source | dataset | Title | Download | Compile |
---|---|---|---|---|
oecd | SNA_TABLE2 | Disposable income and net lending - net borrowing | 2024-04-11 | [2024-07-01] |
eurostat | nama_10_a10 | Gross value added and income by A*10 industry breakdowns | 2024-06-30 | [2024-06-23] |
eurostat | nama_10_a10_e | Employment by A*10 industry breakdowns | 2024-06-30 | [2024-06-23] |
eurostat | nama_10_gdp | GDP and main components (output, expenditure and income) | 2024-06-30 | [2024-06-24] |
eurostat | nama_10_lp_ulc | Labour productivity and unit labour costs | 2024-06-30 | [2024-06-24] |
eurostat | namq_10_a10 | Gross value added and income A*10 industry breakdowns | 2024-06-30 | [2024-06-24] |
eurostat | namq_10_a10_e | Employment A*10 industry breakdowns | 2024-06-30 | [2024-06-24] |
eurostat | namq_10_gdp | GDP and main components (output, expenditure and income) | 2024-06-30 | [2024-06-24] |
eurostat | namq_10_lp_ulc | Labour productivity and unit labour costs | 2024-06-30 | [2024-06-24] |
eurostat | namq_10_pc | Main GDP aggregates per capita | 2024-06-30 | [2024-06-24] |
eurostat | nasa_10_nf_tr | Non-financial transactions | 2024-06-30 | [2024-06-24] |
eurostat | nasq_10_nf_tr | Non-financial transactions | 2024-06-30 | [2024-06-24] |
fred | gdp | Gross Domestic Product | 2024-06-30 | [2024-06-30] |
oecd | QNA | Quarterly National Accounts, Per Capita | 2024-06-30 | [2024-06-06] |
oecd | SNA_TABLE1 | Gross domestic product (GDP) | 2024-06-30 | [2024-07-01] |
oecd | SNA_TABLE14A | Non-financial accounts by sectors | 2024-06-30 | [2024-07-01] |
oecd | SNA_TABLE6A | Value added and its components by activity, ISIC rev4 | 2024-06-30 | [2024-07-01] |
wdi | NE.RSB.GNFS.ZS | External balance on goods and services (% of GDP) | 2024-04-14 | [2024-06-20] |
wdi | NY.GDP.MKTP.CD | GDP (current USD) | 2024-05-06 | [2024-06-20] |
wdi | NY.GDP.MKTP.PP.CD | GDP, PPP (current international D) | 2024-04-14 | [2024-06-20] |
wdi | NY.GDP.PCAP.CD | GDP per capita (current USD) | 2024-04-22 | [2024-06-20] |
wdi | NY.GDP.PCAP.KD | GDP per capita (constant 2015 USD) | 2024-05-06 | [2024-06-20] |
wdi | NY.GDP.PCAP.PP.CD | GDP per capita, PPP (current international D) | 2024-04-22 | [2024-06-20] |
wdi | NY.GDP.PCAP.PP.KD | GDP per capita, PPP (constant 2011 international D) | 2024-05-06 | [2024-06-20] |
Disposable income and net lending - net borrowing
Data - OECD
Info
LAST_COMPILE
COMPILE_TIME |
---|
2024-07-01 |
Last
obsTime | Nobs |
---|---|
2023 | 15 |
Layout
- OECD Website. html
TRANSACT
Code
%>%
SNA_TABLE2 left_join(SNA_TABLE2_var$TRANSACT, by = "TRANSACT") %>%
group_by(TRANSACT, Transact) %>%
summarise(Nobs = n()) %>%
arrange(-Nobs) %>%
print_table_conditional()
TRANSACT | Transact | Nobs |
---|---|---|
B1_GS1 | Gross domestic product | 24088 |
B5_GS1 | Gross national income at market prices | 18208 |
B5_NS1 | Net national income at market prices | 18008 |
GDIS1 | Gross domestic income | 16179 |
B6NS1 | Net national disposable income | 14847 |
B6GS1 | Gross national disposable income | 14617 |
D5_D7NFRS2 | Net current transfers from the rest of the world | 14520 |
D1_D4NFRS2 | Net primary incomes from the rest of the world | 14388 |
D1_D4TOS2 | Primary incomes payable to the rest of the world | 14309 |
D5_D7TOS2 | Current transfers payable to the rest of the world | 14075 |
D5_D7FRS2 | Current transfers receivable from the rest of the world | 13953 |
D1_D4FRS2 | Primary incomes receivable from the rest of the world | 13879 |
K1MS1 | Consumption of fixed capital | 12180 |
P3S1 | Final consumption expenditures | 10434 |
P5S1 | Gross capital formation | 10389 |
B8NS1 | Saving, net | 9456 |
B9S1 | Net lending/net borrowing | 9437 |
K1S1 | Consumption of fixed capital, capital account | 9015 |
D9TOS2 | Capital transfers payable to the rest of the world | 8602 |
D9NFRS2 | Net capital transfers from the rest of the world | 8521 |
D9FRS2 | Capital transfers receivable from the rest of the world | 8186 |
K2S1 | Acquisitions less disposals of non-financial non-produced assets | 7206 |
D8S1 | Adjustment for the change in net equity of households in pension funds | 6373 |
TGLS1 | Trading gain or loss | 3956 |
B1_GE | Gross domestic product (expenditure approach) | 2771 |
MEASURE
Code
%>%
SNA_TABLE2 left_join(SNA_TABLE2_var$MEASURE, by = "MEASURE") %>%
group_by(MEASURE, Measure) %>%
summarise(Nobs = n()) %>%
print_table_conditional()
MEASURE | Measure | Nobs |
---|---|---|
C | Current prices | 45273 |
CPC | Current prices, current PPPs | 42245 |
CXC | Current prices, current exchange rates | 42958 |
HCPC | Per head, current prices, current PPPs | 3815 |
HVPVOB | Per head, constant prices, constant PPPs, OECD base year | 1279 |
PVP | Previous year prices and previous year PPPs | 1868 |
V | Constant prices, national base year | 17657 |
VOB | Constant prices, OECD base year | 18320 |
VP | Constant prices, previous year prices | 552 |
VPCOB | Current prices, constant PPPs, OECD base year | 42479 |
VPVOB | Constant prices, constant PPPs, OECD base year | 18375 |
VXCOB | Current prices, constant exchange rates, OECD base year | 42479 |
VXVOB | Constant prices, constant exchange rates, OECD base year | 18375 |
XVP | Previous year prices and previous year exchange rates | 1922 |
LOCATION
Code
%>%
SNA_TABLE2 left_join(SNA_TABLE2_var$LOCATION, by = "LOCATION") %>%
group_by(LOCATION, Location) %>%
summarise(Nobs = n()) %>%
arrange(-Nobs) %>%
mutate(Flag = gsub(" ", "-", str_to_lower(gsub(" ", "-", Location))),
Flag = paste0('<img src="../../icon/flag/vsmall/', Flag, '.png" alt="Flag">')) %>%
select(Flag, everything()) %>%
if (is_html_output()) datatable(., filter = 'top', rownames = F, escape = F) else .} {
B1_GE - Gross domestic product
All Decomposition
Code
%>%
SNA_TABLE2 filter(LOCATION %in% c("FRA", "DEU", "USA", "GBR"),
== "2018",
obsTime == "C") %>%
MEASURE select(LOCATION, TRANSACT, obsValue) %>%
left_join(SNA_TABLE2_var$TRANSACT %>%
setNames(c("TRANSACT", "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
Code
%>%
SNA_TABLE2 filter(TRANSACT == "B1_GE",
== "C") %>%
MEASURE left_join(SNA_TABLE2_var$LOCATION %>%
setNames(c("LOCATION", "Location")),
by = "LOCATION") %>%
group_by(LOCATION, UNIT, Location) %>%
summarise(year_first = first(obsTime),
year_last = last(obsTime),
value_last = last(round(obsValue))) %>%
mutate(Loc = gsub(" ", "-", str_to_lower(Location)),
Loc = paste0('<img src="../../bib/flags/vsmall/', Loc, '.png" alt="Flag">')) %>%
select(Loc, everything()) %>%
if (is_html_output()) datatable(., filter = 'top', rownames = F, escape = F) else .} {
All
US and France
Code
%>%
SNA_TABLE2 filter(obsTime == "2018",
== "C",
MEASURE %in% c("USA", "FRA")) %>%
LOCATION select(LOCATION, TRANSACT, obsValue) %>%
left_join(SNA_TABLE2_var$TRANSACT %>%
setNames(c("TRANSACT", "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()) print_table(.) else .} {
TRANSACT | Transact | FRA | USA |
---|---|---|---|
B1_GE | Gross domestic product (expenditure approach) | 100 % | 100 % |
B1_GS1 | Gross domestic product | 100 % | 100 % |
B5_GS1 | Gross national income at market prices | 102.3 % | 101.1 % |
B5_NS1 | Net national income at market prices | 84.1 % | 85.1 % |
B6GS1 | Gross national disposable income | 100.3 % | 100.4 % |
B6NS1 | Net national disposable income | 82.1 % | 84.4 % |
B8NS1 | Saving, net | 5 % | 3.1 % |
B9S1 | Net lending/net borrowing | -0.7 % | -2.5 % |
D1_D4FRS2 | Primary incomes receivable from the rest of the world | 7.6 % | 5.5 % |
D1_D4NFRS2 | Net primary incomes from the rest of the world | 2.3 % | 1.4 % |
D1_D4TOS2 | Primary incomes payable to the rest of the world | 5.3 % | 4.1 % |
D5_D7FRS2 | Current transfers receivable from the rest of the world | 1.1 % | 0.7 % |
D5_D7NFRS2 | Net current transfers from the rest of the world | -2 % | -0.7 % |
D5_D7TOS2 | Current transfers payable to the rest of the world | 3.1 % | 1.4 % |
D8S1 | Adjustment for the change in net equity of households in pension funds | 0 % | 0 % |
D9FRS2 | Capital transfers receivable from the rest of the world | 0.1 % | 0 % |
D9NFRS2 | Net capital transfers from the rest of the world | 0 % | 0 % |
D9TOS2 | Capital transfers payable to the rest of the world | 0 % | 0 % |
GDIS1 | Gross domestic income | 100 % | 100.6 % |
K1MS1 | Consumption of fixed capital | 18.2 % | 16 % |
K1S1 | Consumption of fixed capital, capital account | 18.2 % | 16 % |
K2S1 | Acquisitions less disposals of non-financial non-produced assets | 0 % | 0 % |
P3S1 | Final consumption expenditures | 77.2 % | 81.3 % |
P5S1 | Gross capital formation | 23.9 % | 21.6 % |
B6GS1 - Gross National Disposable Income / GDP
France, Germany, Italy
Code
%>%
SNA_TABLE2 filter(MEASURE == "C",
%in% c("FRA", "DEU", "ITA"),
LOCATION %in% c("B1_GE", "B6GS1")) %>%
TRANSACT %>%
year_to_date left_join(SNA_TABLE2_var$LOCATION, by = "LOCATION") %>%
select(Location, date, TRANSACT, obsValue) %>%
spread(TRANSACT, obsValue) %>%
group_by(Location) %>%
mutate(obsValue = B6GS1 / B1_GE) %>%
select(Location, date, obsValue) %>%
%>%
na.omit left_join(colors, by = c("Location" = "country")) %>%
ggplot() + geom_line(aes(x = date, y = obsValue, color = color)) +
scale_color_identity() + theme_minimal() + add_3flags +
scale_x_date(breaks = seq(1920, 2025, 10) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
scale_y_continuous(breaks = 0.01*seq(-10, 200, 1),
labels = scales::percent_format(accuracy = 1)) +
ylab("Gross National Disposable Income (% of GDP)") + xlab("")
France, Germany, Belgium, Spain, Netherlands, Italy
All
Code
%>%
SNA_TABLE2 filter(MEASURE == "C",
%in% c("FRA", "DEU", "BEL", "ESP", "NLD", "ITA"),
LOCATION %in% c("B1_GE", "B6GS1")) %>%
TRANSACT %>%
year_to_date left_join(SNA_TABLE2_var$LOCATION, by = "LOCATION") %>%
select(Location, date, TRANSACT, obsValue) %>%
spread(TRANSACT, obsValue) %>%
group_by(Location) %>%
mutate(obsValue = B6GS1 / B1_GE) %>%
select(Location, date, obsValue) %>%
%>%
na.omit left_join(colors, by = c("Location" = "country")) %>%
mutate(color = ifelse(Location == "Netherlands", color2, color)) %>%
ggplot() + geom_line(aes(x = date, y = obsValue, color = color)) +
scale_color_identity() + theme_minimal() + add_6flags +
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)) +
ylab("Gross National Disposable Income (% of GDP)") + xlab("")
1995-
Code
%>%
SNA_TABLE2 filter(MEASURE == "C",
%in% c("FRA", "DEU", "BEL", "ESP", "NLD", "ITA"),
LOCATION %in% c("B1_GE", "B6GS1")) %>%
TRANSACT %>%
year_to_date left_join(SNA_TABLE2_var$LOCATION, by = "LOCATION") %>%
select(Location, date, TRANSACT, obsValue) %>%
spread(TRANSACT, obsValue) %>%
group_by(Location) %>%
mutate(obsValue = B6GS1 / B1_GE) %>%
select(Location, date, obsValue) %>%
%>%
na.omit left_join(colors, by = c("Location" = "country")) %>%
mutate(color = ifelse(Location == "Netherlands", color2, color)) %>%
filter(date >= as.Date("1995-01-01")) %>%
ggplot() + geom_line(aes(x = date, y = obsValue, color = color)) +
scale_color_identity() + theme_minimal() + add_6flags +
scale_x_date(breaks = seq(1920, 2025, 5) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
scale_y_continuous(breaks = 0.01*seq(-10, 120, 1),
labels = scales::percent_format(accuracy = 1)) +
ylab("Gross National Disposable Income (% of GDP)") + xlab("")
B6NS1 - Net national disposable income / GDP
France, Germany, Italy
Code
%>%
SNA_TABLE2 filter(MEASURE == "C",
%in% c("FRA", "DEU", "ITA"),
LOCATION %in% c("B1_GE", "B6NS1")) %>%
TRANSACT %>%
year_to_date left_join(SNA_TABLE2_var$LOCATION, by = "LOCATION") %>%
select(Location, date, TRANSACT, obsValue) %>%
spread(TRANSACT, obsValue) %>%
group_by(Location) %>%
mutate(obsValue = B6NS1 / B1_GE) %>%
select(Location, date, obsValue) %>%
%>%
na.omit 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, 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)) +
ylab("Net National Disposable Income (% of GDP)") + xlab("")
France, Germany, US, Denmark
Code
%>%
SNA_TABLE2 filter(MEASURE == "C",
%in% c("FRA", "DEU", "DNK", "USA"),
LOCATION %in% c("B1_GE", "B6NS1")) %>%
TRANSACT %>%
year_to_date left_join(SNA_TABLE2_var$LOCATION, by = "LOCATION") %>%
select(LOCATION, Location, date, TRANSACT, obsValue) %>%
spread(TRANSACT, obsValue) %>%
group_by(Location) %>%
mutate(obsValue = B6NS1 / B1_GE) %>%
select(LOCATION, Location, date, obsValue) %>%
%>%
na.omit left_join(colors, by = c("Location" = "country")) %>%
mutate(color = ifelse(LOCATION == "FRA", color2, color)) %>%
ggplot() + geom_line(aes(x = date, y = obsValue, color = color)) +
scale_color_identity() + theme_minimal() + add_4flags +
scale_x_date(breaks = seq(1920, 2025, 10) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
scale_y_continuous(breaks = 0.01*seq(-10, 100, 1),
labels = scales::percent_format(accuracy = 1)) +
ylab("Net National Disposable Income (% of GDP)") + xlab("")
France, Germany, Belgium, Spain, Netherlands, Italy
All
Code
%>%
SNA_TABLE2 filter(MEASURE == "C",
%in% c("FRA", "DEU", "BEL", "ESP", "NLD", "ITA"),
LOCATION %in% c("B1_GE", "B6NS1")) %>%
TRANSACT %>%
year_to_date left_join(SNA_TABLE2_var$LOCATION, by = "LOCATION") %>%
select(Location, date, TRANSACT, obsValue) %>%
spread(TRANSACT, obsValue) %>%
group_by(Location) %>%
mutate(obsValue = B6NS1 / B1_GE) %>%
select(Location, date, obsValue) %>%
%>%
na.omit left_join(colors, by = c("Location" = "country")) %>%
mutate(color = ifelse(Location == "Netherlands", color2, color)) %>%
ggplot() + geom_line(aes(x = date, y = obsValue, color = color)) +
scale_color_identity() + theme_minimal() + add_6flags +
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)) +
ylab("Net National Disposable Income (% of GDP)") + xlab("")
1995-
Code
%>%
SNA_TABLE2 filter(MEASURE == "C",
%in% c("FRA", "DEU", "BEL", "ESP", "NLD", "ITA"),
LOCATION %in% c("B1_GE", "B6NS1")) %>%
TRANSACT %>%
year_to_date left_join(SNA_TABLE2_var$LOCATION, by = "LOCATION") %>%
select(Location, date, TRANSACT, obsValue) %>%
spread(TRANSACT, obsValue) %>%
group_by(Location) %>%
mutate(obsValue = B6NS1 / B1_GE) %>%
select(Location, date, obsValue) %>%
%>%
na.omit left_join(colors, by = c("Location" = "country")) %>%
mutate(color = ifelse(Location == "Netherlands", color2, color)) %>%
filter(date >= as.Date("1995-01-01")) %>%
ggplot() + geom_line(aes(x = date, y = obsValue, color = color)) +
scale_color_identity() + theme_minimal() + add_6flags +
scale_x_date(breaks = seq(1920, 2025, 5) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
scale_y_continuous(breaks = 0.01*seq(-10, 100, 1),
labels = scales::percent_format(accuracy = 1)) +
ylab("Net National Disposable Income (% of GDP)") + xlab("")
B8NS1 - Net Saving / GDP
France, Germany, Italy
Code
%>%
SNA_TABLE2 filter(MEASURE == "C",
%in% c("FRA", "DEU", "ITA"),
LOCATION %in% c("B1_GE", "B8NS1")) %>%
TRANSACT %>%
year_to_date left_join(SNA_TABLE2_var$LOCATION, by = "LOCATION") %>%
select(Location, date, TRANSACT, obsValue) %>%
spread(TRANSACT, obsValue) %>%
group_by(Location) %>%
mutate(obsValue = B8NS1 / B1_GE) %>%
select(Location, date, obsValue) %>%
%>%
na.omit 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, 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)) +
ylab("Net Saving (% of GDP)") + xlab("")
France, Italy
Code
%>%
SNA_TABLE2 filter(MEASURE == "C",
%in% c("FRA", "ITA"),
LOCATION %in% c("B1_GE", "B8NS1")) %>%
TRANSACT %>%
year_to_date left_join(SNA_TABLE2_var$LOCATION, by = "LOCATION") %>%
select(Location, date, TRANSACT, obsValue) %>%
spread(TRANSACT, obsValue) %>%
group_by(Location) %>%
mutate(obsValue = B8NS1 / B1_GE) %>%
select(Location, date, obsValue) %>%
%>%
na.omit left_join(colors, by = c("Location" = "country")) %>%
ggplot() + geom_line(aes(x = date, y = obsValue, color = color)) +
scale_color_identity() + theme_minimal() + add_2flags +
scale_x_date(breaks = seq(1920, 2025, 10) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
scale_y_continuous(breaks = 0.01*seq(-10, 100, 1),
labels = scales::percent_format(accuracy = 1)) +
ylab("Net Saving (% of GDP)") + xlab("")
United Kingdom, Japan, United States
Code
%>%
SNA_TABLE2 filter(MEASURE == "C",
%in% c("JPN", "GBR", "USA"),
LOCATION %in% c("B1_GE", "B8NS1")) %>%
TRANSACT %>%
year_to_date left_join(SNA_TABLE2_var$LOCATION, by = "LOCATION") %>%
select(Location, date, TRANSACT, obsValue) %>%
spread(TRANSACT, obsValue) %>%
group_by(Location) %>%
mutate(obsValue = B8NS1 / B1_GE) %>%
select(Location, date, obsValue) %>%
%>%
na.omit 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, 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)) +
ylab("Net Saving (% of GDP)") + xlab("")
Switzerland, Germany, Netherlands
Code
%>%
SNA_TABLE2 filter(MEASURE == "C",
%in% c("NLD", "CHE", "DEU"),
LOCATION %in% c("B1_GE", "B8NS1")) %>%
TRANSACT %>%
year_to_date left_join(SNA_TABLE2_var$LOCATION, by = "LOCATION") %>%
select(Location, date, TRANSACT, obsValue) %>%
spread(TRANSACT, obsValue) %>%
group_by(Location) %>%
mutate(obsValue = B8NS1 / B1_GE) %>%
select(Location, date, obsValue) %>%
left_join(colors, by = c("Location" = "country")) %>%
ggplot() + geom_line(aes(x = date, y = obsValue, color = color)) +
scale_color_identity() + theme_minimal() + add_3flags +
scale_x_date(breaks = seq(1920, 2025, 10) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
scale_y_continuous(breaks = 0.01*seq(-10, 100, 1),
labels = scales::percent_format(accuracy = 1)) +
ylab("Net Saving (% of GDP)") + xlab("")
P5S1 - K1S1 - Net capital formation / GDP
France, Germany, Italy
Code
%>%
SNA_TABLE2 filter(MEASURE == "C",
%in% c("FRA", "DEU", "ITA"),
LOCATION %in% c("B1_GE", "P5S1", "K1S1")) %>%
TRANSACT %>%
year_to_date left_join(SNA_TABLE2_var$LOCATION, by = "LOCATION") %>%
select(Location, date, TRANSACT, obsValue) %>%
spread(TRANSACT, obsValue) %>%
mutate(obsValue = (P5S1 - K1S1) / B1_GE) %>%
filter(!is.na(obsValue)) %>%
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, 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("Net capital formation (% of GDP)") + xlab("")
United Kingdom, Japan, United States
Code
%>%
SNA_TABLE2 filter(MEASURE == "C",
%in% c("GBR", "JPN", "USA"),
LOCATION %in% c("B1_GE", "P5S1", "K1S1")) %>%
TRANSACT %>%
year_to_date left_join(SNA_TABLE2_var$LOCATION, by = "LOCATION") %>%
select(Location, date, TRANSACT, obsValue) %>%
spread(TRANSACT, obsValue) %>%
mutate(obsValue = (P5S1 - K1S1) / B1_GE) %>%
filter(!is.na(obsValue)) %>%
left_join(colors, by = c("Location" = "country")) %>%
ggplot() + geom_line(aes(x = date, y = obsValue, color = color)) +
scale_color_identity() + theme_minimal() + add_3flags +
scale_x_date(breaks = seq(1920, 2025, 10) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
theme(legend.position = c(0.85, 0.9),
legend.title = element_blank()) +
scale_y_continuous(breaks = 0.01*seq(-10, 100, 1),
labels = scales::percent_format(accuracy = 1)) +
ylab("Net capital formation (% of GDP)") + xlab("")
Switzerland, Germany, Netherlands
Code
%>%
SNA_TABLE2 filter(MEASURE == "C",
%in% c("CHE", "DEU", "NLD"),
LOCATION %in% c("B1_GE", "P5S1", "K1S1")) %>%
TRANSACT %>%
year_to_date left_join(SNA_TABLE2_var$LOCATION, by = "LOCATION") %>%
select(Location, date, TRANSACT, obsValue) %>%
spread(TRANSACT, obsValue) %>%
mutate(obsValue = (P5S1 - K1S1) / B1_GE) %>%
filter(!is.na(obsValue)) %>%
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, 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("Net capital formation (% of GDP)") + xlab("")
P5S1 - Gross capital formation / GDP
France, Germany, Italy
Code
%>%
SNA_TABLE2 filter(MEASURE == "C",
%in% c("FRA", "DEU", "ITA"),
LOCATION %in% c("B1_GE", "P5S1")) %>%
TRANSACT %>%
year_to_date left_join(SNA_TABLE2_var$LOCATION, by = "LOCATION") %>%
spread(TRANSACT, obsValue) %>%
mutate(obsValue = (P5S1) / B1_GE) %>%
filter(!is.na(obsValue)) %>%
left_join(colors, by = c("Location" = "country")) %>%
ggplot() + geom_line(aes(x = date, y = obsValue, color = color)) +
scale_color_identity() + theme_minimal() + add_3flags +
scale_x_date(breaks = seq(1920, 2025, 10) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
theme(legend.position = c(0.85, 0.9),
legend.title = element_blank()) +
scale_y_continuous(breaks = 0.01*seq(-10, 100, 1),
labels = scales::percent_format(accuracy = 1)) +
ylab("Gross capital formation (% of GDP)") + xlab("")
United Kingdom, Japan, United States
Code
%>%
SNA_TABLE2 filter(MEASURE == "C",
%in% c("GBR", "JPN", "USA"),
LOCATION %in% c("B1_GE", "P5S1")) %>%
TRANSACT %>%
year_to_date left_join(SNA_TABLE2_var$LOCATION, by = "LOCATION") %>%
spread(TRANSACT, obsValue) %>%
mutate(obsValue = (P5S1) / B1_GE) %>%
filter(!is.na(obsValue)) %>%
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, 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("Gross capital formation (% of GDP)") + xlab("")
Switzerland, Germany, Netherlands
Code
%>%
SNA_TABLE2 filter(MEASURE == "C",
%in% c("CHE", "DEU", "NLD"),
LOCATION %in% c("B1_GE", "P5S1")) %>%
TRANSACT %>%
year_to_date left_join(SNA_TABLE2_var$LOCATION, by = "LOCATION") %>%
spread(TRANSACT, obsValue) %>%
mutate(obsValue = (P5S1) / B1_GE) %>%
filter(!is.na(obsValue)) %>%
left_join(colors, by = c("Location" = "country")) %>%
ggplot() + geom_line(aes(x = date, y = obsValue, color = color)) +
scale_color_identity() + theme_minimal() + add_3flags +
scale_x_date(breaks = seq(1920, 2025, 10) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
theme(legend.position = c(0.85, 0.9),
legend.title = element_blank()) +
scale_y_continuous(breaks = 0.01*seq(-10, 100, 1),
labels = scales::percent_format(accuracy = 1)) +
ylab("Gross capital formation (% of GDP)") + xlab("")
B9S1 - Net Lending - Net Borrowing / GDP
France, Germany, Italy
Code
%>%
SNA_TABLE2 filter(MEASURE == "C",
%in% c("FRA", "DEU", "ITA"),
LOCATION %in% c("B1_GE", "B9S1")) %>%
TRANSACT %>%
year_to_date left_join(SNA_TABLE2_var$LOCATION, by = "LOCATION") %>%
select(Location, date, TRANSACT, obsValue) %>%
spread(TRANSACT, obsValue) %>%
group_by(Location) %>%
mutate(obsValue = B9S1 / B1_GE) %>%
select(Location, date, obsValue) %>%
%>%
na.omit 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, 10) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
scale_y_continuous(breaks = 0.01*seq(-100, 100, 5),
labels = scales::percent_format(accuracy = 1)) +
ylab("Net Lending - Net Borrowing (% of GDP)") + xlab("")
France, Italy
Code
%>%
SNA_TABLE2 filter(MEASURE == "C",
%in% c("FRA", "ITA"),
LOCATION %in% c("B1_GE", "B9S1")) %>%
TRANSACT %>%
year_to_date left_join(SNA_TABLE2_var$LOCATION, by = "LOCATION") %>%
select(Location, date, TRANSACT, obsValue) %>%
spread(TRANSACT, obsValue) %>%
group_by(Location) %>%
mutate(obsValue = B9S1 / B1_GE) %>%
select(Location, date, obsValue) %>%
%>%
na.omit left_join(colors, by = c("Location" = "country")) %>%
ggplot() + geom_line(aes(x = date, y = obsValue, color = color)) +
scale_color_identity() + theme_minimal() + add_2flags +
scale_x_date(breaks = seq(1920, 2025, 10) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
scale_y_continuous(breaks = 0.01*seq(-100, 100, 1),
labels = scales::percent_format(accuracy = 1)) +
ylab("Net Lending - Net Borrowing (% of GDP)") + xlab("")
United Kingdom, Japan, United States
Code
%>%
SNA_TABLE2 filter(MEASURE == "C",
%in% c("JPN", "GBR", "USA"),
LOCATION %in% c("B1_GE", "B9S1")) %>%
TRANSACT %>%
year_to_date left_join(SNA_TABLE2_var$LOCATION, by = "LOCATION") %>%
select(Location, date, TRANSACT, obsValue) %>%
spread(TRANSACT, obsValue) %>%
group_by(Location) %>%
mutate(obsValue = B9S1 / B1_GE) %>%
select(Location, date, obsValue) %>%
%>%
na.omit 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, 10) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
scale_y_continuous(breaks = 0.01*seq(-100, 100, 1),
labels = scales::percent_format(accuracy = 1)) +
ylab("Net Lending - Net Borrowing (% of GDP)") + xlab("")
Switzerland, Germany, Netherlands
Code
%>%
SNA_TABLE2 filter(MEASURE == "C",
%in% c("NLD", "CHE", "DEU"),
LOCATION %in% c("B1_GE", "B9S1")) %>%
TRANSACT %>%
year_to_date left_join(SNA_TABLE2_var$LOCATION, by = "LOCATION") %>%
select(Location, date, TRANSACT, obsValue) %>%
spread(TRANSACT, obsValue) %>%
group_by(Location) %>%
mutate(obsValue = B9S1 / B1_GE) %>%
select(Location, date, obsValue) %>%
%>%
na.omit 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, 10) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
scale_y_continuous(breaks = 0.01*seq(-100, 100, 1),
labels = scales::percent_format(accuracy = 1)) +
ylab("Net Lending - Net Borrowing (% of GDP)") + xlab("")