Government budget allocations for R and D
Data - OECD
François Geerolf
Info
Data on industry
LAST_COMPILE
| LAST_COMPILE |
|---|
| 2024-04-16 |
Last
| obsTime | Nobs |
|---|---|
| 2023 | 136 |
Measure
Data are provided in million national currency (for the euro zone, pre-EMU euro or EUR), million current PPP USD and million constant USD (2015 prices and PPPs).
Presentation
This table presents data on Government budget allocations for RD (GBARD) by socio-economic objective (SEO), using the NABS 2007 classification i.e.: Exploration and exploitation of the Earth, Environment, Exploration and exploitation of space, Transport, telecommunication and other infrastructures, Energy, Industrial production and technology, Health, Agriculture, Education, Culture, recreation, religion and mass media, Political and social systems, structures and processes, General advancement of knowledge: RD financed from General University Funds (GUF), General advancement of knowledge: RD financed from sources other than GUF, Defence. Please note that in this new NABS 2007 classification, the three socio-economic objectives – Education, Culture, recreation, religion and mass media, and Political and social systems, structures and processes – were previously grouped under a single objective: Social structures and relationships. At the time of this publication there is no breakdown of historical data into the three new SEOs. Another issue relating to the transition from NABS 1993 to NABS 2007 is that what was formerly Other civil research is now to be distributed among the other chapters. This distribution has not yet been done in this database. Therefore, until the countries are in a position to provide breakdown according to the NABS 2007 classification, in some cases GBAORD by SEO is greater than the sum of its chapters.
Data Structure
| id | description |
|---|---|
| COUNTRY | Country |
| SEO | Socio economic objective |
| MEASURE | Measure |
| YEAR | Year |
| OBS_VALUE | Observation Value |
| TIME_FORMAT | Time Format |
| OBS_STATUS | Observation Status |
| UNIT | Unit |
| POWERCODE | Unit multiplier |
| REFERENCEPERIOD | Reference period |
COUNTRY
GBARD_NABS2007 %>%
left_join(GBARD_NABS2007_var$COUNTRY, by = "COUNTRY") %>%
group_by(COUNTRY, Country) %>%
summarise(Nobs = n()) %>%
arrange(-Nobs) %>%
mutate(Flag = gsub(" ", "-", str_to_lower(Country)),
Flag = paste0('<img src="../../icon/flag/vsmall/', Flag, '.png" alt="Flag">')) %>%
select(Flag, everything()) %>%
{if (is_html_output()) datatable(., filter = 'top', rownames = F, escape = F) else .}SEO
MEASURE
GBARD_NABS2007 %>%
left_join(GBARD_NABS2007_var$MEASURE, by = "MEASURE") %>%
group_by(MEASURE, Measure) %>%
summarise(Nobs = n()) %>%
arrange(-Nobs) %>%
{if (is_html_output()) print_table(.) else .}| MEASURE | Measure | Nobs |
|---|---|---|
| MIO_NAC | National Currency | 20431 |
| DC6 | PPP Dollars - Current prices | 20295 |
| DF6 | 2015 Dollars - Constant prices and PPPs | 20295 |
obsTime
Table
2018
GBARD_NABS2007 %>%
filter(obsTime == 2018,
MEASURE == "DF6",
COUNTRY %in% c("DEU", "FRA", "GBR", "ITA")) %>%
left_join(GBARD_NABS2007_var$SEO, by = "SEO") %>%
left_join(GBARD_NABS2007_var$COUNTRY, by = "COUNTRY") %>%
select(Country, SEO, Seo, obsValue) %>%
mutate(obsValue = round(obsValue/1000, 1)) %>%
spread(Country, obsValue) %>%
{if (is_html_output()) datatable(., filter = 'top', rownames = F) else .}2011
GBARD_NABS2007 %>%
filter(obsTime == 2011,
MEASURE == "DF6",
COUNTRY %in% c("DEU", "FRA", "GBR", "ITA")) %>%
left_join(GBARD_NABS2007_var$SEO, by = "SEO") %>%
left_join(GBARD_NABS2007_var$COUNTRY, by = "COUNTRY") %>%
select(Country, SEO, Seo, obsValue) %>%
mutate(obsValue = round(obsValue/1000, 1)) %>%
spread(Country, obsValue) %>%
{if (is_html_output()) datatable(., filter = 'top', rownames = F) else .}2015
GBARD_NABS2007 %>%
filter(obsTime == 2015,
MEASURE == "DF6",
COUNTRY %in% c("DEU", "FRA", "GBR", "ITA")) %>%
left_join(GBARD_NABS2007_var$SEO, by = "SEO") %>%
left_join(GBARD_NABS2007_var$COUNTRY, by = "COUNTRY") %>%
select(Country, SEO, Seo, obsValue) %>%
mutate(obsValue = round(obsValue/1000, 1)) %>%
spread(Country, obsValue) %>%
{if (is_html_output()) datatable(., filter = 'top', rownames = F) else .}T - Total
Nobs
DF6 - 2015 Dollars - Constant prices and PPPs
GBARD_NABS2007 %>%
filter(SEO == "_T",
MEASURE == "DF6",
COUNTRY %in% c("DEU", "FRA", "GBR", "ITA")) %>%
left_join(GBARD_NABS2007_var$COUNTRY, by = "COUNTRY") %>%
year_to_date %>%
mutate(obsValue = obsValue/1000,
Location = Country) %>%
ggplot() + theme_minimal() + xlab("") + ylab("All Government budget allocations for R&D") +
geom_line(aes(x = date, y = obsValue, color = Country)) + add_4flags +
scale_color_manual(values = c("#002395", "#000000", "#009246", "#CF142B")) +
theme(legend.position = c(0.85, 0.9),
legend.title = element_blank()) +
scale_x_date(breaks = seq(1920, 2025, 5) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
scale_y_continuous(breaks = seq(0, 100, 2)) +
theme(legend.position = c(0.25, 0.9),
legend.title = element_blank())
DC6 - PPP Dollars - Current prices
GBARD_NABS2007 %>%
filter(SEO == "_T",
MEASURE == "DC6",
COUNTRY %in% c("DEU", "FRA", "ITA", "GBR")) %>%
left_join(GBARD_NABS2007_var$COUNTRY, by = "COUNTRY") %>%
year_to_date %>%
ggplot() + theme_minimal() + xlab("") + ylab("All Government budget allocations for R&D") +
geom_line(aes(x = date, y = obsValue/1000, color = Country)) +
scale_color_manual(values = c("#002395", "#000000", "#009246", "#CF142B")) +
theme(legend.position = c(0.85, 0.9),
legend.title = element_blank()) +
scale_x_date(breaks = seq(1920, 2025, 2) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%y")) +
scale_y_continuous(breaks = seq(0, 100, 2)) +
theme(legend.position = c(0.25, 0.9),
legend.title = element_blank())
MIO_NAC - National Currency
GBARD_NABS2007 %>%
filter(SEO == "_T",
MEASURE == "MIO_NAC",
COUNTRY %in% c("DEU", "FRA", "ITA", "GBR")) %>%
left_join(GBARD_NABS2007_var$COUNTRY, by = "COUNTRY") %>%
year_to_date %>%
ggplot() + theme_minimal() + xlab("") + ylab("Health Government budget allocations for R&D") +
geom_line(aes(x = date, y = obsValue/1000, color = Country)) +
scale_color_manual(values = c("#002395", "#000000", "#009246", "#CF142B")) +
theme(legend.position = c(0.85, 0.9),
legend.title = element_blank()) +
scale_x_date(breaks = seq(1920, 2025, 2) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%y")) +
scale_y_continuous(breaks = seq(0, 100, 2)) +
theme(legend.position = c(0.25, 0.9),
legend.title = element_blank())
NABS07 - Health
Nobs
Nobs - DC6
GBARD_NABS2007 %>%
filter(SEO == "NABS07",
MEASURE == "DC6") %>%
left_join(GBARD_NABS2007_var$COUNTRY, by = "COUNTRY") %>%
group_by(COUNTRY, Country, MEASURE) %>%
summarise(Nobs = n(),
first = first(obsTime),
last = last(obsTime)) %>%
arrange(-Nobs) %>%
{if (is_html_output()) datatable(., filter = 'top', rownames = F) else .}DF6 - 2015 Dollars - Constant prices and PPPs
Table
GBARD_NABS2007 %>%
filter(SEO == "NABS07",
MEASURE == "DF6",
obsTime %in% c("1983", "2018")) %>%
left_join(SNA_TABLE3 %>%
filter(TRANSACT == "POPNC",
MEASURE == "PER") %>%
select(COUNTRY = LOCATION, obsTime, POPNC = obsValue),
by = c("COUNTRY", "obsTime")) %>%
left_join(GBARD_NABS2007_var$COUNTRY, by = "COUNTRY") %>%
mutate(obsValue = 1000*obsValue/POPNC) %>%
select(COUNTRY, Country, obsTime, obsValue) %>%
spread(obsTime, obsValue) %>%
na.omit %>%
mutate(`Growth` = 100*(`2018` / `1983`-1)) %>%
arrange(Growth) %>%
mutate(Growth = paste0(round(`Growth`), "%")) %>%
mutate_at(vars(3, 4), funs(round(., 1))) %>%
mutate(Flag = gsub(" ", "-", str_to_lower(Country)),
Flag = paste0('<img src="../../icon/flag/vsmall/', Flag, '.png" alt="Flag">')) %>%
select(COU = Flag, everything()) %>%
{if (is_html_output()) datatable(., filter = 'top', rownames = F, escape = F, options = list(pageLength = 40)) else .}Germany, France, United Kingdom, Italy
Plot
GBARD_NABS2007 %>%
filter(SEO == "NABS07",
MEASURE == "DF6",
COUNTRY %in% c("DEU", "FRA", "GBR", "ITA")) %>%
left_join(GBARD_NABS2007_var$COUNTRY, by = "COUNTRY") %>%
year_to_date %>%
ggplot() + theme_minimal() + xlab("") + ylab("Health Government Budget Allocations for R&D (2015$ Bn)") +
geom_line(aes(x = date, y = obsValue/1000, color = Country)) +
scale_color_manual(values = c("#002395", "#000000", "#009246", "#CF142B")) +
geom_image(data = . %>%
group_by(Country) %>%
summarise(date = last(date),
obsValue = last(obsValue)) %>%
mutate(date = as.Date("2020-01-01"),
image = paste0("../../icon/flag/", str_to_lower(gsub(" ", "-", Country)), ".png")),
aes(x = date, y = obsValue/1000, image = image), asp = 1.5) +
theme(legend.position = "none") +
scale_x_date(breaks = seq(1920, 2025, 2) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%y")) +
scale_y_continuous(breaks = seq(0, 5, 0.5))
Plot - français
GBARD_NABS2007 %>%
filter(SEO == "NABS07",
MEASURE == "DF6",
COUNTRY %in% c("DEU", "FRA", "GBR", "ITA")) %>%
left_join(GBARD_NABS2007_var$COUNTRY, by = "COUNTRY") %>%
year_to_date %>%
ggplot() + theme_minimal() + xlab("") + ylab("Santé - Crédits Budgétaires Publics de R&D ($2015, PPA)") +
geom_line(aes(x = date, y = obsValue/1000, color = Country)) +
scale_color_manual(values = c("#002395", "#000000", "#009246", "#CF142B")) +
geom_image(data = . %>%
group_by(Country) %>%
summarise(date = last(date),
obsValue = last(obsValue)) %>%
mutate(date = as.Date("2020-01-01"),
image = paste0("../../icon/flag/", str_to_lower(gsub(" ", "-", Country)), ".png")),
aes(x = date, y = obsValue/1000, image = image), asp = 1.5) +
theme(legend.position = "none") +
scale_x_date(breaks = seq(1920, 2025, 2) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%y")) +
scale_y_continuous(breaks = seq(0, 5, 0.5))
Table
GBARD_NABS2007 %>%
filter(SEO == "NABS07",
MEASURE == "DF6",
COUNTRY %in% c("DEU", "FRA", "GBR", "ITA"),
obsTime %in% c("2011", 2018)) %>%
left_join(GBARD_NABS2007_var$COUNTRY, by = "COUNTRY") %>%
select(COUNTRY, Country, obsTime, obsValue) %>%
spread(obsTime, obsValue) %>%
mutate(`Growth (%)` = (`2018`/`2011`-1)*100) %>%
mutate_at(vars(-COUNTRY, -Country), funs(round(., 1))) %>%
{if (is_html_output()) print_table(.) else .}| COUNTRY | Country | 2011 | 2018 | Growth (%) |
|---|---|---|---|---|
| DEU | Germany | 1508.8 | 1996.1 | 32.3 |
| FRA | France | 1464.5 | 1225.5 | -16.3 |
| GBR | United Kingdom | 2944.4 | 3486.5 | 18.4 |
| ITA | Italy | 1365.3 | 1201.4 | -12.0 |
South Korea, Switzerland, Spain
GBARD_NABS2007 %>%
filter(SEO == "NABS07",
MEASURE == "DF6",
COUNTRY %in% c("KOR", "CHE", "ESP")) %>%
left_join(GBARD_NABS2007_var$COUNTRY, by = "COUNTRY") %>%
year_to_date %>%
ggplot() + theme_minimal() + xlab("") + ylab("Health Government budget allocations for R&D") +
geom_line(aes(x = date, y = obsValue/1000, color = Country)) +
scale_color_manual(values = c("#002395", "#000000", "#009246", "#CF142B")) +
geom_image(data = . %>%
group_by(Country) %>%
summarise(date = last(date),
obsValue = last(obsValue)) %>%
mutate(date = as.Date("2020-01-01"),
image = paste0("../../icon/flag/", str_to_lower(gsub(" ", "-", Country)), ".png")),
aes(x = date, y = obsValue/1000, image = image), asp = 1.5) +
theme(legend.position = c(0.85, 0.9),
legend.title = element_blank()) +
scale_x_date(breaks = seq(1920, 2025, 2) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%y")) +
scale_y_continuous(breaks = seq(0, 5, 0.5)) +
theme(legend.position = c(0.25, 0.9),
legend.title = element_blank())
DC6 - PPP Dollars - Current prices
GBARD_NABS2007 %>%
filter(SEO == "NABS07",
MEASURE == "DC6",
COUNTRY %in% c("DEU", "FRA", "ITA", "GBR")) %>%
left_join(GBARD_NABS2007_var$COUNTRY, by = "COUNTRY") %>%
year_to_date %>%
ggplot() + theme_minimal() + xlab("") + ylab("Health Government budget allocations for R&D") +
geom_line(aes(x = date, y = obsValue/1000, color = Country)) +
scale_color_manual(values = c("#002395", "#000000", "#009246", "#CF142B")) +
geom_image(data = . %>%
group_by(Country) %>%
summarise(date = last(date),
obsValue = last(obsValue)) %>%
mutate(date = as.Date("2020-01-01"),
image = paste0("../../icon/flag/", str_to_lower(gsub(" ", "-", Country)), ".png")),
aes(x = date, y = obsValue/1000, image = image), asp = 1.5) +
theme(legend.position = c(0.85, 0.9),
legend.title = element_blank()) +
scale_x_date(breaks = seq(1920, 2025, 2) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%y")) +
scale_y_continuous(breaks = seq(0, 5, 0.5)) +
theme(legend.position = c(0.25, 0.9),
legend.title = element_blank())
MIO_NAC - National Currency
Germany, France, United Kingdom, Italy
GBARD_NABS2007 %>%
filter(SEO == "NABS07",
MEASURE == "MIO_NAC",
COUNTRY %in% c("DEU", "FRA", "ITA", "GBR")) %>%
left_join(GBARD_NABS2007_var$COUNTRY, by = "COUNTRY") %>%
year_to_date %>%
ggplot() + theme_minimal() + xlab("") + ylab("Health Government budget allocations for R&D") +
geom_line(aes(x = date, y = obsValue/1000, color = Country)) +
scale_color_manual(values = c("#002395", "#000000", "#009246", "#CF142B")) +
geom_image(data = . %>%
group_by(Country) %>%
summarise(date = last(date),
obsValue = last(obsValue)) %>%
mutate(date = as.Date("2020-01-01"),
image = paste0("../../icon/flag/", str_to_lower(gsub(" ", "-", Country)), ".png")),
aes(x = date, y = obsValue/1000, image = image), asp = 1.5) +
theme(legend.position = c(0.85, 0.9),
legend.title = element_blank()) +
scale_x_date(breaks = seq(1920, 2025, 2) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%y")) +
scale_y_continuous(breaks = seq(0, 5, 0.5)) +
theme(legend.position = c(0.25, 0.9),
legend.title = element_blank())
NABS133 - R&D related to Medical and health sciences - financed from sources other than GUF
Nobs
DF6 - 2015 Dollars - Constant prices and PPPs
GBARD_NABS2007 %>%
filter(SEO == "NABS133",
MEASURE == "DF6",
COUNTRY %in% c("DEU", "FRA", "GBR", "ITA")) %>%
left_join(GBARD_NABS2007_var$COUNTRY, by = "COUNTRY") %>%
year_to_date %>%
ggplot() + theme_minimal() + xlab("") + ylab("R&D related to Medical and health sciences") +
geom_line(aes(x = date, y = obsValue/1000, color = Country)) +
scale_color_manual(values = c("#002395", "#000000", "#009246", "#CF142B")) +
theme(legend.position = c(0.85, 0.9),
legend.title = element_blank()) +
scale_x_date(breaks = seq(1920, 2025, 2) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%y")) +
scale_y_continuous(breaks = seq(0, 5, 0.5)) +
theme(legend.position = c(0.25, 0.9),
legend.title = element_blank())
DC6 - PPP Dollars - Current prices
GBARD_NABS2007 %>%
filter(SEO == "NABS133",
MEASURE == "DC6",
COUNTRY %in% c("DEU", "FRA", "ITA", "GBR")) %>%
left_join(GBARD_NABS2007_var$COUNTRY, by = "COUNTRY") %>%
year_to_date %>%
ggplot() + theme_minimal() + xlab("") + ylab("R&D related to Medical and health sciences") +
geom_line(aes(x = date, y = obsValue/1000, color = Country)) +
scale_color_manual(values = c("#002395", "#000000", "#009246", "#CF142B")) +
theme(legend.position = c(0.85, 0.9),
legend.title = element_blank()) +
scale_x_date(breaks = seq(1920, 2025, 2) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%y")) +
scale_y_continuous(breaks = seq(0, 5, 0.5)) +
theme(legend.position = c(0.25, 0.9),
legend.title = element_blank())
MIO_NAC - National Currency
GBARD_NABS2007 %>%
filter(SEO == "NABS133",
MEASURE == "MIO_NAC",
COUNTRY %in% c("DEU", "FRA", "ITA", "GBR")) %>%
left_join(GBARD_NABS2007_var$COUNTRY, by = "COUNTRY") %>%
year_to_date %>%
ggplot() + theme_minimal() + xlab("") + ylab("Health Government budget allocations for R&D") +
geom_line(aes(x = date, y = obsValue/1000, color = Country)) +
scale_color_manual(values = c("#002395", "#000000", "#009246", "#CF142B")) +
theme(legend.position = c(0.85, 0.9),
legend.title = element_blank()) +
scale_x_date(breaks = seq(1920, 2025, 2) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%y")) +
scale_y_continuous(breaks = seq(0, 5, 0.5)) +
theme(legend.position = c(0.25, 0.9),
legend.title = element_blank())
NABS123 - R&D related to Medical and health sciences - financed from GUF
Nobs
DF6 - 2015 Dollars - Constant prices and PPPs
GBARD_NABS2007 %>%
filter(SEO == "NABS123",
MEASURE == "DF6",
COUNTRY %in% c("DEU", "FRA", "GBR", "ITA")) %>%
left_join(GBARD_NABS2007_var$COUNTRY, by = "COUNTRY") %>%
year_to_date %>%
ggplot() + theme_minimal() + xlab("") + ylab("R&D related to Medical and health sciences - financed from GUF") +
geom_line(aes(x = date, y = obsValue/1000, color = Country)) +
scale_color_manual(values = c("#002395", "#000000", "#009246", "#CF142B")) +
theme(legend.position = c(0.85, 0.9),
legend.title = element_blank()) +
scale_x_date(breaks = seq(1920, 2025, 2) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%y")) +
scale_y_continuous(breaks = seq(0, 5, 0.5)) +
theme(legend.position = c(0.25, 0.9),
legend.title = element_blank())
DC6 - PPP Dollars - Current prices
GBARD_NABS2007 %>%
filter(SEO == "NABS123",
MEASURE == "DC6",
COUNTRY %in% c("DEU", "FRA", "ITA", "GBR")) %>%
left_join(GBARD_NABS2007_var$COUNTRY, by = "COUNTRY") %>%
year_to_date %>%
ggplot() + theme_minimal() + xlab("") + ylab("Health Government budget allocations for R&D") +
geom_line(aes(x = date, y = obsValue/1000, color = Country)) +
scale_color_manual(values = c("#002395", "#000000", "#009246", "#CF142B")) +
theme(legend.position = c(0.85, 0.9),
legend.title = element_blank()) +
scale_x_date(breaks = seq(1920, 2025, 2) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%y")) +
scale_y_continuous(breaks = seq(0, 5, 0.5)) +
theme(legend.position = c(0.25, 0.9),
legend.title = element_blank())
MIO_NAC - National Currency
GBARD_NABS2007 %>%
filter(SEO == "NABS123",
MEASURE == "MIO_NAC",
COUNTRY %in% c("DEU", "FRA", "ITA", "GBR")) %>%
left_join(GBARD_NABS2007_var$COUNTRY, by = "COUNTRY") %>%
year_to_date %>%
ggplot() + theme_minimal() + xlab("") + ylab("Health Government budget allocations for R&D") +
geom_line(aes(x = date, y = obsValue/1000, color = Country)) +
scale_color_manual(values = c("#002395", "#000000", "#009246", "#CF142B")) +
theme(legend.position = c(0.85, 0.9),
legend.title = element_blank()) +
scale_x_date(breaks = seq(1920, 2025, 2) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%y")) +
scale_y_continuous(breaks = seq(0, 5, 0.5)) +
theme(legend.position = c(0.25, 0.9),
legend.title = element_blank())