Social Expenditure - Aggregated data - SOCX_AGG

Data - OECD

Author

François Geerolf

Info

source dataset Title .html .rData
oecd SOCX_AGG Social Expenditure - Aggregated data - SOCX_AGG 2025-09-29 2024-06-30

Data on germany

source dataset Title .html .rData
oecd QNA Quarterly National Accounts 2024-06-06 2025-05-24
wdi NY.GDP.PCAP.PP.CD GDP per capita, PPP (current international D) 2025-11-15 2025-11-15

LAST_COMPILE

LAST_COMPILE
2025-11-17

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 .}

Social aggregate, Public

World

Code 
SOCX_AGG %>%
  filter(SOURCE == 10, 
         TYPROG == 0, 
         TYPEXP == 0, 
         BRANCH %in% c(90, 1), 
         UNIT == "PCT_GDP") %>%
  left_join(SOCX_AGG_var$COUNTRY, by = "COUNTRY") %>%
  left_join(SOCX_AGG_var$BRANCH, by = "BRANCH") %>%
  group_by(COUNTRY, Country, Branch) %>%
  arrange(obsTime) %>%
  summarise(obsTime = last(obsTime),
            obsValue = last(obsValue)) %>%
  select(-obsTime) %>%
  arrange(-obsValue)  %>%
  spread(Branch, obsValue) %>%
  mutate(`Total - Old age` = Total - `Old age`) %>%
  arrange(-`Total - Old age`)
# # A tibble: 39 × 5
# # Groups:   COUNTRY, Country [39]
#    COUNTRY Country `Old age` Total `Total - Old age`
#    <chr>   <chr>       <dbl> <dbl>             <dbl>
#  1 CAN     Canada       4.94  24.9              20.0
#  2 BEL     Belgium     10.2   29.0              18.8
#  3 ESP     Spain        9.58  28.1              18.5
#  4 FRA     France      13.3   31.6              18.3
#  5 DEU     Germany      8.68  26.7              18.0
#  6 ISL     Iceland      3.24  20.8              17.5
#  7 AUT     Austria     12.1   29.4              17.2
#  8 CHL     Chile        2.65  19.6              17.0
#  9 ITA     Italy       13.4   30.1              16.6
# 10 FIN     Finland     12.7   29.0              16.3
# # ℹ 29 more rows

World

Code 
SOCX_AGG %>%
  filter(SOURCE == 10, 
         TYPROG == 0, 
         TYPEXP == 0, 
         BRANCH %in% c("90",  "1_2"), 
         UNIT == "PCT_GDP") %>%
  left_join(SOCX_AGG_var$COUNTRY, by = "COUNTRY") %>%
  left_join(SOCX_AGG_var$BRANCH, by = "BRANCH") %>%
  group_by(COUNTRY, Country, Branch) %>%
  arrange(obsTime) %>%
  summarise(obsTime = last(obsTime),
            obsValue = last(obsValue)) %>%
  select(-obsTime) %>%
  arrange(-obsValue)  %>%
  spread(Branch, obsValue) %>%
  mutate(`Total - Old age and Survivors` = Total - `Old age and Survivors`) %>%
  arrange(-`Total - Old age and Survivors`) %>%
  View
Code 
SOCX_AGG %>%
  filter(SOURCE == 10, 
         TYPROG == 0, 
         TYPEXP == 0, 
         UNIT == "PCT_GDP",
         obsTime == "2019") %>%
  left_join(SOCX_AGG_var$COUNTRY, by = "COUNTRY") %>%
  left_join(SOCX_AGG_var$BRANCH, by = "BRANCH") %>%
  spread(Branch, obsValue) %>%
  mutate(`Total - Old age` = Total - `Old age`) %>%
  arrange(-`Total - Old age`)
# # A tibble: 429 × 25
#    SOURCE BRANCH TYPEXP TYPROG UNIT    COUNTRY TIME_FORMAT UNIT.1  POWERCODE
#    <chr>  <chr>  <chr>  <chr>  <chr>   <chr>   <chr>       <chr>   <chr>    
#  1 10     1      0      0      PCT_GDP AUS     P1Y         PCT_GDP 0        
#  2 10     1      0      0      PCT_GDP AUT     P1Y         PCT_GDP 0        
#  3 10     1      0      0      PCT_GDP BEL     P1Y         PCT_GDP 0        
#  4 10     1      0      0      PCT_GDP CAN     P1Y         PCT_GDP 0        
#  5 10     1      0      0      PCT_GDP CHE     P1Y         PCT_GDP 0        
#  6 10     1      0      0      PCT_GDP CHL     P1Y         PCT_GDP 0        
#  7 10     1      0      0      PCT_GDP COL     P1Y         PCT_GDP 0        
#  8 10     1      0      0      PCT_GDP CRI     P1Y         PCT_GDP 0        
#  9 10     1      0      0      PCT_GDP CZE     P1Y         PCT_GDP 0        
# 10 10     1      0      0      PCT_GDP DEU     P1Y         PCT_GDP 0        
# # ℹ 419 more rows
# # ℹ 16 more variables: REFERENCEPERIOD <chr>, obsTime <chr>, OBS_STATUS <chr>,
# #   Country <chr>, `Active labour market programmes` <dbl>, Family <dbl>,
# #   Health <dbl>, Housing <dbl>, `Incapacity related` <dbl>, `Old age` <dbl>,
# #   `Old age and Survivors` <dbl>, `Other social policy areas` <dbl>,
# #   Survivors <dbl>, Total <dbl>, Unemployment <dbl>, `Total - Old age` <dbl>

All

Cash

Code 
SOCX_AGG %>%
  filter(SOURCE == 10, 
         TYPROG == 0, 
         TYPEXP == 1, 
         COUNTRY %in% c("USA", "FRA", "DEU", "GBR"),
         UNIT == "PCT_GDP",
         obsTime == "2015") %>%
  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, 
         TYPROG == 0, 
         TYPEXP == 2, 
         COUNTRY %in% c("USA", "FRA", "DEU", "GBR"),
         UNIT == "PCT_GDP",
         obsTime == "2015") %>%
  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, 
         TYPROG == 0, 
         TYPEXP == 0, 
         COUNTRY %in% c("USA", "FRA", "DEU", "GBR"),
         UNIT == "PCT_GDP",
         obsTime == "2015") %>%
  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, 
         TYPROG == 0, 
         TYPEXP == 1, 
         BRANCH == 90, 
         UNIT == "PCT_GDP") %>%
  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, 
         TYPROG == 0, 
         TYPEXP == 1, 
         BRANCH == 90, 
         UNIT == "PCT_GDP",
         COUNTRY %in% c("USA", "GBR", "AUS")) %>%
  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, 
         TYPROG == 0, 
         TYPEXP == 1, 
         BRANCH == 90, 
         UNIT == "PCT_GDP",
         COUNTRY %in% c("FRA", "DEU", "NLD")) %>%
  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, 
         TYPROG == 0, 
         TYPEXP == 1, 
         BRANCH == 90, 
         UNIT == "PCT_GDP",
         COUNTRY %in% c("DNK", "SWE", "CHL")) %>%
  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, 
         TYPROG == 0, 
         TYPEXP == 1, 
         BRANCH == 90, 
         UNIT == "PCT_GDP",
         COUNTRY %in% c("CHE", "CAN", "SVN")) %>%
  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, 
         TYPROG == 0, 
         TYPEXP == 1, 
         BRANCH == 90, 
         UNIT == "PCT_GDP",
         COUNTRY %in% c("BEL", "AUT", "ITA")) %>%
  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, 
         TYPROG == 0, 
         TYPEXP == 1, 
         BRANCH == 1, 
         UNIT == "PCT_GDP") %>%
  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, 
         TYPEXP == 1, 
         BRANCH == 1, 
         UNIT == "PCT_GDP",
         obsTime == "2014") %>%
  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, 
         TYPEXP == 1, 
         BRANCH == 1, 
         UNIT == "PCT_GDP",
         obsTime == "2017") %>%
  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, 
         TYPROG == 0, 
         TYPEXP == 1, 
         BRANCH == 1, 
         UNIT == "PCT_GDP",
         COUNTRY %in% c("USA", "GBR", "AUS")) %>%
  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, 
         TYPROG == 0, 
         TYPEXP == 1, 
         BRANCH == 1, 
         UNIT == "PCT_GDP",
         COUNTRY %in% c("FRA", "DEU", "NLD")) %>%
  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, 
         TYPROG == 0, 
         TYPEXP == 1, 
         BRANCH == 1, 
         UNIT == "PCT_GDP",
         COUNTRY %in% c("FRA", "DEU", "NLD")) %>%
  mutate(Country = case_when(COUNTRY == "FRA" ~ "France",
                             COUNTRY == "DEU" ~ "Allemagne",
                             COUNTRY == "NLD" ~ "Pays-Bas")) %>%
  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, 
         TYPROG == 0, 
         TYPEXP == 1, 
         BRANCH == 1, 
         UNIT == "PCT_GDP",
         COUNTRY %in% c("DNK", "SWE", "CHL")) %>%
  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, 
         TYPROG == 0, 
         TYPEXP == 1, 
         BRANCH == 1, 
         UNIT == "PCT_GDP",
         COUNTRY %in% c("CHE", "CAN", "SVN")) %>%
  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, 
         TYPROG == 0, 
         TYPEXP == 1, 
         BRANCH == 1, 
         UNIT == "PCT_GDP",
         COUNTRY %in% c("BEL", "AUT", "ITA")) %>%
  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, 
         TYPROG == 0, 
         TYPEXP == 1, 
         BRANCH == 2, 
         UNIT == "PCT_GDP") %>%
  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, 
         TYPROG == 0, 
         TYPEXP == 1, 
         BRANCH == 2, 
         UNIT == "PCT_GDP",
         COUNTRY %in% c("USA", "GBR", "AUS")) %>%
  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, 
         TYPROG == 0, 
         TYPEXP == 1, 
         BRANCH == 2, 
         UNIT == "PCT_GDP",
         COUNTRY %in% c("FRA", "DEU", "NLD")) %>%
  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, 
         TYPROG == 0, 
         TYPEXP == 1, 
         BRANCH == 2, 
         UNIT == "PCT_GDP",
         COUNTRY %in% c("DNK", "SWE", "CHL")) %>%
  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, 
         TYPROG == 0, 
         TYPEXP == 1, 
         BRANCH == 2, 
         UNIT == "PCT_GDP",
         COUNTRY %in% c("CHE", "CAN", "SVN")) %>%
  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, 
         TYPROG == 0, 
         TYPEXP == 1, 
         BRANCH == 2, 
         UNIT == "PCT_GDP",
         COUNTRY %in% c("BEL", "AUT", "ITA")) %>%
  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, 
         TYPROG == 0, 
         TYPEXP == 0, 
         BRANCH == 4, 
         UNIT == "PCT_GDP") %>%
  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, 
         TYPROG == 0, 
         TYPEXP == 0, 
         BRANCH == 4, 
         UNIT == "PCT_GDP",
         COUNTRY %in% c("USA", "GBR", "AUS")) %>%
  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, 
         TYPROG == 0, 
         TYPEXP == 0, 
         BRANCH == 4, 
         UNIT == "PCT_GDP",
         COUNTRY %in% c("FRA", "DEU", "NLD")) %>%
  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, 
         TYPROG == 0, 
         TYPEXP == 0, 
         BRANCH == 4, 
         UNIT == "PCT_GDP",
         COUNTRY %in% c("DNK", "SWE", "CHL")) %>%
  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, 
         TYPROG == 0, 
         TYPEXP == 0, 
         BRANCH == 4, 
         UNIT == "PCT_GDP",
         COUNTRY %in% c("CHE", "CAN", "SVN")) %>%
  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, 
         TYPROG == 0, 
         TYPEXP == 0, 
         BRANCH == 4, 
         UNIT == "PCT_GDP",
         COUNTRY %in% c("BEL", "AUT", "ITA")) %>%
  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, 
         TYPROG == 0, 
         TYPEXP == 1, 
         BRANCH == 7, 
         UNIT == "PCT_GDP") %>%
  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, 
         TYPROG == 0, 
         TYPEXP == 1, 
         BRANCH == 7, 
         UNIT == "PCT_GDP",
         COUNTRY %in% c("FRA", "DEU", "NLD")) %>%
  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, 
         TYPROG == 0, 
         TYPEXP == 1, 
         BRANCH == 7, 
         UNIT == "PCT_GDP",
         COUNTRY %in% c("DNK", "SWE", "CHL")) %>%
  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, 
         TYPROG == 0, 
         TYPEXP == 1, 
         BRANCH == 7, 
         UNIT == "PCT_GDP",
         COUNTRY %in% c("CHE", "CAN", "SVN")) %>%
  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, 
         TYPROG == 0, 
         TYPEXP == 1, 
         BRANCH == 7, 
         UNIT == "PCT_GDP",
         COUNTRY %in% c("BEL", "AUT", "ITA")) %>%
  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, 
         TYPROG == 0, 
         TYPEXP == 1, 
         BRANCH == 1, 
         UNIT == "PCT_GDP",
         COUNTRY %in% c("FRA", "DEU")) %>%
  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, 
         TYPROG == 0, 
         TYPEXP == 1, 
         BRANCH == 1, 
         UNIT == "PCT_GDP",
         COUNTRY %in% c("FRA", "DEU")) %>%
  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, 
         TYPROG == 0, 
         TYPEXP == 1, 
         BRANCH == 90, 
         UNIT == "PCT_GDP", 
         COUNTRY %in% c("FRA", "DEU")) %>%
  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, 
         TYPROG == 0, 
         TYPEXP == 0, 
         BRANCH == 4, 
         UNIT == "PCT_GDP",
         COUNTRY %in% c("FRA", "DEU")) %>%
  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, 
         TYPROG == 0, 
         TYPEXP == 0, 
         BRANCH == 5, 
         UNIT == "PCT_GDP",
         COUNTRY %in% c("FRA", "DEU")) %>%
  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, 
         TYPROG == 0, 
         TYPEXP == 0, 
         BRANCH == 8, 
         UNIT == "PCT_GDP",
         COUNTRY %in% c("FRA", "DEU")) %>%
  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, 
         TYPROG == 112, 
         TYPEXP == 1, 
         BRANCH == 1, 
         UNIT == "PCT_GDP",
         COUNTRY %in% c("FRA", "DEU")) %>%
  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, 
         TYPROG == 0, 
         TYPEXP == 1, 
         BRANCH == 1, 
         UNIT == "PCT_GDP",
         COUNTRY %in% c("FRA", "DEU", "NLD")) %>%
  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, 
         TYPROG == 0, 
         TYPEXP == 1, 
         BRANCH == 1, 
         UNIT == "PCT_GDP",
         COUNTRY %in% c("BEL", "AUT", "ITA")) %>%
  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("")