Social Expenditure - Aggregated data - SOCX_AGG
Data - OECD
Info
Data on germany
source | dataset | .html | .RData |
---|---|---|---|
2024-06-06 | 2024-06-30 | ||
2024-09-15 | 2024-09-15 |
LAST_COMPILE
LAST_COMPILE |
---|
2024-09-15 |
Last
obsTime | Nobs |
---|---|
2022 | 27 |
Number of Observations
Code
%>%
SOCX_AGG left_join(SOCX_AGG_var$BRANCH, by = "BRANCH") %>%
left_join(SOCX_AGG_var$TYPEXP, by = "TYPEXP") %>%
group_by(BRANCH, Branch, TYPEXP, Typexp, SOURCE, TYPROG, UNIT) %>%
summarise(Nobs = n()) %>%
arrange(-Nobs) %>%
if (is_html_output()) datatable(., filter = 'top', rownames = F) else .} {
SOURCE
Code
%>%
SOCX_AGG left_join(SOCX_AGG_var$SOURCE, by = "SOURCE") %>%
group_by(SOURCE, Source) %>%
summarise(Nobs = n()) %>%
arrange(-Nobs) %>%
if (is_html_output()) print_table(.) else .} {
SOURCE | Source | Nobs |
---|---|---|
10 | Public | 620962 |
20 | Mandatory private | 555275 |
10_20 | Public and mandatory private | 486610 |
20_30 | Private (Mandatory and Voluntary) | 99093 |
30 | Voluntary private | 46257 |
40 | Net Public | 380 |
50 | Net Total | 379 |
BRANCH
Code
%>%
SOCX_AGG left_join(SOCX_AGG_var$BRANCH, by = "BRANCH") %>%
group_by(BRANCH, Branch) %>%
summarise(Nobs = n()) %>%
arrange(-Nobs) %>%
if (is_html_output()) print_table(.) else .} {
BRANCH | Branch | Nobs |
---|---|---|
3 | Incapacity related | 276495 |
5 | Family | 216535 |
1 | Old age | 208398 |
1_2 | Old age and Survivors | 187863 |
6 | Active labour market programmes | 181100 |
9 | Other social policy areas | 177418 |
2 | Survivors | 165123 |
7 | Unemployment | 111688 |
8 | Housing | 107355 |
90 | Total | 95210 |
4 | Health | 81771 |
TYPEXP
Code
%>%
SOCX_AGG left_join(SOCX_AGG_var$TYPEXP, by = "TYPEXP") %>%
group_by(TYPEXP, Typexp) %>%
summarise(Nobs = n()) %>%
arrange(-Nobs) %>%
if (is_html_output()) print_table(.) else .} {
TYPEXP | Typexp | Nobs |
---|---|---|
1 | Cash benefits | 722474 |
2 | Benefits in kind | 584666 |
0 | Total | 501816 |
TYPROG
Code
%>%
SOCX_AGG left_join(SOCX_AGG_var$TYPROG, by = "TYPROG") %>%
group_by(TYPROG, Typrog) %>%
summarise(Nobs = n()) %>%
arrange(-Nobs) %>%
print_table_conditional()
UNIT
Code
%>%
SOCX_AGG left_join(SOCX_AGG_var$UNIT, by = "UNIT") %>%
group_by(UNIT, Unit) %>%
summarise(Nobs = n()) %>%
arrange(-Nobs) %>%
print_table_conditional()
UNIT | Unit | Nobs |
---|---|---|
NCUR | NA | 326763 |
PCT_GDP | NA | 320149 |
PPPVH | NA | 315628 |
PPPH | NA | 310505 |
NCST | NA | 309449 |
PCT_GOV | NA | 226462 |
COUNTRY
Code
%>%
SOCX_AGG left_join(SOCX_AGG_var$COUNTRY, by = "COUNTRY") %>%
group_by(COUNTRY, Country) %>%
summarise(Nobs = n()) %>%
arrange(-Nobs) %>%
print_table_conditional()
obsTime
Code
%>%
SOCX_AGG group_by(obsTime) %>%
summarise(Nobs = n()) %>%
if (is_html_output()) datatable(., filter = 'top', rownames = F) else .} {
All
Cash
Code
%>%
SOCX_AGG filter(SOURCE == 10,
== 0,
TYPROG == 1,
TYPEXP %in% c("USA", "FRA", "DEU", "GBR"),
COUNTRY == "PCT_GDP",
UNIT == "2015") %>%
obsTime left_join(SOCX_AGG_var$BRANCH, by = "BRANCH") %>%
select(BRANCH, Branch, COUNTRY, obsValue) %>%
spread(COUNTRY, obsValue) %>%
mutate_at(vars(-BRANCH, -Branch), funs(round(., digits = 1))) -> SOC_AGG_TYPEXP_1
do.call(save, list("SOC_AGG_TYPEXP_1", file = "SOC_AGG_TYPEXP_1.RData"))
%>%
SOC_AGG_TYPEXP_1 if (is_html_output()) datatable(., filter = 'top', rownames = F) else .} {
In-kind
Code
%>%
SOCX_AGG filter(SOURCE == 10,
== 0,
TYPROG == 2,
TYPEXP %in% c("USA", "FRA", "DEU", "GBR"),
COUNTRY == "PCT_GDP",
UNIT == "2015") %>%
obsTime left_join(SOCX_AGG_var$BRANCH, by = "BRANCH") %>%
select(BRANCH, Branch, COUNTRY, obsValue) %>%
spread(COUNTRY, obsValue) %>%
mutate_at(vars(-BRANCH, -Branch), funs(round(., digits = 1))) -> SOC_AGG_TYPEXP_2
do.call(save, list("SOC_AGG_TYPEXP_2", file = "SOC_AGG_TYPEXP_2.RData"))
%>%
SOC_AGG_TYPEXP_2 if (is_html_output()) datatable(., filter = 'top', rownames = F) else .} {
Total
Code
%>%
SOCX_AGG filter(SOURCE == 10,
== 0,
TYPROG == 0,
TYPEXP %in% c("USA", "FRA", "DEU", "GBR"),
COUNTRY == "PCT_GDP",
UNIT == "2015") %>%
obsTime left_join(SOCX_AGG_var$BRANCH, by = "BRANCH") %>%
select(BRANCH, Branch, COUNTRY, obsValue) %>%
spread(COUNTRY, obsValue) %>%
mutate_at(vars(-BRANCH, -Branch), funs(round(., digits = 1))) -> SOC_AGG_TYPEXP_0
do.call(save, list("SOC_AGG_TYPEXP_0", file = "SOC_AGG_TYPEXP_0.RData"))
%>%
SOC_AGG_TYPEXP_0 if (is_html_output()) datatable(., filter = 'top', rownames = F) else .} {
Total - Cash benefits (Branch 90)
World
Code
%>%
SOCX_AGG filter(SOURCE == 10,
== 0,
TYPROG == 1,
TYPEXP == 90,
BRANCH == "PCT_GDP") %>%
UNIT left_join(SOCX_AGG_var$COUNTRY, by = "COUNTRY") %>%
%>%
year_to_enddate mutate(year = year(date)) %>%
filter(year %in% c(1990, 2000, 2015)) %>%
arrange(Country, year) %>%
group_by(Country) %>%
summarise(`1990 (% GDP)` = obsValue[1],
`2000 (% GDP)` = obsValue[2],
`2015 (% GDP)` = obsValue[3],
`Delta 2000-15` = obsValue[3] - obsValue[2]) %>%
arrange(-`2015 (% GDP)`) %>%
mutate_at(vars(-Country), funs(round(., digits = 1))) -> SOC_AGG_BRANCH_90
# do.call(save, list("SOC_AGG_BRANCH_90", file = "SOC_AGG_BRANCH_90.RData"))
%>%
SOC_AGG_BRANCH_90 if (is_html_output()) datatable(., filter = 'top', rownames = F) else .} {
United States, United Kingdom, Australia
Code
%>%
SOCX_AGG filter(SOURCE == 10,
== 0,
TYPROG == 1,
TYPEXP == 90,
BRANCH == "PCT_GDP",
UNIT %in% c("USA", "GBR", "AUS")) %>%
COUNTRY left_join(SOCX_AGG_var$COUNTRY, by = "COUNTRY") %>%
%>%
year_to_date rename(Location = Country) %>%
left_join(colors, by = c("Location" = "country")) %>%
mutate(obsValue = obsValue / 100) %>%
ggplot() + theme_minimal() + ylab("Total - Cash Benefits (% of GDP)") + xlab("") +
geom_line(aes(x = date, y = obsValue, color = color)) +
scale_color_identity() + add_3flags +
scale_x_date(breaks = seq(1920, 2025, 5) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
theme(legend.position = c(0.8, 0.15),
legend.title = element_blank()) +
scale_y_continuous(breaks = 0.01*seq(-7, 30, 1),
labels = scales::percent_format(accuracy = 1))
France, Germany, Netherlands
Code
%>%
SOCX_AGG filter(SOURCE == 10,
== 0,
TYPROG == 1,
TYPEXP == 90,
BRANCH == "PCT_GDP",
UNIT %in% c("FRA", "DEU", "NLD")) %>%
COUNTRY left_join(SOCX_AGG_var$COUNTRY, by = "COUNTRY") %>%
%>%
year_to_date rename(Location = Country) %>%
left_join(colors, by = c("Location" = "country")) %>%
mutate(obsValue = obsValue / 100) %>%
ggplot() + theme_minimal() + ylab("Total - Cash Benefits (% of GDP)") + xlab("") +
geom_line(aes(x = date, y = obsValue, color = color)) +
scale_color_identity() + add_3flags +
scale_x_date(breaks = seq(1920, 2025, 5) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
theme(legend.position = c(0.2, 0.15),
legend.title = element_blank()) +
scale_y_continuous(breaks = 0.01*seq(-7, 30, 1),
labels = scales::percent_format(accuracy = 1))
Chile, Denmark, Sweden
Code
%>%
SOCX_AGG filter(SOURCE == 10,
== 0,
TYPROG == 1,
TYPEXP == 90,
BRANCH == "PCT_GDP",
UNIT %in% c("DNK", "SWE", "CHL")) %>%
COUNTRY left_join(SOCX_AGG_var$COUNTRY, by = "COUNTRY") %>%
%>%
year_to_date rename(Location = Country) %>%
left_join(colors, by = c("Location" = "country")) %>%
mutate(obsValue = obsValue / 100) %>%
ggplot() + theme_minimal() + ylab("Total - Cash Benefits (% of GDP)") + xlab("") +
geom_line(aes(x = date, y = obsValue, color = color)) +
scale_color_identity() + add_3flags +
scale_x_date(breaks = seq(1920, 2025, 5) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
theme(legend.position = c(0.7, 0.85),
legend.title = element_blank(),
legend.direction = "horizontal") +
scale_y_continuous(breaks = 0.01*seq(-7, 30, 1),
labels = scales::percent_format(accuracy = 1))
Switzerland, Canada, Sloveinia
Code
%>%
SOCX_AGG filter(SOURCE == 10,
== 0,
TYPROG == 1,
TYPEXP == 90,
BRANCH == "PCT_GDP",
UNIT %in% c("CHE", "CAN", "SVN")) %>%
COUNTRY left_join(SOCX_AGG_var$COUNTRY, by = "COUNTRY") %>%
%>%
year_to_date rename(Location = Country) %>%
left_join(colors, by = c("Location" = "country")) %>%
mutate(obsValue = obsValue / 100) %>%
ggplot() + theme_minimal() + ylab("Total - Cash Benefits (% of GDP)") + xlab("") +
geom_line(aes(x = date, y = obsValue, color = color)) +
scale_color_identity() + add_3flags +
scale_x_date(breaks = seq(1920, 2025, 5) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
theme(legend.position = c(0.8, 0.25),
legend.title = element_blank()) +
scale_y_continuous(breaks = 0.01*seq(-7, 30, 1),
labels = scales::percent_format(accuracy = 1))
Austria, Belgium, Italy
Code
%>%
SOCX_AGG filter(SOURCE == 10,
== 0,
TYPROG == 1,
TYPEXP == 90,
BRANCH == "PCT_GDP",
UNIT %in% c("BEL", "AUT", "ITA")) %>%
COUNTRY left_join(SOCX_AGG_var$COUNTRY, by = "COUNTRY") %>%
%>%
year_to_date rename(Location = Country) %>%
left_join(colors, by = c("Location" = "country")) %>%
mutate(obsValue = obsValue / 100) %>%
ggplot() + theme_minimal() + ylab("Total - Cash Benefits (% of GDP)") + xlab("") +
geom_line(aes(x = date, y = obsValue, color = color)) +
scale_color_identity() + add_3flags +
scale_x_date(breaks = seq(1920, 2025, 5) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
theme(legend.position = c(0.8, 0.25),
legend.title = element_blank()) +
scale_y_continuous(breaks = 0.01*seq(-7, 30, 1),
labels = scales::percent_format(accuracy = 1))
Old age - Cash benefits (Branch 1)
World
Code
%>%
SOCX_AGG filter(SOURCE == 10,
== 0,
TYPROG == 1,
TYPEXP == 1,
BRANCH == "PCT_GDP") %>%
UNIT left_join(SOCX_AGG_var$COUNTRY, by = "COUNTRY") %>%
%>%
year_to_enddate mutate(year = year(date)) %>%
filter(year %in% c(1990, 2000, 2015)) %>%
arrange(Country, year) %>%
group_by(Country, COUNTRY) %>%
summarise(`1990 (% GDP)` = obsValue[1],
`2000 (% GDP)` = obsValue[2],
`2015 (% GDP)` = obsValue[3],
`Delta 2000-15` = obsValue[3] - obsValue[2]) %>%
arrange(-`2015 (% GDP)`) %>%
mutate_at(vars(-Country, -COUNTRY), funs(round(., digits = 1))) %>%
rename(`Country Name` = Country,
`Country Code` = COUNTRY) -> SOC_AGG_BRANCH_1
do.call(save, list("SOC_AGG_BRANCH_1", file = "SOC_AGG_BRANCH_1.RData"))
%>%
SOC_AGG_BRANCH_1 if (is_html_output()) datatable(., filter = 'top', rownames = F) else .} {
Retirement
2015
Code
%>%
SOCX_AGG filter(TYPROG == 0,
== 1,
TYPEXP == 1,
BRANCH == "PCT_GDP",
UNIT == "2014") %>%
obsTime left_join(SOCX_AGG_var$COUNTRY, by = "COUNTRY") %>%
left_join(SOCX_AGG_var$SOURCE, by = "SOURCE") %>%
select(Source, COUNTRY, Country, obsValue) %>%
spread(Source, obsValue) %>%
arrange(-`Public`) %>%
if (is_html_output()) datatable(., filter = 'top', rownames = F) else .} {
2017
Code
%>%
SOCX_AGG filter(TYPROG == 0,
== 1,
TYPEXP == 1,
BRANCH == "PCT_GDP",
UNIT == "2017") %>%
obsTime left_join(SOCX_AGG_var$COUNTRY, by = "COUNTRY") %>%
left_join(SOCX_AGG_var$SOURCE, by = "SOURCE") %>%
select(Source, COUNTRY, Country, obsValue) %>%
spread(Source, obsValue) %>%
arrange(-`Public`) %>%
if (is_html_output()) datatable(., filter = 'top', rownames = F) else .} {
United States, United Kingdom, Australia
Code
%>%
SOCX_AGG filter(SOURCE == 10,
== 0,
TYPROG == 1,
TYPEXP == 1,
BRANCH == "PCT_GDP",
UNIT %in% c("USA", "GBR", "AUS")) %>%
COUNTRY left_join(SOCX_AGG_var$COUNTRY, by = "COUNTRY") %>%
%>%
year_to_date rename(Location = Country) %>%
left_join(colors, by = c("Location" = "country")) %>%
mutate(obsValue = obsValue / 100) %>%
ggplot() + theme_minimal() + ylab("Old age - Cash Benefits (% of GDP)") + xlab("") +
geom_line(aes(x = date, y = obsValue, color = color)) +
scale_color_identity() + add_3flags +
scale_x_date(breaks = seq(1920, 2025, 5) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
theme(legend.position = c(0.35, 0.9),
legend.title = element_blank(),
legend.direction = "horizontal") +
scale_y_continuous(breaks = 0.01*seq(-7, 16, 0.5),
labels = scales::percent_format(accuracy = 0.1))
France, Germany, Netherlands
English
Code
%>%
SOCX_AGG filter(SOURCE == 10,
== 0,
TYPROG == 1,
TYPEXP == 1,
BRANCH == "PCT_GDP",
UNIT %in% c("FRA", "DEU", "NLD")) %>%
COUNTRY left_join(SOCX_AGG_var$COUNTRY, by = "COUNTRY") %>%
%>%
year_to_date rename(Location = Country) %>%
left_join(colors, by = c("Location" = "country")) %>%
mutate(obsValue = obsValue / 100) %>%
ggplot() + theme_minimal() + ylab("Old age - Cash Benefits (% of GDP)") + xlab("") +
geom_line(aes(x = date, y = obsValue, color = color)) +
scale_color_identity() + add_3flags +
scale_x_date(breaks = seq(1920, 2025, 5) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
theme(legend.position = c(0.25, 0.9),
legend.title = element_blank(),
legend.direction = "horizontal") +
scale_y_continuous(breaks = 0.01*seq(-7, 16, 1),
labels = scales::percent_format(accuracy = 0.1))
French
Code
%>%
SOCX_AGG filter(SOURCE == 10,
== 0,
TYPROG == 1,
TYPEXP == 1,
BRANCH == "PCT_GDP",
UNIT %in% c("FRA", "DEU", "NLD")) %>%
COUNTRY mutate(Country = case_when(COUNTRY == "FRA" ~ "France",
== "DEU" ~ "Allemagne",
COUNTRY == "NLD" ~ "Pays-Bas")) %>%
COUNTRY %>%
year_to_enddate ggplot() + theme_minimal() + ylab("Retraites - Dépenses Monétaires (% of PIB)") + xlab("") +
geom_line(aes(x = date, y = obsValue / 100, color = Country, linetype = Country)) +
scale_color_manual(values = viridis(4)[1:3]) +
scale_x_date(breaks = seq(1920, 2025, 2) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%y")) +
theme(legend.position = c(0.25, 0.9),
legend.title = element_blank(),
legend.direction = "horizontal") +
scale_y_continuous(breaks = 0.01*seq(-7, 16, 1),
labels = scales::percent_format(accuracy = 1))
Chile, Denmark, Sweden
Code
%>%
SOCX_AGG filter(SOURCE == 10,
== 0,
TYPROG == 1,
TYPEXP == 1,
BRANCH == "PCT_GDP",
UNIT %in% c("DNK", "SWE", "CHL")) %>%
COUNTRY left_join(SOCX_AGG_var$COUNTRY, by = "COUNTRY") %>%
%>%
year_to_date rename(Location = Country) %>%
left_join(colors, by = c("Location" = "country")) %>%
mutate(obsValue = obsValue / 100) %>%
ggplot() + theme_minimal() + ylab("Old age - Cash Benefits (% of GDP)") + xlab("") +
geom_line(aes(x = date, y = obsValue, color = color)) +
scale_color_identity() + add_3flags +
scale_x_date(breaks = seq(1920, 2025, 5) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
theme(legend.position = c(0.25, 0.2),
legend.title = element_blank()) +
scale_y_continuous(breaks = 0.01*seq(-7, 16, 0.5),
labels = scales::percent_format(accuracy = 0.1))
Switzerland, Canada, Sloveinia
Code
%>%
SOCX_AGG filter(SOURCE == 10,
== 0,
TYPROG == 1,
TYPEXP == 1,
BRANCH == "PCT_GDP",
UNIT %in% c("CHE", "CAN", "SVN")) %>%
COUNTRY left_join(SOCX_AGG_var$COUNTRY, by = "COUNTRY") %>%
%>%
year_to_date rename(Location = Country) %>%
left_join(colors, by = c("Location" = "country")) %>%
mutate(obsValue = obsValue / 100) %>%
ggplot() + theme_minimal() + ylab("Old age - Cash Benefits (% of GDP)") + xlab("") +
geom_line(aes(x = date, y = obsValue, color = color)) +
scale_color_identity() + add_3flags +
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(-7, 16, 1),
labels = scales::percent_format(accuracy = 0.1))
Austria, Belgium, Italy
Code
%>%
SOCX_AGG filter(SOURCE == 10,
== 0,
TYPROG == 1,
TYPEXP == 1,
BRANCH == "PCT_GDP",
UNIT %in% c("BEL", "AUT", "ITA")) %>%
COUNTRY left_join(SOCX_AGG_var$COUNTRY, by = "COUNTRY") %>%
%>%
year_to_date rename(Location = Country) %>%
left_join(colors, by = c("Location" = "country")) %>%
mutate(obsValue = obsValue / 100) %>%
ggplot() + theme_minimal() + ylab("Old age - Cash Benefits (% of GDP)") + xlab("") +
geom_line(aes(x = date, y = obsValue, color = color)) +
scale_color_identity() + add_3flags +
scale_x_date(breaks = seq(1920, 2025, 5) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
theme(legend.position = c(0.15, 0.85),
legend.title = element_blank()) +
scale_y_continuous(breaks = 0.01*seq(-7, 16, 1),
labels = scales::percent_format(accuracy = 0.1))
Survivors - Cash benefits (Branch 2)
World
Code
%>%
SOCX_AGG filter(SOURCE == 10,
== 0,
TYPROG == 1,
TYPEXP == 2,
BRANCH == "PCT_GDP") %>%
UNIT left_join(SOCX_AGG_var$COUNTRY, by = "COUNTRY") %>%
%>%
year_to_enddate mutate(year = year(date)) %>%
filter(year %in% c(1990, 2000, 2015)) %>%
arrange(Country, year) %>%
group_by(Country, COUNTRY) %>%
summarise(`1990 (% GDP)` = obsValue[1],
`2000 (% GDP)` = obsValue[2],
`2015 (% GDP)` = obsValue[3],
`Delta 2000-15` = obsValue[3] - obsValue[2]) %>%
arrange(-`2015 (% GDP)`) %>%
mutate_at(vars(-Country, -COUNTRY), funs(round(., digits = 1))) %>%
rename(`Country Name` = Country,
`Country Code` = COUNTRY) -> SOC_AGG_BRANCH_2
# do.call(save, list("SOC_AGG_BRANCH_2", file = "SOC_AGG_BRANCH_2.RData"))
%>%
SOC_AGG_BRANCH_2 if (is_html_output()) datatable(., filter = 'top', rownames = F) else .} {
United States, United Kingdom, Australia
Code
%>%
SOCX_AGG filter(SOURCE == 10,
== 0,
TYPROG == 1,
TYPEXP == 2,
BRANCH == "PCT_GDP",
UNIT %in% c("USA", "GBR", "AUS")) %>%
COUNTRY left_join(SOCX_AGG_var$COUNTRY, by = "COUNTRY") %>%
%>%
year_to_date rename(Location = Country) %>%
left_join(colors, by = c("Location" = "country")) %>%
mutate(obsValue = obsValue / 100) %>%
ggplot() + theme_minimal() + ylab("Survivors - Cash Benefits (% of GDP)") + xlab("") +
geom_line(aes(x = date, y = obsValue, color = color)) +
scale_color_identity() + add_3flags +
scale_x_date(breaks = seq(1920, 2025, 5) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
theme(legend.position = c(0.65, 0.9),
legend.title = element_blank(),
legend.direction = "horizontal") +
scale_y_continuous(breaks = 0.01*seq(-7, 16, 0.5),
labels = scales::percent_format(accuracy = 0.1))
France, Germany, Netherlands
Code
%>%
SOCX_AGG filter(SOURCE == 10,
== 0,
TYPROG == 1,
TYPEXP == 2,
BRANCH == "PCT_GDP",
UNIT %in% c("FRA", "DEU", "NLD")) %>%
COUNTRY left_join(SOCX_AGG_var$COUNTRY, by = "COUNTRY") %>%
%>%
year_to_date rename(Location = Country) %>%
left_join(colors, by = c("Location" = "country")) %>%
mutate(obsValue = obsValue / 100) %>%
ggplot() + theme_minimal() + ylab("Survivors - Cash Benefits (% of GDP)") + xlab("") +
geom_line(aes(x = date, y = obsValue, color = color)) +
scale_color_identity() + add_3flags +
scale_x_date(breaks = seq(1920, 2025, 5) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
theme(legend.position = c(0.25, 0.9),
legend.title = element_blank(),
legend.direction = "horizontal") +
scale_y_continuous(breaks = 0.01*seq(-7, 16, 0.5),
labels = scales::percent_format(accuracy = 0.1))
Chile, Denmark, Sweden
Code
%>%
SOCX_AGG filter(SOURCE == 10,
== 0,
TYPROG == 1,
TYPEXP == 2,
BRANCH == "PCT_GDP",
UNIT %in% c("DNK", "SWE", "CHL")) %>%
COUNTRY left_join(SOCX_AGG_var$COUNTRY, by = "COUNTRY") %>%
%>%
year_to_date rename(Location = Country) %>%
left_join(colors, by = c("Location" = "country")) %>%
mutate(obsValue = obsValue / 100) %>%
ggplot() + theme_minimal() + ylab("Survivors - Cash Benefits (% of GDP)") + xlab("") +
geom_line(aes(x = date, y = obsValue, color = color)) +
scale_color_identity() + add_3flags +
scale_x_date(breaks = seq(1920, 2025, 5) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
theme(legend.position = c(0.25, 0.2),
legend.title = element_blank()) +
scale_y_continuous(breaks = 0.01*seq(-7, 16, 0.5),
labels = scales::percent_format(accuracy = 0.1))
Switzerland, Canada, Sloveinia
Code
%>%
SOCX_AGG filter(SOURCE == 10,
== 0,
TYPROG == 1,
TYPEXP == 2,
BRANCH == "PCT_GDP",
UNIT %in% c("CHE", "CAN", "SVN")) %>%
COUNTRY left_join(SOCX_AGG_var$COUNTRY, by = "COUNTRY") %>%
%>%
year_to_date rename(Location = Country) %>%
left_join(colors, by = c("Location" = "country")) %>%
mutate(obsValue = obsValue / 100) %>%
ggplot() + theme_minimal() + ylab("Survivors - Cash Benefits (% of GDP)") + xlab("") +
geom_line(aes(x = date, y = obsValue, color = color)) +
scale_color_identity() + add_3flags +
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(-7, 16, 1),
labels = scales::percent_format(accuracy = 0.1))
Austria, Belgium, Italy
Code
%>%
SOCX_AGG filter(SOURCE == 10,
== 0,
TYPROG == 1,
TYPEXP == 2,
BRANCH == "PCT_GDP",
UNIT %in% c("BEL", "AUT", "ITA")) %>%
COUNTRY left_join(SOCX_AGG_var$COUNTRY, by = "COUNTRY") %>%
%>%
year_to_date rename(Location = Country) %>%
left_join(colors, by = c("Location" = "country")) %>%
mutate(obsValue = obsValue / 100) %>%
ggplot() + theme_minimal() + ylab("Survivors - Cash Benefits (% of GDP)") + xlab("") +
geom_line(aes(x = date, y = obsValue, color = color)) +
scale_color_identity() + add_3flags +
scale_x_date(breaks = seq(1920, 2025, 5) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
theme(legend.position = c(0.35, 0.25),
legend.title = element_blank(),
legend.direction = "horizontal") +
scale_y_continuous(breaks = 0.01*seq(-7, 16, 0.5),
labels = scales::percent_format(accuracy = 0.1))
Health - Total (Branch 7)
World
Code
%>%
SOCX_AGG filter(SOURCE == 10,
== 0,
TYPROG == 0,
TYPEXP == 4,
BRANCH == "PCT_GDP") %>%
UNIT left_join(SOCX_AGG_var$COUNTRY, by = "COUNTRY") %>%
%>%
year_to_enddate mutate(year = year(date)) %>%
filter(year %in% c(1990, 2000, 2015)) %>%
arrange(Country, year) %>%
group_by(Country, COUNTRY) %>%
summarise(`1990 (% GDP)` = obsValue[1],
`2000 (% GDP)` = obsValue[2],
`2015 (% GDP)` = obsValue[3],
`Delta 2000-15` = obsValue[3] - obsValue[2]) %>%
arrange(-`2015 (% GDP)`) %>%
mutate_at(vars(-Country, -COUNTRY), funs(round(., digits = 1))) %>%
rename(`Country Name` = Country,
`Country Code` = COUNTRY) -> SOC_AGG_BRANCH_4
# do.call(save, list("SOC_AGG_BRANCH_4", file = "SOC_AGG_BRANCH_4.RData"))
%>%
SOC_AGG_BRANCH_4 if (is_html_output()) datatable(., filter = 'top', rownames = F) else .} {
United States, United Kingdom, Australia
Code
%>%
SOCX_AGG filter(SOURCE == 10,
== 0,
TYPROG == 0,
TYPEXP == 4,
BRANCH == "PCT_GDP",
UNIT %in% c("USA", "GBR", "AUS")) %>%
COUNTRY left_join(SOCX_AGG_var$COUNTRY, by = "COUNTRY") %>%
%>%
year_to_date rename(Location = Country) %>%
left_join(colors, by = c("Location" = "country")) %>%
mutate(obsValue = obsValue / 100) %>%
ggplot() + theme_minimal() + ylab("Health (% of GDP)") + xlab("") +
geom_line(aes(x = date, y = obsValue, color = color)) +
scale_color_identity() + add_3flags +
scale_x_date(breaks = seq(1920, 2025, 5) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
theme(legend.position = c(0.15, 0.9),
legend.title = element_blank()) +
scale_y_continuous(breaks = 0.01*seq(-7, 16, 0.5),
labels = scales::percent_format(accuracy = 0.1))
France, Germany, Netherlands
Code
%>%
SOCX_AGG filter(SOURCE == 10,
== 0,
TYPROG == 0,
TYPEXP == 4,
BRANCH == "PCT_GDP",
UNIT %in% c("FRA", "DEU", "NLD")) %>%
COUNTRY left_join(SOCX_AGG_var$COUNTRY, by = "COUNTRY") %>%
%>%
year_to_date rename(Location = Country) %>%
left_join(colors, by = c("Location" = "country")) %>%
mutate(obsValue = obsValue / 100) %>%
ggplot() + theme_minimal() + ylab("Health (% of GDP)") + xlab("") +
geom_line(aes(x = date, y = obsValue, color = color)) +
scale_color_identity() + add_3flags +
scale_x_date(breaks = seq(1920, 2025, 5) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
theme(legend.position = c(0.25, 0.9),
legend.title = element_blank(),
legend.direction = "horizontal") +
scale_y_continuous(breaks = 0.01*seq(-7, 16, 0.5),
labels = scales::percent_format(accuracy = 0.1))
Chile, Denmark, Sweden
Code
%>%
SOCX_AGG filter(SOURCE == 10,
== 0,
TYPROG == 0,
TYPEXP == 4,
BRANCH == "PCT_GDP",
UNIT %in% c("DNK", "SWE", "CHL")) %>%
COUNTRY left_join(SOCX_AGG_var$COUNTRY, by = "COUNTRY") %>%
%>%
year_to_date rename(Location = Country) %>%
left_join(colors, by = c("Location" = "country")) %>%
mutate(obsValue = obsValue / 100) %>%
ggplot() + theme_minimal() + ylab("Health (% of GDP)") + xlab("") +
geom_line(aes(x = date, y = obsValue, color = color)) +
scale_color_identity() + add_3flags +
scale_x_date(breaks = seq(1920, 2025, 5) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
theme(legend.position = c(0.25, 0.2),
legend.title = element_blank()) +
scale_y_continuous(breaks = 0.01*seq(-7, 16, 0.5),
labels = scales::percent_format(accuracy = 0.1))
Switzerland, Canada, Slovenia
Code
%>%
SOCX_AGG filter(SOURCE == 10,
== 0,
TYPROG == 0,
TYPEXP == 4,
BRANCH == "PCT_GDP",
UNIT %in% c("CHE", "CAN", "SVN")) %>%
COUNTRY left_join(SOCX_AGG_var$COUNTRY, by = "COUNTRY") %>%
%>%
year_to_date rename(Location = Country) %>%
left_join(colors, by = c("Location" = "country")) %>%
mutate(obsValue = obsValue / 100) %>%
ggplot() + theme_minimal() + ylab("Health (% of GDP)") + xlab("") +
geom_line(aes(x = date, y = obsValue, color = color)) +
scale_color_identity() + add_3flags +
scale_x_date(breaks = seq(1920, 2025, 5) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
theme(legend.position = c(0.1, 0.85),
legend.title = element_blank()) +
scale_y_continuous(breaks = 0.01*seq(-7, 16, 0.5),
labels = scales::percent_format(accuracy = 0.1))
Austria, Belgium, Italy
Code
%>%
SOCX_AGG filter(SOURCE == 10,
== 0,
TYPROG == 0,
TYPEXP == 4,
BRANCH == "PCT_GDP",
UNIT %in% c("BEL", "AUT", "ITA")) %>%
COUNTRY left_join(SOCX_AGG_var$COUNTRY, by = "COUNTRY") %>%
%>%
year_to_date rename(Location = Country) %>%
left_join(colors, by = c("Location" = "country")) %>%
mutate(obsValue = obsValue / 100) %>%
ggplot() + theme_minimal() + ylab("Health (% of GDP)") + xlab("") +
geom_line(aes(x = date, y = obsValue, color = color)) +
scale_color_identity() + add_3flags +
scale_x_date(breaks = seq(1920, 2025, 5) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
theme(legend.position = c(0.15, 0.85),
legend.title = element_blank()) +
scale_y_continuous(breaks = 0.01*seq(-7, 16, 0.5),
labels = scales::percent_format(accuracy = 0.1))
Unemployment - Cash Benefits (Branch 7)
World
Code
%>%
SOCX_AGG filter(SOURCE == 10,
== 0,
TYPROG == 1,
TYPEXP == 7,
BRANCH == "PCT_GDP") %>%
UNIT left_join(SOCX_AGG_var$COUNTRY, by = "COUNTRY") %>%
%>%
year_to_enddate mutate(year = year(date)) %>%
filter(year %in% c(1990, 2000, 2015)) %>%
arrange(Country, year) %>%
group_by(Country, COUNTRY) %>%
summarise(`1990 (% GDP)` = obsValue[1],
`2000 (% GDP)` = obsValue[2],
`2015 (% GDP)` = obsValue[3],
`Delta 2000-15` = obsValue[3] - obsValue[2]) %>%
arrange(-`2015 (% GDP)`) %>%
mutate_at(vars(-Country, -COUNTRY), funs(round(., digits = 1))) %>%
rename(`Country Name` = Country,
`Country Code` = COUNTRY) -> SOC_AGG_BRANCH_7
do.call(save, list("SOC_AGG_BRANCH_7", file = "SOC_AGG_BRANCH_7.RData"))
%>%
SOC_AGG_BRANCH_7 if (is_html_output()) datatable(., filter = 'top', rownames = F) else .} {
France, Germany, Netherlands
Code
%>%
SOCX_AGG filter(SOURCE == 10,
== 0,
TYPROG == 1,
TYPEXP == 7,
BRANCH == "PCT_GDP",
UNIT %in% c("FRA", "DEU", "NLD")) %>%
COUNTRY left_join(SOCX_AGG_var$COUNTRY, by = "COUNTRY") %>%
%>%
year_to_date rename(Location = Country) %>%
left_join(colors, by = c("Location" = "country")) %>%
mutate(obsValue = obsValue / 100) %>%
ggplot() + theme_minimal() + ylab("Unemployment - Cash Benefits (% of GDP)") + xlab("") +
geom_line(aes(x = date, y = obsValue, color = color)) +
scale_color_identity() + add_3flags +
scale_x_date(breaks = seq(1920, 2025, 5) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
theme(legend.position = c(0.25, 0.9),
legend.title = element_blank(),
legend.direction = "horizontal") +
scale_y_continuous(breaks = 0.01*seq(-7, 16, 0.5),
labels = scales::percent_format(accuracy = 0.1))
Chile, Denmark, Sweden
Code
%>%
SOCX_AGG filter(SOURCE == 10,
== 0,
TYPROG == 1,
TYPEXP == 7,
BRANCH == "PCT_GDP",
UNIT %in% c("DNK", "SWE", "CHL")) %>%
COUNTRY left_join(SOCX_AGG_var$COUNTRY, by = "COUNTRY") %>%
%>%
year_to_date rename(Location = Country) %>%
left_join(colors, by = c("Location" = "country")) %>%
mutate(obsValue = obsValue / 100) %>%
ggplot() + theme_minimal() + ylab("Unemployment - Cash Benefits (% of GDP)") + xlab("") +
geom_line(aes(x = date, y = obsValue, color = color)) +
scale_color_identity() + add_3flags +
scale_x_date(breaks = seq(1920, 2025, 5) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
theme(legend.position = c(0.25, 0.2),
legend.title = element_blank()) +
scale_y_continuous(breaks = 0.01*seq(-7, 16, 0.5),
labels = scales::percent_format(accuracy = 0.1))
Switzerland, Canada, Sloveinia
Code
%>%
SOCX_AGG filter(SOURCE == 10,
== 0,
TYPROG == 1,
TYPEXP == 7,
BRANCH == "PCT_GDP",
UNIT %in% c("CHE", "CAN", "SVN")) %>%
COUNTRY left_join(SOCX_AGG_var$COUNTRY, by = "COUNTRY") %>%
%>%
year_to_date rename(Location = Country) %>%
left_join(colors, by = c("Location" = "country")) %>%
mutate(obsValue = obsValue / 100) %>%
ggplot() + theme_minimal() + ylab("Unemployment - Cash Benefits (% of GDP)") + xlab("") +
geom_line(aes(x = date, y = obsValue, color = color)) +
scale_color_identity() + add_3flags +
scale_x_date(breaks = seq(1920, 2025, 5) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
theme(legend.position = c(0.75, 0.85),
legend.title = element_blank()) +
scale_y_continuous(breaks = 0.01*seq(-7, 16, 0.5),
labels = scales::percent_format(accuracy = 0.1))
Austria, Belgium, Italy
Code
%>%
SOCX_AGG filter(SOURCE == 10,
== 0,
TYPROG == 1,
TYPEXP == 7,
BRANCH == "PCT_GDP",
UNIT %in% c("BEL", "AUT", "ITA")) %>%
COUNTRY left_join(SOCX_AGG_var$COUNTRY, by = "COUNTRY") %>%
%>%
year_to_date rename(Location = Country) %>%
left_join(colors, by = c("Location" = "country")) %>%
mutate(obsValue = obsValue / 100) %>%
ggplot() + theme_minimal() + ylab("Unemployment - Cash Benefits (% of GDP)") + xlab("") +
geom_line(aes(x = date, y = obsValue, color = color)) +
scale_color_identity() + add_3flags +
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(-7, 16, 1),
labels = scales::percent_format(accuracy = 0.1))
France and Germany
Total - Old Age
Code
%>%
SOCX_AGG filter(SOURCE == 10,
== 0,
TYPROG == 1,
TYPEXP == 1,
BRANCH == "PCT_GDP",
UNIT %in% c("FRA", "DEU")) %>%
COUNTRY left_join(SOCX_AGG_var$COUNTRY, by = "COUNTRY") %>%
left_join(SOCX_AGG_var$BRANCH, by = "BRANCH") %>%
left_join(SOCX_AGG_var$TYPEXP, by = "TYPEXP") %>%
mutate(date = paste0(obsTime, "-01-01") %>% as.Date,
value = obsValue / 100) %>%
filter(year(date) >= 1990) %>%
arrange(Country) %>%
select(Branch, Country, Typexp, date, value) %>%
mutate(Variable = paste0(Branch, " - ", Typexp, " (", Country, ")")) %>%
ggplot() + geom_line(aes(x = date, y = value, color = Variable)) +
scale_color_manual(values = viridis(3)[1:2]) +
theme_minimal() +
scale_x_date(breaks = seq(1920, 2025, 2) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%y")) +
theme(legend.position = c(0.25, 0.9),
legend.title = element_blank()) +
scale_y_continuous(breaks = 0.01*seq(-7, 16, 0.5),
labels = scales::percent_format(accuracy = 0.1)) +
ylab("% of GDP") + xlab("")
Old age - Cash benefits
Code
%>%
SOCX_AGG filter(SOURCE == 10,
== 0,
TYPROG == 1,
TYPEXP == 1,
BRANCH == "PCT_GDP",
UNIT %in% c("FRA", "DEU")) %>%
COUNTRY left_join(SOCX_AGG_var$COUNTRY, by = "COUNTRY") %>%
left_join(SOCX_AGG_var$BRANCH, by = "BRANCH") %>%
left_join(SOCX_AGG_var$TYPEXP, by = "TYPEXP") %>%
mutate(date = paste0(obsTime, "-01-01") %>% as.Date,
value = obsValue / 100) %>%
filter(year(date) >= 1990) %>%
arrange(Country) %>%
select(Branch, Country, Typexp, date, value) %>%
mutate(Variable = paste0(Branch, " - ", Typexp, " (", Country, ")")) %>%
ggplot() + geom_line(aes(x = date, y = value, color = Variable)) +
scale_color_manual(values = viridis(3)[1:2]) +
theme_minimal() +
scale_x_date(breaks = seq(1920, 2025, 2) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%y")) +
theme(legend.position = c(0.25, 0.8),
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("")
Total - Cash Benefits
Code
%>%
SOCX_AGG filter(SOURCE == 10,
== 0,
TYPROG == 1,
TYPEXP == 90,
BRANCH == "PCT_GDP",
UNIT %in% c("FRA", "DEU")) %>%
COUNTRY left_join(SOCX_AGG_var$COUNTRY, by = "COUNTRY") %>%
left_join(SOCX_AGG_var$BRANCH, by = "BRANCH") %>%
left_join(SOCX_AGG_var$TYPEXP, by = "TYPEXP") %>%
mutate(date = paste0(obsTime, "-01-01") %>% as.Date,
value = obsValue / 100) %>%
filter(year(date) >= 1990) %>%
arrange(Country) %>%
select(Branch, Country, Typexp, date, value) %>%
mutate(Variable = paste0(Branch, " - ", Typexp, " (", Country, ")")) %>%
ggplot() + geom_line(aes(x = date, y = value, color = Variable)) +
scale_color_manual(values = viridis(3)[1:2]) +
theme_minimal() +
scale_x_date(breaks = seq(1920, 2025, 2) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%y")) +
theme(legend.position = c(0.25, 0.7),
legend.title = element_blank()) +
scale_y_continuous(breaks = 0.01*seq(-7, 26, 0.5),
labels = scales::percent_format(accuracy = 0.1)) +
ylab("% of GDP") + xlab("")
Health - Total
Code
%>%
SOCX_AGG filter(SOURCE == 10,
== 0,
TYPROG == 0,
TYPEXP == 4,
BRANCH == "PCT_GDP",
UNIT %in% c("FRA", "DEU")) %>%
COUNTRY left_join(SOCX_AGG_var$COUNTRY, by = "COUNTRY") %>%
left_join(SOCX_AGG_var$BRANCH, by = "BRANCH") %>%
left_join(SOCX_AGG_var$TYPEXP, by = "TYPEXP") %>%
mutate(date = paste0(obsTime, "-01-01") %>% as.Date,
value = obsValue / 100) %>%
filter(year(date) >= 1990) %>%
arrange(Country) %>%
select(Branch, Country, Typexp, date, value) %>%
mutate(Variable = paste0(Branch, " - ", Typexp, " (", Country, ")")) %>%
ggplot() + geom_line(aes(x = date, y = value, color = Variable)) +
scale_color_manual(values = viridis(3)[1:2]) +
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, 0.9),
legend.title = element_blank()) +
scale_y_continuous(breaks = 0.01*seq(-7, 26, 0.5),
labels = scales::percent_format(accuracy = 0.1)) +
ylab("% of GDP") + xlab("")
Family - Total
Code
%>%
SOCX_AGG filter(SOURCE == 10,
== 0,
TYPROG == 0,
TYPEXP == 5,
BRANCH == "PCT_GDP",
UNIT %in% c("FRA", "DEU")) %>%
COUNTRY left_join(SOCX_AGG_var$COUNTRY, by = "COUNTRY") %>%
left_join(SOCX_AGG_var$BRANCH, by = "BRANCH") %>%
left_join(SOCX_AGG_var$TYPEXP, by = "TYPEXP") %>%
mutate(date = paste0(obsTime, "-01-01") %>% as.Date,
value = obsValue / 100) %>%
filter(year(date) >= 1990) %>%
arrange(Country) %>%
select(Branch, Country, Typexp, date, value) %>%
mutate(Variable = paste0(Branch, " - ", Typexp, " (", Country, ")")) %>%
ggplot() + geom_line(aes(x = date, y = value, color = Variable)) +
scale_color_manual(values = viridis(3)[1:2]) +
theme_minimal() +
scale_x_date(breaks = seq(1920, 2025, 2) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%y")) +
theme(legend.position = c(0.15, 0.9),
legend.title = element_blank()) +
scale_y_continuous(breaks = 0.01*seq(-7, 26, 0.2),
labels = scales::percent_format(accuracy = 0.1)) +
ylab("% of GDP") + xlab("")
Housing - Total
Code
%>%
SOCX_AGG filter(SOURCE == 10,
== 0,
TYPROG == 0,
TYPEXP == 8,
BRANCH == "PCT_GDP",
UNIT %in% c("FRA", "DEU")) %>%
COUNTRY left_join(SOCX_AGG_var$COUNTRY, by = "COUNTRY") %>%
left_join(SOCX_AGG_var$BRANCH, by = "BRANCH") %>%
left_join(SOCX_AGG_var$TYPEXP, by = "TYPEXP") %>%
mutate(date = paste0(obsTime, "-01-01") %>% as.Date,
value = obsValue / 100) %>%
filter(year(date) >= 1990) %>%
arrange(Country) %>%
select(Branch, Country, Typexp, date, value) %>%
mutate(Variable = paste0(Branch, " - ", Typexp, " (", Country, ")")) %>%
ggplot() + geom_line(aes(x = date, y = value, color = Variable)) +
scale_color_manual(values = viridis(3)[1:2]) +
theme_minimal() +
scale_x_date(breaks = seq(1920, 2025, 2) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%y")) +
theme(legend.position = c(0.2, 0.5),
legend.title = element_blank()) +
scale_y_continuous(breaks = 0.01*seq(-7, 26, 0.1),
labels = scales::percent_format(accuracy = 0.1)) +
ylab("% of GDP") + xlab("")
10-112
Code
%>%
SOCX_AGG filter(SOURCE == 10,
== 112,
TYPROG == 1,
TYPEXP == 1,
BRANCH == "PCT_GDP",
UNIT %in% c("FRA", "DEU")) %>%
COUNTRY left_join(SOCX_AGG_var$COUNTRY, by = "COUNTRY") %>%
mutate(date = paste0(obsTime, "-01-01") %>% as.Date,
value = obsValue / 100) %>%
arrange(Country) %>%
select(Country, date, value) %>%
ggplot() + geom_line(aes(x = date, y = value, color = Country)) +
scale_color_manual(values = viridis(3)[1:2]) +
theme_minimal() +
scale_x_date(breaks = seq(1920, 2025, 2) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%y")) +
theme(legend.position = c(0.25, 0.4),
legend.title = element_blank()) +
scale_y_continuous(breaks = 0.01*seq(-7, 2, 0.1),
labels = scales::percent_format(accuracy = 0.1)) +
ylab("% of GDP") + xlab("")
Old age - Cash benefits
France, Germany, Netherlands
Code
%>%
SOCX_AGG filter(SOURCE == 10,
== 0,
TYPROG == 1,
TYPEXP == 1,
BRANCH == "PCT_GDP",
UNIT %in% c("FRA", "DEU", "NLD")) %>%
COUNTRY left_join(SOCX_AGG_var$COUNTRY, by = "COUNTRY") %>%
left_join(SOCX_AGG_var$BRANCH, by = "BRANCH") %>%
left_join(SOCX_AGG_var$TYPEXP, by = "TYPEXP") %>%
mutate(date = paste0(obsTime, "-01-01") %>% as.Date,
value = obsValue / 100) %>%
filter(year(date) >= 1990) %>%
arrange(Country) %>%
select(Branch, Country, Typexp, date, value) %>%
mutate(Variable = paste0(Branch, " - ", Typexp, " (", Country, ")")) %>%
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, 2) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%y")) +
theme(legend.position = c(0.25, 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("")
Belgium, Austria, Italy
Code
%>%
SOCX_AGG filter(SOURCE == 10,
== 0,
TYPROG == 1,
TYPEXP == 1,
BRANCH == "PCT_GDP",
UNIT %in% c("BEL", "AUT", "ITA")) %>%
COUNTRY left_join(SOCX_AGG_var$COUNTRY, by = "COUNTRY") %>%
left_join(SOCX_AGG_var$BRANCH, by = "BRANCH") %>%
left_join(SOCX_AGG_var$TYPEXP, by = "TYPEXP") %>%
mutate(date = paste0(obsTime, "-01-01") %>% as.Date,
value = obsValue / 100) %>%
filter(year(date) >= 1980) %>%
arrange(Country) %>%
select(Branch, Country, Typexp, date, value) %>%
mutate(Variable = paste0(Branch, " - ", Typexp, " (", Country, ")")) %>%
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, 2) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%y")) +
theme(legend.position = c(0.25, 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("")
Social aggregate, Public
World
Code
World
Code
Code