National Accounts at a Glance - NAAG
Data - OECD
François Geerolf
Info
| source | dataset | .html | .RData |
|---|---|---|---|
| oecd | NAAG | 2024-04-15 | 2024-03-13 |
Data on saving
| source | dataset | .html | .RData |
|---|---|---|---|
| bdf | CFT | 2024-04-15 | 2024-04-15 |
| bea | T50100 | 2024-02-11 | 2024-03-13 |
| fred | saving | 2024-04-15 | 2024-04-15 |
| oecd | NAAG | 2024-04-15 | 2024-03-13 |
| wdi | NY.GDS.TOTL.ZS | 2022-09-27 | 2024-04-14 |
| wdi | NY.GNS.ICTR.ZS | 2022-09-27 | 2024-04-14 |
LAST_COMPILE
| LAST_COMPILE |
|---|
| 2024-04-16 |
Last
| obsTime | Nobs |
|---|---|
| 2023 | 2562 |
LOCATION
NAAG %>%
left_join(NAAG_var$LOCATION, by = "LOCATION") %>%
group_by(LOCATION, Location) %>%
summarise(Nobs = n()) %>%
mutate(Flag = gsub(" ", "-", str_to_lower(gsub(" ", "-", Location))),
Flag = paste0('<img src="../../icon/flag/vsmall/', Flag, '.png" alt="Flag">')) %>%
select(Flag, everything()) %>%
{if (is_html_output()) datatable(., filter = 'top', rownames = F, escape = F) else .}INDICATOR
Gross debt of general government, percentage of GDP - DBTS13GDP
France, Germany, United States, Italy
NAAG %>%
filter(LOCATION %in% c("FRA", "ITA", "USA", "DEU"),
INDICATOR == "DBTS13GDP") %>%
year_to_date %>%
left_join(NAAG_var$LOCATION, by = "LOCATION") %>%
group_by(LOCATION) %>%
arrange(date) %>%
select(date, LOCATION, Location, obsValue) %>%
left_join(colors, by = c("Location" = "country")) %>%
mutate(color = ifelse(LOCATION == "USA", color2, color)) %>%
ggplot(.) + geom_line(aes(x = date, y = obsValue / 100, color = color)) +
theme_minimal() + xlab("") + ylab("Gross debt of general government, % of GDP") +
scale_color_identity() + add_4flags +
scale_x_date(breaks = seq(1995, 2100, 5) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
scale_y_continuous(breaks = 0.01*seq(-10, 400, 10),
labels = percent_format(accuracy = 1)) +
theme(legend.position = c(0.5, 0.90),
legend.title = element_blank(),
legend.direction = "horizontal")
B6NS14_S15DEF - Real household net disposable income, deflated by household Final consumption, millions of national currency
Italy, Spain, France, Germany
1995-
NAAG %>%
filter(INDICATOR == "B6NS14_S15DEF",
LOCATION %in% c("ITA", "ESP", "FRA", "DEU")) %>%
left_join(NAAG_var$LOCATION, by = "LOCATION") %>%
year_to_date %>%
filter(date >= as.Date("1995-01-01")) %>%
select(Location, date, obsValue) %>%
left_join(colors, by = c("Location" = "country")) %>%
group_by(Location) %>%
mutate(obsValue = 100*100*obsValue/obsValue[1]) %>%
ggplot(.) + geom_line(aes(x = date, y = obsValue/100, color = color)) +
scale_color_identity() + add_4flags + theme_minimal() +
scale_x_date(breaks = seq(1920, 2100, 5) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
scale_y_continuous(breaks = seq(100, 2000, 10)) +
ylab("Real household net disposable income") + xlab("")
1999-
NAAG %>%
filter(INDICATOR == "B6NS14_S15DEF",
LOCATION %in% c("ITA", "ESP", "FRA", "DEU")) %>%
left_join(NAAG_var$LOCATION, by = "LOCATION") %>%
year_to_date %>%
filter(date >= as.Date("1999-01-01")) %>%
select(Location, date, obsValue) %>%
left_join(colors, by = c("Location" = "country")) %>%
group_by(Location) %>%
mutate(obsValue = 100*100*obsValue/obsValue[1]) %>%
ggplot(.) + geom_line(aes(x = date, y = obsValue/100, color = color)) +
scale_color_identity() + add_4flags + theme_minimal() +
scale_x_date(breaks = seq(1999, 2100, 5) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
scale_y_continuous(breaks = seq(10, 2000, 5)) +
ylab("Real household net disposable income") + xlab("")
B8NS14_S15SB6NS14 - Net household saving, percentage of households net disposable income
Italy, Spain, France, Germany
NAAG %>%
filter(INDICATOR == "B8NS14_S15SB6NS14",
LOCATION %in% c("ITA", "ESP", "FRA", "DEU")) %>%
left_join(NAAG_var$LOCATION, by = "LOCATION") %>%
year_to_date %>%
select(Location, date, obsValue) %>%
left_join(colors, by = c("Location" = "country")) %>%
ggplot(.) + geom_line(aes(x = date, y = obsValue / 100, color = color)) +
scale_color_identity() + add_4flags + theme_minimal() +
scale_x_date(breaks = seq(1920, 2100, 5) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
scale_y_continuous(breaks = 0.01*seq(-10, 40, 2),
labels = scales::percent_format(accuracy = 1)) +
ylab("Net household saving (% of household NDI)") + xlab("")
Net Saving - B8NS
With Germany, Frugal Four: Austria, Denmark, the Netherlands and Sweden
NAAG %>%
filter(INDICATOR == "B8NS",
LOCATION %in% c("AUT", "DNK", "NLD", "SWE", "DEU")) %>%
left_join(NAAG_var$LOCATION, by = "LOCATION") %>%
year_to_date %>%
select(Location, date, obsValue) %>%
left_join(colors, by = c("Location" = "country")) %>%
ggplot(.) + geom_line(aes(x = date, y = obsValue / 100, color = color)) +
scale_color_identity() + add_5flags + theme_minimal() +
scale_x_date(breaks = seq(1920, 2100, 5) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
scale_y_continuous(breaks = 0.01*seq(-10, 40, 2),
labels = scales::percent_format(accuracy = 1)) +
ylab("Net Saving (% of GDP)") + xlab("")
Frugal Four: Austria, Denmark, the Netherlands and Sweden
NAAG %>%
filter(INDICATOR == "B8NS",
LOCATION %in% c("AUT", "DNK", "NLD", "SWE")) %>%
left_join(NAAG_var$LOCATION, by = "LOCATION") %>%
year_to_date %>%
select(Location, date, obsValue) %>%
left_join(colors, by = c("Location" = "country")) %>%
ggplot(.) + geom_line(aes(x = date, y = obsValue / 100, color = color)) +
scale_color_identity() + add_4flags + theme_minimal() +
scale_x_date(breaks = seq(1920, 2100, 5) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
scale_y_continuous(breaks = 0.01*seq(-10, 40, 2),
labels = scales::percent_format(accuracy = 1)) +
ylab("Net Saving (% of GDP)") + xlab("")
Italy, Spain, France, Germany
NAAG %>%
filter(INDICATOR == "B8NS",
LOCATION %in% c("ITA", "ESP", "FRA", "DEU")) %>%
left_join(NAAG_var$LOCATION, by = "LOCATION") %>%
year_to_date %>%
select(Location, date, obsValue) %>%
left_join(colors, by = c("Location" = "country")) %>%
ggplot(.) + geom_line(aes(x = date, y = obsValue / 100, color = color)) +
scale_color_identity() + add_4flags + theme_minimal() +
scale_x_date(breaks = seq(1920, 2100, 5) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
scale_y_continuous(breaks = 0.01*seq(-10, 40, 2),
labels = scales::percent_format(accuracy = 1)) +
ylab("Net Saving (% of GDP)") + xlab("")
Net Saving of General Government - B8NS13S
With Germany, Frugal Four: Austria, Denmark, the Netherlands and Sweden
NAAG %>%
filter(INDICATOR == "B8NS13S",
LOCATION %in% c("AUT", "DNK", "NLD", "SWE", "DEU")) %>%
left_join(NAAG_var$LOCATION, by = "LOCATION") %>%
year_to_date %>%
select(Location, date, obsValue) %>%
left_join(colors, by = c("Location" = "country")) %>%
ggplot(.) + geom_line(aes(x = date, y = obsValue / 100, color = color)) +
scale_color_identity() + add_5flags + theme_minimal() +
scale_x_date(breaks = seq(1920, 2100, 2) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
scale_y_continuous(breaks = 0.01*seq(-10, 16, 1),
labels = scales::percent_format(accuracy = 0.1)) +
ylab("Net Lending, General Government (% of GDP)") + xlab("")
Frugal Four: Austria, Denmark, the Netherlands and Sweden
NAAG %>%
filter(INDICATOR == "B8NS13S",
LOCATION %in% c("AUT", "DNK", "NLD", "SWE")) %>%
left_join(NAAG_var$LOCATION, by = "LOCATION") %>%
year_to_date %>%
select(Location, date, obsValue) %>%
left_join(colors, by = c("Location" = "country")) %>%
ggplot(.) + geom_line(aes(x = date, y = obsValue / 100, color = color)) +
scale_color_identity() + add_4flags + theme_minimal() +
scale_x_date(breaks = seq(1920, 2100, 2) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
scale_y_continuous(breaks = 0.01*seq(-10, 16, 1),
labels = scales::percent_format(accuracy = 0.1)) +
ylab("Net Lending, General Government (% of GDP)") + xlab("")
Italy, Spain, France, Germany
NAAG %>%
filter(INDICATOR == "B8NS13S",
LOCATION %in% c("ITA", "ESP", "FRA", "DEU")) %>%
left_join(NAAG_var$LOCATION, by = "LOCATION") %>%
year_to_date %>%
select(Location, date, obsValue) %>%
left_join(colors, by = c("Location" = "country")) %>%
ggplot(.) + geom_line(aes(x = date, y = obsValue / 100, color = color)) +
scale_color_identity() + add_4flags + theme_minimal() +
scale_x_date(breaks = seq(1920, 2100, 5) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
scale_y_continuous(breaks = 0.01*seq(-10, 16, 1),
labels = scales::percent_format(accuracy = 0.1)) +
ylab("Net Lending, General Government (% of GDP)") + xlab("")
Terms of Trade - TOT
All
NAAG %>%
filter(LOCATION %in% c("FRA", "ITA", "DEU", "PRT"),
INDICATOR == "TOT") %>%
year_to_date %>%
left_join(NAAG_var$LOCATION, by = "LOCATION") %>%
left_join(colors, by = c("Location" = "country")) %>%
ggplot(.) + geom_line(aes(x = date, y = obsValue / 100, color = color)) +
theme_minimal() + xlab("") + ylab("Terms of Trade") +
scale_color_identity() + add_4flags +
scale_x_date(breaks = seq(1960, 2100, 5) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
scale_y_continuous(breaks = 0.01*seq(-10, 400, 10),
labels = percent_format(accuracy = 1)) +
theme(legend.position = c(0.5, 0.90),
legend.title = element_blank(),
legend.direction = "horizontal")
Household Debt (% of PDI)
All
NAAG %>%
filter(LOCATION %in% c("FRA", "ITA", "DEU", "PRT"),
INDICATOR == "DBTS14_S15NDI") %>%
year_to_date %>%
left_join(NAAG_var$LOCATION, by = "LOCATION") %>%
group_by(LOCATION) %>%
arrange(date) %>%
select(date, LOCATION, Location, obsValue) %>%
left_join(colors, by = c("Location" = "country")) %>%
ggplot(.) + geom_line(aes(x = date, y = obsValue / 100, color = color)) +
theme_minimal() + xlab("") + ylab("Household Debt, % of NDI") +
scale_color_identity() + add_4flags +
scale_x_date(breaks = seq(1960, 2100, 2) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
scale_y_continuous(breaks = 0.01*seq(-10, 400, 10),
labels = percent_format(accuracy = 1)) +
theme(legend.position = c(0.5, 0.90),
legend.title = element_blank(),
legend.direction = "horizontal")
2009-2019
NAAG %>%
year_to_date %>%
filter(LOCATION %in% c("FRA", "ITA", "DEU", "PRT"),
INDICATOR == "DBTS14_S15NDI",
date >= as.Date("2008-01-01")) %>%
left_join(NAAG_var$LOCATION, by = "LOCATION") %>%
group_by(LOCATION) %>%
arrange(date) %>%
select(date, LOCATION, Location, obsValue) %>%
left_join(colors, by = c("Location" = "country")) %>%
ggplot(.) + geom_line(aes(x = date, y = obsValue / 100, color = color)) +
scale_color_identity() + add_4flags +
theme_minimal() + xlab("") + ylab("Household Debt, % of NDI") +
scale_x_date(breaks = seq(1960, 2100, 1) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
scale_y_continuous(breaks = 0.01*seq(-10, 400, 10),
labels = percent_format(accuracy = 1)) +
theme(legend.position = c(0.5, 0.90),
legend.title = element_blank(),
legend.direction = "horizontal")
France, Germany
Net Saving - Total
NAAG %>%
filter(INDICATOR == "B8NS",
LOCATION %in% c("FRA", "DEU")) %>%
left_join(NAAG_var$LOCATION, by = "LOCATION") %>%
left_join(NAAG_var$INDICATOR, by = "INDICATOR") %>%
year_to_date %>%
arrange(Location) %>%
select(Indicator, Location, date, obsValue) %>%
left_join(colors, by = c("Location" = "country")) %>%
ggplot(.) + geom_line(aes(x = date, y = obsValue / 100, color = color)) +
scale_color_identity() + add_2flags + theme_minimal() +
scale_x_date(breaks = seq(1920, 2100, 5) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
scale_y_continuous(breaks = 0.01*seq(-7, 30, 2),
labels = scales::percent_format(accuracy = 0.1)) +
ylab("Net Saving, Total (% of GDP)") + xlab("")
Figure 1: Net saving , TOTAL (% of GDP)
Net Saving of General Government
NAAG %>%
filter(INDICATOR == "B8NS13S",
LOCATION %in% c("FRA", "DEU")) %>%
left_join(NAAG_var$LOCATION, by = "LOCATION") %>%
left_join(NAAG_var$INDICATOR, by = "INDICATOR") %>%
year_to_date %>%
filter(year(date) >= 1990) %>%
arrange(Location) %>%
select(Indicator, Location, date, obsValue) %>%
left_join(colors, by = c("Location" = "country")) %>%
ggplot(.) + geom_line(aes(x = date, y = obsValue / 100, color = color)) +
scale_color_identity() + add_2flags + theme_minimal() +
scale_x_date(breaks = seq(1920, 2100, 2) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
theme(legend.position = c(0.1, 0.9),
legend.title = element_blank()) +
scale_y_continuous(breaks = 0.01*seq(-7, 16, 0.5),
labels = scales::percent_format(accuracy = 0.1)) +
ylab("Net Lending, General Government (% of GDP)") + xlab("")
Figure 2: Net saving of General Government (% of GDP)
Corporate Saving
NAAG %>%
filter(INDICATOR == "B9S14_S15S",
LOCATION %in% c("FRA", "DEU")) %>%
left_join(NAAG_var$LOCATION, by = "LOCATION") %>%
left_join(NAAG_var$INDICATOR, by = "INDICATOR") %>%
year_to_date %>%
filter(year(date) >= 1990) %>%
arrange(Location) %>%
select(Indicator, Location, date, obsValue) %>%
left_join(colors, by = c("Location" = "country")) %>%
ggplot(.) + geom_line(aes(x = date, y = obsValue / 100, color = color)) +
scale_color_identity() + add_2flags + theme_minimal() +
scale_x_date(breaks = seq(1920, 2100, 2) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
theme(legend.position = c(0.1, 0.9),
legend.title = element_blank()) +
scale_y_continuous(breaks = 0.01*seq(-7, 16, 0.5),
labels = scales::percent_format(accuracy = 0.1)) +
ylab("Net lending/net borrowing, Households and NPISHs") + xlab("")
Figure 3: Corporate Saving
Net Lending / Net Borrowing - Total Economy
NAAG %>%
filter(INDICATOR == "B9S1S",
LOCATION %in% c("FRA", "DEU")) %>%
left_join(NAAG_var$LOCATION, by = "LOCATION") %>%
left_join(NAAG_var$INDICATOR, by = "INDICATOR") %>%
year_to_date %>%
filter(year(date) >= 1990) %>%
arrange(Location) %>%
select(Indicator, Location, date, obsValue) %>%
left_join(colors, by = c("Location" = "country")) %>%
ggplot(.) + geom_line(aes(x = date, y = obsValue / 100, color = color)) +
scale_color_identity() + add_2flags + theme_minimal() +
scale_x_date(breaks = seq(1920, 2100, 2) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
theme(legend.position = c(0.1, 0.9),
legend.title = element_blank()) +
scale_y_continuous(breaks = 0.01*seq(-7, 16, 0.5),
labels = scales::percent_format(accuracy = 0.1)) +
ylab("% of GDP") + xlab("")
Figure 4: Net lending/net borrowing, Total economy
Net lending/net borrowing, Corporations
NAAG %>%
filter(INDICATOR == "B9S11_S12S",
LOCATION %in% c("FRA", "DEU")) %>%
left_join(NAAG_var$LOCATION, by = "LOCATION") %>%
left_join(NAAG_var$INDICATOR, by = "INDICATOR") %>%
year_to_date %>%
filter(year(date) >= 1990) %>%
arrange(Location) %>%
select(Indicator, Location, date, obsValue) %>%
left_join(colors, by = c("Location" = "country")) %>%
ggplot(.) + geom_line(aes(x = date, y = obsValue / 100, color = color)) +
scale_color_identity() + add_2flags + theme_minimal() +
scale_x_date(breaks = seq(1920, 2100, 2) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
theme(legend.position = c(0.1, 0.9),
legend.title = element_blank()) +
scale_y_continuous(breaks = 0.01*seq(-7, 16, 0.5),
labels = scales::percent_format(accuracy = 0.1)) +
ylab("% of GDP") + xlab("")
Figure 5: Net lending/net borrowing, Corporations
Total net worth of households
NAAG %>%
filter(INDICATOR == "TOTNETWORTHS14_S15NDI",
LOCATION %in% c("FRA", "DEU")) %>%
left_join(NAAG_var$LOCATION, by = "LOCATION") %>%
left_join(NAAG_var$INDICATOR, by = "INDICATOR") %>%
year_to_date %>%
filter(year(date) >= 1990) %>%
arrange(Location) %>%
select(Indicator, Location, date, obsValue) %>%
left_join(colors, by = c("Location" = "country")) %>%
ggplot(.) + geom_line(aes(x = date, y = obsValue / 100, color = color)) +
scale_color_identity() + add_2flags + theme_minimal() +
scale_x_date(breaks = seq(1920, 2100, 2) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
theme(legend.position = c(0.1, 0.9),
legend.title = element_blank()) +
scale_y_continuous(breaks = 0.01*seq(0, 900, 20),
labels = scales::percent_format(accuracy = 1)) +
ylab("% of Disposable Income") + xlab("")
Household housing consumption, percentage of households net adjusted disposable income
NAAG %>%
filter(INDICATOR == "P040B7NS14_S15",
LOCATION %in% c("FRA", "DEU")) %>%
left_join(NAAG_var$LOCATION, by = "LOCATION") %>%
left_join(NAAG_var$INDICATOR, by = "INDICATOR") %>%
year_to_date %>%
filter(year(date) >= 1990) %>%
arrange(Location) %>%
select(Indicator, Location, date, obsValue) %>%
left_join(colors, by = c("Location" = "country")) %>%
ggplot(.) + geom_line(aes(x = date, y = obsValue / 100, color = color)) +
scale_color_identity() + add_2flags + theme_minimal() +
scale_x_date(breaks = seq(1920, 2100, 2) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
theme(legend.position = c(0.1, 0.9),
legend.title = element_blank()) +
scale_y_continuous(breaks = 0.01*seq(-7, 25, 0.5),
labels = scales::percent_format(accuracy = 0.1)) +
ylab("% of GDP") + xlab("")
Household savings (% of household disposable income)
NAAG %>%
filter(INDICATOR == "B8NS14_S15SB6NS14",
LOCATION %in% c("FRA", "DEU")) %>%
left_join(NAAG_var$LOCATION, by = "LOCATION") %>%
left_join(NAAG_var$INDICATOR, by = "INDICATOR") %>%
year_to_date %>%
filter(year(date) >= 1990) %>%
arrange(Location) %>%
select(Indicator, Location, date, obsValue) %>%
left_join(colors, by = c("Location" = "country")) %>%
ggplot(.) + geom_line(aes(x = date, y = obsValue / 100, color = color)) +
scale_color_identity() + add_2flags + theme_minimal() +
scale_x_date(breaks = seq(1920, 2100, 2) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
theme(legend.position = c(0.1, 0.9),
legend.title = element_blank()) +
scale_y_continuous(breaks = 0.01*seq(-7, 16, 0.5),
labels = scales::percent_format(accuracy = 0.1)) +
ylab("% of GDP") + xlab("")
France, Germany, Japan, United States
Net Saving Rate (% of GDP)
nber_recessions_extract <- nber_recessions %>%
filter(Peak >= as.Date("1970-01-01"))
NAAG %>%
filter(LOCATION %in% c("FRA", "USA", "DEU", "JPN"),
INDICATOR == "B8NS") %>%
year_to_date %>%
left_join(NAAG_var$LOCATION, by = "LOCATION") %>%
group_by(LOCATION) %>%
arrange(date) %>%
left_join(colors, by = c("Location" = "country")) %>%
ggplot(.) + geom_line(aes(x = date, y = obsValue / 100, color = color)) +
scale_color_identity() + add_4flags +
theme_minimal() + xlab("") + ylab("Net saving rate, % of GDP") +
geom_rect(data = nber_recessions_extract,
aes(xmin = Peak, xmax = Trough, ymin = -Inf, ymax = +Inf),
fill = 'grey', alpha = 0.5) +
scale_x_date(breaks = seq(1960, 2100, 5) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
scale_y_continuous(breaks = 0.01*seq(-10, 50, 2),
labels = percent_format(accuracy = 1))
Net saving of General Government (% of GDP)
nber_recessions_extract <- nber_recessions %>%
filter(Peak >= as.Date("1970-01-01"))
NAAG %>%
filter(LOCATION %in% c("FRA", "USA", "DEU", "JPN"),
INDICATOR == "B8NS13S") %>%
year_to_date %>%
left_join(NAAG_var$LOCATION, by = "LOCATION") %>%
group_by(LOCATION) %>%
arrange(date) %>%
left_join(colors, by = c("Location" = "country")) %>%
ggplot(.) + geom_line(aes(x = date, y = obsValue / 100, color = color)) +
scale_color_identity() + add_4flags +
theme_minimal() + xlab("") + ylab("Net saving of General Government, % of GDP") +
geom_rect(data = nber_recessions_extract,
aes(xmin = Peak, xmax = Trough, ymin = -Inf, ymax = +Inf),
fill = 'grey', alpha = 0.5) +
scale_x_date(breaks = seq(1960, 2100, 5) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
scale_y_continuous(breaks = 0.01*seq(-20, 50, 1),
labels = percent_format(accuracy = 1)) +
theme(legend.position = c(0.4, 0.15),
legend.title = element_blank(),
legend.direction = "horizontal")
Net lending/net borrowing, Corporations, percentage of GDP (% of GDP)
nber_recessions_extract <- nber_recessions %>%
filter(Peak >= as.Date("1970-01-01"))
NAAG %>%
filter(LOCATION %in% c("FRA", "USA", "DEU", "JPN"),
INDICATOR == "B9S11_S12S") %>%
year_to_date %>%
left_join(NAAG_var$LOCATION, by = "LOCATION") %>%
left_join(colors, by = c("Location" = "country")) %>%
ggplot(.) + geom_line(aes(x = date, y = obsValue / 100, color = color)) +
scale_color_identity() + add_4flags +
theme_minimal() + xlab("") + ylab("Net lending, Corporations, % of GDP") +
scale_x_date(breaks = seq(1960, 2100, 5) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
scale_y_continuous(breaks = 0.01*seq(-20, 50, 1),
labels = percent_format(accuracy = 1)) +
geom_rect(data = nber_recessions_extract,
aes(xmin = Peak, xmax = Trough, ymin = -Inf, ymax = +Inf),
fill = 'grey', alpha = 0.5) +
theme(legend.position = c(0.3, 0.90),
legend.title = element_blank(),
legend.direction = "horizontal")
Net household saving, % of households net disposable income
nber_recessions_extract <- nber_recessions %>%
filter(Peak >= as.Date("1970-01-01"))
NAAG %>%
filter(LOCATION %in% c("FRA", "USA", "DEU", "JPN"),
INDICATOR == "B8NS14_S15SB6NS14") %>%
year_to_date %>%
left_join(NAAG_var$LOCATION, by = "LOCATION") %>%
left_join(colors, by = c("Location" = "country")) %>%
ggplot(.) + geom_line(aes(x = date, y = obsValue / 100, color = color)) +
scale_color_identity() + add_4flags +
theme_minimal() + xlab("") + ylab("Net household saving, % of households NDI") +
geom_rect(data = nber_recessions_extract,
aes(xmin = Peak, xmax = Trough, ymin = -Inf, ymax = +Inf),
fill = 'grey', alpha = 0.5) +
scale_x_date(breaks = seq(1960, 2100, 5) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
scale_y_continuous(breaks = 0.01*seq(-20, 50, 1),
labels = percent_format(accuracy = 1)) +
theme(legend.position = c(0.4, 0.10),
legend.title = element_blank(),
legend.direction = "horizontal")
Gross fixed capital formation, % of GDP
nber_recessions_extract <- nber_recessions %>%
filter(Peak >= as.Date("1970-01-01"))
NAAG %>%
filter(LOCATION %in% c("FRA", "USA", "DEU", "JPN"),
INDICATOR == "P51S") %>%
year_to_date %>%
mutate(value = obsValue / 100) %>%
left_join(NAAG_var$LOCATION, by = "LOCATION") %>%
left_join(colors, by = c("Location" = "country")) %>%
ggplot(.) + geom_line(aes(x = date, y = obsValue / 100, color = color)) +
scale_color_identity() + add_4flags +
theme_minimal() + xlab("") + ylab("Gross fixed capital formation (% of GDP)") +
geom_rect(data = nber_recessions_extract,
aes(xmin = Peak, xmax = Trough, ymin = -Inf, ymax = +Inf),
fill = 'grey', alpha = 0.5) +
scale_x_date(breaks = seq(1960, 2100, 5) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
scale_y_continuous(breaks = 0.01*seq(-20, 50, 2),
labels = percent_format(accuracy = 1)) +
theme(legend.position = c(0.8, 0.80),
legend.title = element_blank())
Net lending/net borrowing, Total economy, % of GDP
nber_recessions_extract <- nber_recessions %>%
filter(Peak >= as.Date("1970-01-01"))
NAAG %>%
filter(LOCATION %in% c("FRA", "USA", "DEU", "JPN"),
INDICATOR == "B9S1S") %>%
year_to_date %>%
mutate(value = obsValue / 100) %>%
left_join(NAAG_var$LOCATION, by = "LOCATION") %>%
left_join(colors, by = c("Location" = "country")) %>%
ggplot(.) + geom_line(aes(x = date, y = obsValue / 100, color = color)) +
scale_color_identity() + add_4flags +
theme_minimal() + xlab("") + ylab("Net lending, Total economy, % of GDP") +
geom_rect(data = nber_recessions_extract,
aes(xmin = Peak, xmax = Trough, ymin = -Inf, ymax = +Inf),
fill = 'grey', alpha = 0.5) +
scale_x_date(breaks = seq(1960, 2100, 5) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
scale_y_continuous(breaks = 0.01*seq(-20, 50, 1),
labels = percent_format(accuracy = 1)) +
theme(legend.position = c(0.2, 0.80),
legend.title = element_blank())
Transport Equipment (% of total GFCF)
nber_recessions_extract <- nber_recessions %>%
filter(Peak >= as.Date("1970-01-01"))
NAAG %>%
filter(LOCATION %in% c("FRA", "USA", "DEU", "JPN"),
INDICATOR == "P51N11131SP51") %>%
year_to_date %>%
mutate(value = obsValue / 100) %>%
left_join(NAAG_var$LOCATION, by = "LOCATION") %>%
left_join(colors, by = c("Location" = "country")) %>%
ggplot(.) + geom_line(aes(x = date, y = obsValue / 100, color = color)) +
scale_color_identity() + add_4flags +
theme_minimal() + xlab("") + ylab("Transport equipment, % of total GFCF") +
geom_rect(data = nber_recessions_extract,
aes(xmin = Peak, xmax = Trough, ymin = -Inf, ymax = +Inf),
fill = 'grey', alpha = 0.5) +
scale_x_date(breaks = seq(1960, 2100, 5) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
scale_y_continuous(breaks = 0.01*seq(0, 50, 1),
labels = percent_format(accuracy = 1))