., 1), "%")))) %>%
  select(1, 4, 2, 3, 5, 6, 7) %>%
  {if (is_html_output()) print_table(.) else .}

Location	Computer software and databases	Computer Hardware	Computer software	Cultivated biological resources	Dwellings	Entertainment, literary and artistic originals
China (People’s Republic of)	NA	NA	NA	NA	NA	NA
Zambia	NA	NA	NA	NA	NA	NA
Indonesia	NA	NA	NA	1.8%	24%	NA
Korea	NA	NA	NA	NA	5.8%	NA
Turkey	NA	NA	NA	NA	NA	NA
Morocco	NA	NA	NA	NA	NA	NA
Ireland	NA	NA	NA	0%	2.3%	NA
Czech Republic	NA	NA	NA	0.1%	4.3%	NA
Switzerland	NA	NA	NA	0%	4.8%	NA
Sweden	NA	NA	NA	0%	5.2%	NA
Hungary	NA	NA	NA	0.1%	3%	NA
Estonia	NA	NA	NA	0.1%	4.5%	NA
Finland	NA	NA	NA	0%	7.3%	NA
Japan	1.9%	NA	1.9%	0%	3.1%	NA
Austria	NA	NA	NA	0%	4.5%	NA
Norway	NA	NA	NA	0%	5.4%	NA
Albania	NA	NA	NA	0.2%	10.8%	NA
Belgium	NA	NA	NA	0%	5.9%	NA
New Zealand	NA	0.8%	NA	NA	7.4%	NA
Australia	1.1%	0.3%	NA	0.2%	5.7%	0.1%
Singapore	NA	NA	NA	NA	3.6%	NA
France	NA	NA	NA	0%	6.4%	NA
Canada	NA	NA	NA	NA	7.6%	NA
Latvia	0.7%	0.9%	NA	0.1%	2.5%	0.2%
Mexico	0%	0.4%	0%	0%	6%	0%
Denmark	NA	NA	NA	0%	4.8%	NA
Hong Kong, China	NA	NA	NA	NA	NA	NA
Iceland	NA	NA	NA	0%	4.3%	NA
Israel	NA	NA	NA	0%	6.6%	NA
Colombia	0.3%	0.4%	NA	0.5%	5%	0%
Chile	NA	NA	NA	NA	NA	NA
Germany	NA	NA	NA	0%	6.3%	NA
Malta	NA	NA	NA	0%	5.1%	NA
Romania	NA	NA	NA	0%	2%	NA
Euro area (19 countries)	NA	NA	NA	0.1%	5.4%	NA
Saudi Arabia	NA	NA	NA	NA	NA	NA
Slovak Republic	NA	NA	NA	0.7%	3.4%	NA
Lithuania	NA	NA	NA	0.2%	2.7%	NA
Madagascar	NA	NA	NA	NA	NA	NA
United States	2.1%	0.6%	2.1%	NA	3.8%	0.4%
Netherlands	NA	NA	NA	0%	4.9%	NA
Croatia	NA	NA	NA	NA	NA	NA
Serbia	NA	NA	NA	0.3%	1.4%	NA
North Macedonia	NA	NA	NA	0.1%	3.9%	NA
Russia	NA	NA	NA	NA	NA	NA
Spain	NA	NA	NA	0.2%	5.4%	NA
Slovenia	NA	NA	NA	0.1%	2.1%	NA
Cyprus	NA	NA	NA	0%	6.5%	NA
Bulgaria	NA	NA	NA	0.2%	2.7%	NA
Poland	NA	NA	NA	0%	1.9%	NA
South Africa	NA	NA	NA	0.2%	1.8%	NA
Italy	NA	NA	NA	0%	4.1%	NA
United Kingdom	NA	NA	NA	0.1%	4%	NA
Costa Rica	NA	NA	NA	0.2%	3.5%	NA
Portugal	NA	NA	NA	0.2%	3.1%	NA
Luxembourg	NA	NA	NA	0%	3.8%	NA
Argentina	NA	NA	NA	NA	NA	NA
Greece	NA	NA	NA	0%	0.7%	NA

P51 - Investment

SNA_TABLE1 %>%
  filter(LOCATION %in% c("USA"),
         TRANSACT == "P51",
         MEASURE %in% c("C", "V")) %>%
  mutate(date = paste0(obsTime, "-01-01") %>% as.Date,
         obsValue = obsValue / 10^6) %>%
  left_join(SNA_TABLE1_var$LOCATION, by = "LOCATION") %>%
  left_join(SNA_TABLE1_var$MEASURE, by = "MEASURE") %>%
  group_by(LOCATION) %>%
  arrange(date) %>%
  select(date, obsValue, Measure) %>%
  ggplot(.) + geom_line(aes(x = date, y = obsValue, linetype = Measure, color = Measure)) + 
  theme_minimal() + xlab("") + ylab("") +
  scale_color_manual(values = viridis(4)[1:3]) +
  geom_rect(data = nber_recessions, 
            aes(xmin = Peak, xmax = Trough, ymin = -Inf, ymax = +Inf), 
            fill = 'grey', alpha = 0.5) +
  scale_x_date(breaks = seq(1960, 2020, 5) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y"),
               limits = c(1970, 2019) %>% paste0("-01-01") %>% as.Date) +
  scale_y_continuous(breaks = seq(0, 10, 0.5),
                     labels = dollar_format(suffix = "Tn", prefix = "$", accuracy = 0.1)) +
  theme(legend.position = c(0.3, 0.90),
        legend.title = element_blank())

P51N1113 - Machinery and equipment and weapon system

SNA_TABLE1 %>%
  filter(LOCATION %in% c("USA"),
         TRANSACT == "P51N1113",
         MEASURE %in% c("C", "V")) %>%
  mutate(date = paste0(obsTime, "-01-01") %>% as.Date,
         obsValue = obsValue / 10^6) %>%
  left_join(SNA_TABLE1_var$LOCATION, by = "LOCATION") %>%
  left_join(SNA_TABLE1_var$MEASURE, by = "MEASURE") %>%
  group_by(LOCATION) %>%
  arrange(date) %>%
  select(date, obsValue, Measure) %>%
  ggplot(.) + geom_line(aes(x = date, y = obsValue, linetype = Measure, color = Measure)) + 
  theme_minimal() + xlab("") + ylab("") +
  scale_color_manual(values = viridis(4)[1:3]) +
  geom_rect(data = nber_recessions, 
            aes(xmin = Peak, xmax = Trough, ymin = -Inf, ymax = +Inf), 
            fill = 'grey', alpha = 0.5) +
  scale_x_date(breaks = seq(1960, 2020, 5) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y"),
               limits = c(1970, 2019) %>% paste0("-01-01") %>% as.Date) +
  scale_y_continuous(breaks = seq(0, 2, 0.2),
                     labels = dollar_format(suffix = "Tn", prefix = "$", accuracy = 0.1)) +
  theme(legend.position = c(0.3, 0.90),
        legend.title = element_blank())

Net Exports

X - M, Total, Goods, Services

SNA_TABLE1 %>%
  filter(obsTime == "2018",
         MEASURE == "C",
         TRANSACT %in% c("B1_GE", "P61", "P71", "P62", "P72", "P6", "P7")) %>%
  left_join(SNA_TABLE1_var$LOCATION, by = "LOCATION") %>%
  left_join(SNA_TABLE1_var$MEASURE, by = "MEASURE") %>%
  group_by(LOCATION, Location) %>%
  mutate(obsValue = 100*obsValue / obsValue[TRANSACT == "B1_GE"]) %>%
  filter(TRANSACT != "B1_GE") %>%
  select(LOCATION, Location, TRANSACT, obsValue) %>%
  na.omit %>%
  spread(TRANSACT, obsValue) %>%
  mutate_at(vars(-1, -2), funs(ifelse(is.na(.), NA, paste0(round(., 1), "%")))) %>%
  mutate(Flag = gsub(" ", "-", str_to_lower(gsub(" ", "-", Location))),
         Flag = paste0('<img src="../../icon/flag/vsmall/', Flag, '.png" alt="Flag">')) %>%
  select(Flag, everything()) %>%
  {if (is_html_output()) datatable(., filter = 'top', rownames = F, escape = F) else .}

include_graphics3b("bib/oecd/SNA_TABLE1_X_M.png")

France, Germany, United Kingdom

NX

SNA_TABLE1 %>%
  filter(LOCATION %in% c("DEU", "FRA", "GBR"),
         MEASURE == "C",
         TRANSACT %in% c("B1_GE", "P61", "P71", "P62", "P72", "P6", "P7")) %>%
  year_to_date %>%
  left_join(SNA_TABLE1_var$LOCATION, by = "LOCATION") %>%
  left_join(SNA_TABLE1_var$MEASURE, by = "MEASURE") %>%
  group_by(Location, date) %>%
  mutate(obsValue = 100*obsValue / obsValue[TRANSACT == "B1_GE"]) %>%
  filter(TRANSACT != "B1_GE") %>%
  select(Location, TRANSACT, obsValue) %>%
  na.omit %>%
  spread(TRANSACT, obsValue) %>%
  mutate(NX = P6 - P7,
         `Goods NX` = P61 - P71,
         `Services NX` = P62 - P72) %>%
  select(Location, NX, `Goods NX`, `Services NX`) %>%
  arrange(-`Goods NX`) %>%
  na.omit %>%
  mutate(obsValue = `NX`/100) %>%
  left_join(colors, by = c("Location" = "country")) %>%
  ggplot() + geom_line(aes(x = date, y = obsValue, color = color)) + 
  ylab("Manufacturing Value Added (% of GDP)") + xlab("") +
  scale_color_identity() + theme_minimal() + add_3flags +
  scale_x_date(breaks = seq(1920, 2025, 5) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  scale_y_continuous(breaks = 0.01*seq(-60, 60, 1),
                     labels = scales::percent_format(accuracy = 1))

NX

SNA_TABLE1 %>%
  filter(LOCATION %in% c("DEU", "FRA"),
         MEASURE == "C",
         TRANSACT %in% c("B1_GE", "P61", "P71", "P62", "P72", "P6", "P7")) %>%
  year_to_date %>%
  left_join(SNA_TABLE1_var$LOCATION, by = "LOCATION") %>%
  group_by(Location, date) %>%
  mutate(obsValue = 100*obsValue / obsValue[TRANSACT == "B1_GE"]) %>%
  filter(TRANSACT != "B1_GE",
         date >= as.Date("1990-01-01")) %>%
  select(Location, TRANSACT, obsValue) %>%
  na.omit %>%
  spread(TRANSACT, obsValue) %>%
  left_join(tibble(Location = c("France", "Germany"),
                   LocationF = c("France", "Allemagne")), by = "Location") %>%
  mutate(NX = P6 - P7,
         `Goods NX` = P61 - P71,
         `Services NX` = P62 - P72) %>%
  na.omit %>%
  mutate(obsValue = `NX`/100) %>%
  left_join(colors, by = c("Location" = "country")) %>%
  ggplot() + geom_line(aes(x = date, y = obsValue, color = color)) + 
  ylab("Manufacturing Value Added (% of GDP)") + xlab("") +
  scale_color_identity() + theme_minimal() + add_2flags +
  scale_x_date(breaks = seq(1920, 2025, 5) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  scale_y_continuous(breaks = 0.01*seq(-60, 60, 1),
                     labels = scales::percent_format(accuracy = 1))

Goods NX

SNA_TABLE1 %>%
  filter(LOCATION %in% c("DEU", "FRA", "GBR"),
         MEASURE == "C",
         TRANSACT %in% c("B1_GE", "P61", "P71", "P62", "P72", "P6", "P7")) %>%
  year_to_date %>%
  left_join(SNA_TABLE1_var$LOCATION, by = "LOCATION") %>%
  group_by(Location, date) %>%
  mutate(obsValue = 100*obsValue / obsValue[TRANSACT == "B1_GE"]) %>%
  filter(TRANSACT != "B1_GE") %>%
  select(Location, TRANSACT, obsValue) %>%
  na.omit %>%
  spread(TRANSACT, obsValue) %>%
  mutate(NX = P6 - P7,
         `Goods NX` = P61 - P71,
         `Services NX` = P62 - P72) %>%
  select(Location, NX, `Goods NX`, `Services NX`) %>%
  arrange(-`Goods NX`) %>%
  na.omit %>%
  mutate(obsValue = `NX`/100) %>%
  left_join(colors, by = c("Location" = "country")) %>%
  ggplot() + geom_line(aes(x = date, y = obsValue, color = color)) + 
  ylab("Manufacturing Value Added (% of GDP)") + xlab("") +
  scale_color_identity() + theme_minimal() + add_3flags +
  scale_x_date(breaks = seq(1920, 2025, 5) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  scale_y_continuous(breaks = 0.01*seq(-60, 60, 1),
                     labels = scales::percent_format(accuracy = 1))

SNA_TABLE1 %>%
  filter(LOCATION %in% c("DEU", "FRA"),
         MEASURE == "C",
         TRANSACT %in% c("B1_GE", "P61", "P71", "P62", "P72", "P6", "P7")) %>%
  year_to_date %>%
  left_join(SNA_TABLE1_var$LOCATION, by = "LOCATION") %>%
  group_by(Location, date) %>%
  mutate(obsValue = 100*obsValue / obsValue[TRANSACT == "B1_GE"]) %>%
  filter(TRANSACT != "B1_GE",
         date >= as.Date("1990-01-01")) %>%
  select(Location, TRANSACT, obsValue) %>%
  na.omit %>%
  spread(TRANSACT, obsValue) %>%
  left_join(tibble(Location = c("France", "Germany"),
                   LocationF = c("France", "Allemagne")), by = "Location") %>%
  mutate(NX = P6 - P7,
         `Goods NX` = P61 - P71,
         `Services NX` = P62 - P72) %>%
  na.omit %>%
  mutate(obsValue = `Goods NX`/100) %>%
  left_join(colors, by = c("Location" = "country")) %>%
  ggplot() + geom_line(aes(x = date, y = obsValue, color = color)) + 
  ylab("Goods Net Exports (% of GDP)") + xlab("") +
  scale_color_identity() + theme_minimal() + add_2flags +
  scale_x_date(breaks = seq(1920, 2025, 5) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  scale_y_continuous(breaks = 0.01*seq(-60, 60, 1),
                     labels = scales::percent_format(accuracy = 1))

Services NX

SNA_TABLE1 %>%
  filter(LOCATION %in% c("DEU", "FRA", "GBR"),
         MEASURE == "C",
         TRANSACT %in% c("B1_GE", "P61", "P71", "P62", "P72", "P6", "P7")) %>%
  year_to_date %>%
  left_join(SNA_TABLE1_var$LOCATION, by = "LOCATION") %>%
  group_by(Location, date) %>%
  mutate(obsValue = 100*obsValue / obsValue[TRANSACT == "B1_GE"]) %>%
  filter(TRANSACT != "B1_GE") %>%
  select(Location, TRANSACT, obsValue) %>%
  na.omit %>%
  spread(TRANSACT, obsValue) %>%
  mutate(NX = P6 - P7,
         `Goods NX` = P61 - P71,
         `Services NX` = P62 - P72) %>%
  select(Location, NX, `Goods NX`, `Services NX`) %>%
  arrange(-`Goods NX`) %>%
  na.omit %>%
  mutate(obsValue = `Services NX`/100) %>%
  left_join(colors, by = c("Location" = "country")) %>%
  ggplot() + geom_line(aes(x = date, y = obsValue, color = color)) + 
  ylab("Services Net Exports (% of GDP)") + xlab("") +
  scale_color_identity() + theme_minimal() + add_3flags +
  scale_x_date(breaks = seq(1920, 2025, 5) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  scale_y_continuous(breaks = 0.01*seq(-60, 60, 1),
                     labels = scales::percent_format(accuracy = 1))

SNA_TABLE1 %>%
  filter(LOCATION %in% c("DEU", "FRA"),
         MEASURE == "C",
         TRANSACT %in% c("B1_GE", "P61", "P71", "P62", "P72", "P6", "P7")) %>%
  year_to_date %>%
  left_join(SNA_TABLE1_var$LOCATION, by = "LOCATION") %>%
  group_by(Location, date) %>%
  mutate(obsValue = 100*obsValue / obsValue[TRANSACT == "B1_GE"]) %>%
  filter(TRANSACT != "B1_GE",
         date >= as.Date("1990-01-01")) %>%
  select(Location, TRANSACT, obsValue) %>%
  na.omit %>%
  spread(TRANSACT, obsValue) %>%
  left_join(tibble(Location = c("France", "Germany"),
                   LocationF = c("France", "Allemagne")), by = "Location") %>%
  mutate(NX = P6 - P7,
         `Goods NX` = P61 - P71,
         `Services NX` = P62 - P72) %>%
  na.omit %>%
  ggplot() + geom_line(aes(x = date, y = `Services NX`/100, color = Location)) +
  scale_color_manual(values = viridis(4)[1:3]) +
  theme_minimal() +
  geom_hline(yintercept = 0, linetype = "dashed",  color = viridis(4)[3]) +
  scale_x_date(breaks = seq(1920, 2025, 2) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  theme(legend.position = c(0.15, 0.5),
        legend.title = element_blank()) +
  scale_y_continuous(breaks = 0.01*seq(-10, 100, 1),
                     labels = scales::percent_format(accuracy = 1)) +
  ylab("Exportations Nettes de Services (% of PIB)") + xlab("")

Deindustrialization and Financial Services

United Kingdom

SNA_TABLE1 %>%
  filter(LOCATION %in% c("GBR"),
         MEASURE == "C",
         TRANSACT %in% c("B1_GE", "P61", "P71", "P62", "P72", "P6", "P7")) %>%
  year_to_date %>%
  select(date, TRANSACT, obsValue) %>%
  spread(TRANSACT, obsValue) %>%
  transmute(date,
            NX = P6/B1_GE - P7/B1_GE,
            NX1 = P61/B1_GE - P71/B1_GE,
            NX2 = P62/B1_GE - P72/B1_GE) %>%
  gather(variable, value, -date) %>%
  mutate(variable = factor(variable, 
                           levels=c("NX2", "NX", "NX1"), 
                           labels=c("Services", "Biens et Services", "Biens"))) %>%
  na.omit %>%
  ggplot() + geom_line() + theme_minimal() +
  aes(x = date, y = value, color = variable) +
  scale_color_manual(values = viridis(4)[1:3]) +
  scale_x_date(breaks = seq(1920, 2025, 10) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  theme(legend.position = c(0.2, 0.9),
        legend.title = element_blank()) +
  scale_y_continuous(breaks = 0.01*seq(-10, 100, 1),
                     labels = scales::percent_format(accuracy = 1),
                     limits = c(-0.07, 0.06)) +
  labs(x = "",
       y = "Balance Commerciale, Royaume-Uni (% du PIB)")

France

SNA_TABLE1 %>%
  filter(LOCATION %in% c("FRA"),
         MEASURE == "C",
         TRANSACT %in% c("B1_GE", "P61", "P71", "P62", "P72", "P6", "P7")) %>%
  year_to_date %>%
  select(date, TRANSACT, obsValue) %>%
  spread(TRANSACT, obsValue) %>%
  transmute(date,
            NX = P6/B1_GE - P7/B1_GE,
            NX1 = P61/B1_GE - P71/B1_GE,
            NX2 = P62/B1_GE - P72/B1_GE) %>%
  gather(variable, value, -date) %>%
  mutate(variable = factor(variable, 
                           levels=c("NX2", "NX", "NX1"), 
                           labels=c("Services", "Biens et Services", "Biens"))) %>%
  na.omit %>%
  ggplot() + geom_line() + theme_minimal() +
  aes(x = date, y = value, color = variable) +
  scale_color_manual(values = viridis(4)[1:3]) +
  scale_x_date(breaks = seq(1920, 2025, 10) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  theme(legend.position = c(0.2, 0.9),
        legend.title = element_blank()) +
  scale_y_continuous(breaks = 0.01*seq(-10, 100, 1),
                     labels = scales::percent_format(accuracy = 1),
                     limits = c(-0.07, 0.06)) +
  labs(x = "",
       y = "Balance Commerciale, France (% du PIB)")

Imports, Exports

France

SNA_TABLE1 %>%
  filter(LOCATION == "FRA",
         MEASURE == "C",
         TRANSACT %in% c("B1_GE", "P61", "P71", "P62", "P72")) %>%
  left_join(SNA_TABLE1_var$TRANSACT, by = "TRANSACT") %>%
  year_to_date %>%
  group_by(date) %>%
  mutate(obsValue = obsValue / obsValue[TRANSACT == "B1_GE"]) %>%
  mutate(TRANSACT = factor(TRANSACT, 
                           levels=c("P71", "P61", "P72", "P62"), 
                           labels=c("Importations - Biens", "Exportations - Biens", 
                                    "Importations - Services", "Exportations - Services"))) %>%
  filter(TRANSACT != "B1_GE") %>%
  ggplot() + theme_minimal() + ylab("% du PIB") + xlab("") +
  geom_line(aes(x = date, y = obsValue, color = TRANSACT, linetype = TRANSACT)) +
  scale_color_manual(values = c(viridis(3)[1], viridis(3)[1], viridis(3)[2], viridis(3)[2])) +
  scale_linetype_manual(values = c("solid",  "dashed", "solid", "dashed")) +
  scale_x_date(breaks = seq(1920, 2025, 5) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  theme(legend.position = c(0.15, 0.85),
        legend.title = element_blank()) +
  scale_y_continuous(breaks = 0.01*seq(-60, 60, 2),
                     labels = scales::percent_format(accuracy = 1))

United States

SNA_TABLE1 %>%
  filter(LOCATION == "USA",
         MEASURE == "C",
         TRANSACT %in% c("B1_GE", "P61", "P71", "P62", "P72")) %>%
  left_join(SNA_TABLE1_var$TRANSACT, by = "TRANSACT") %>%
  year_to_date %>%
  group_by(date) %>%
  mutate(obsValue = obsValue / obsValue[TRANSACT == "B1_GE"]) %>%
  mutate(TRANSACT = factor(TRANSACT, 
                           levels=c("P71", "P61", "P72", "P62"), 
                           labels=c("Importations - Biens", "Exportations - Biens", 
                                    "Importations - Services", "Exportations - Services"))) %>%
  filter(TRANSACT != "B1_GE") %>%
  ggplot() + theme_minimal() + ylab("% of GDP") + xlab("") +
  geom_line(aes(x = date, y = obsValue, color = TRANSACT, linetype = TRANSACT)) +
  scale_color_manual(values = c(viridis(3)[1], viridis(3)[1], viridis(3)[2], viridis(3)[2])) +
  scale_linetype_manual(values = c("solid",  "dashed", "solid", "dashed")) +
  scale_x_date(breaks = seq(1920, 2025, 5) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  theme(legend.position = c(0.15, 0.85),
        legend.title = element_blank()) +
  scale_y_continuous(breaks = 0.01*seq(-60, 60, 2),
                     labels = scales::percent_format(accuracy = 1))

China

SNA_TABLE1 %>%
  filter(LOCATION == "CHN",
         MEASURE == "C",
         TRANSACT %in% c("B1_GE", "P61", "P71", "P62", "P72")) %>%
  left_join(SNA_TABLE1_var$TRANSACT, by = "TRANSACT") %>%
  year_to_date %>%
  group_by(date) %>%
  mutate(obsValue = obsValue / obsValue[TRANSACT == "B1_GE"]) %>%
  mutate(TRANSACT = factor(TRANSACT, 
                           levels=c("P71", "P61", "P72", "P62"), 
                           labels=c("Importations - Biens", "Exportations - Biens", 
                                    "Importations - Services", "Exportations - Services"))) %>%
  filter(TRANSACT != "B1_GE") %>%
  ggplot() + theme_minimal() + ylab("% of GDP") + xlab("") +
  geom_line(aes(x = date, y = obsValue, color = TRANSACT, linetype = TRANSACT)) +
  scale_color_manual(values = c(viridis(3)[1], viridis(3)[1], viridis(3)[2], viridis(3)[2])) +
  scale_linetype_manual(values = c("solid",  "dashed", "solid", "dashed")) +
  scale_x_date(breaks = seq(1920, 2025, 5) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  theme(legend.position = c(0.15, 0.85),
        legend.title = element_blank()) +
  scale_y_continuous(breaks = 0.01*seq(-60, 60, 2),
                     labels = scales::percent_format(accuracy = 1))

Germany External Demand

U.S., France, China

SNA_TABLE1 %>%
  filter(LOCATION %in% c("USA", "FRA", "CHN"),
         MEASURE == "C",
         TRANSACT %in% c("B1_GE", "P6", "P7")) %>%
  year_to_date %>%
  filter(date >= as.Date("1997-01-01")) %>%
  left_join(SNA_TABLE1_var$LOCATION, by = "LOCATION") %>%
  group_by(Location, date) %>%
  mutate(obsValue = 100*obsValue / obsValue[TRANSACT == "B1_GE"]) %>%
  select(Location, LOCATION, TRANSACT, obsValue) %>%
  spread(TRANSACT, obsValue) %>%
  select(Location, LOCATION, P6, P7) %>%
  mutate(obsValue = (P6 - P7)/100,
         Location = ifelse(LOCATION == "CHN", "China", Location)) %>%
  left_join(colors, by = c("Location" = "country")) %>%
  ggplot() + geom_line(aes(x = date, y = obsValue, color = color)) + 
  ylab("Manufacturing Value Added (% of GDP)") + xlab("") +
  scale_color_identity() + theme_minimal() + add_3flags +
  scale_x_date(breaks = seq(1920, 2025, 5) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  scale_y_continuous(breaks = 0.01*seq(-60, 60, 1),
                     labels = scales::percent_format(accuracy = 1))

Netherlands, UK, Italy

SNA_TABLE1 %>%
  filter(LOCATION %in% c("NLD", "GBR", "ITA"),
         MEASURE == "C",
         TRANSACT %in% c("B1_GE", "P6", "P7")) %>%
  year_to_date %>%
  filter(date >= as.Date("1997-01-01")) %>%
  left_join(SNA_TABLE1_var$LOCATION, by = "LOCATION") %>%
  group_by(Location, date) %>%
  mutate(obsValue = 100*obsValue / obsValue[TRANSACT == "B1_GE"]) %>%
  select(Location, TRANSACT, obsValue) %>%
  spread(TRANSACT, obsValue) %>%
  select(Location, P6, P7) %>%
  mutate(obsValue = (P6 - P7)/100) %>%
  left_join(colors, by = c("Location" = "country")) %>%
  ggplot() + geom_line(aes(x = date, y = obsValue, color = color)) + 
  ylab("Manufacturing Value Added (% of GDP)") + xlab("") +
  scale_color_identity() + theme_minimal() + add_3flags +
  scale_x_date(breaks = seq(1920, 2025, 5) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  scale_y_continuous(breaks = 0.01*seq(-60, 60, 1),
                     labels = scales::percent_format(accuracy = 1))

Austria, Poland, Switzerland

SNA_TABLE1 %>%
  filter(LOCATION %in% c("AUT", "POL", "CHE"),
         MEASURE == "C",
         TRANSACT %in% c("B1_GE", "P6", "P7")) %>%
  year_to_date %>%
  filter(date >= as.Date("1997-01-01")) %>%
  left_join(SNA_TABLE1_var$LOCATION, by = "LOCATION") %>%
  group_by(Location, date) %>%
  mutate(obsValue = 100*obsValue / obsValue[TRANSACT == "B1_GE"]) %>%
  select(Location, TRANSACT, obsValue) %>%
  spread(TRANSACT, obsValue) %>%
  select(Location, P6, P7) %>%
  mutate(obsValue = (P6 - P7)/100) %>%
  left_join(colors, by = c("Location" = "country")) %>%
  ggplot() + geom_line(aes(x = date, y = obsValue, color = color)) + 
  ylab("Manufacturing Value Added (% of GDP)") + xlab("") +
  scale_color_identity() + theme_minimal() + add_3flags +
  scale_x_date(breaks = seq(1920, 2025, 5) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  scale_y_continuous(breaks = 0.01*seq(-60, 60, 1),
                     labels = scales::percent_format(accuracy = 1))

Belgium, Spain, Czechia

SNA_TABLE1 %>%
  filter(LOCATION %in% c("BEL", "ESP", "CZE"),
         MEASURE == "C",
         TRANSACT %in% c("B1_GE", "P6", "P7")) %>%
  year_to_date %>%
  filter(date >= as.Date("1997-01-01")) %>%
  left_join(SNA_TABLE1_var$LOCATION, by = "LOCATION") %>%
  group_by(Location, date) %>%
  mutate(obsValue = 100*obsValue / obsValue[TRANSACT == "B1_GE"]) %>%
  select(Location, TRANSACT, obsValue) %>%
  spread(TRANSACT, obsValue) %>%
  select(Location, P6, P7) %>%
  mutate(obsValue = (P6 - P7)/100) %>%
  left_join(colors, by = c("Location" = "country")) %>%
  ggplot() + geom_line(aes(x = date, y = obsValue, color = color)) + 
  ylab("Manufacturing Value Added (% of GDP)") + xlab("") +
  scale_color_identity() + theme_minimal() + add_3flags +
  scale_x_date(breaks = seq(1920, 2025, 5) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  scale_y_continuous(breaks = 0.01*seq(-60, 60, 1),
                     labels = scales::percent_format(accuracy = 1))