OECD’s API

Data - OECD

Author

Info

source	dataset	Title	.html	.rData
oecd	api	OECD's API	2026-07-24	2024-04-16

List of APIs

source	dataset	Title	.html	.rData
bdf	api	NA	NA	NA
bea	api	NA	NA	NA
bis	api	NA	NA	NA
bls	api	NA	NA	NA
dbnomics	api	NA	NA	NA
ecb	api	NA	NA	NA
eurostat	api	NA	NA	NA
imf	api	NA	NA	NA
insee	api	NA	NA	NA
oecd	api	OECD's API	2026-07-24	2024-04-16
wdi	api	NA	NA	NA

LAST_COMPILE

LAST_COMPILE
2026-07-26

Info

OECD Data API documentation: https://gitlab.algobank.oecd.org/public-documentation/dotstat-migration/-/raw/main/OECD_Data_API_documentation.pdf
Upgrading your queries from the legacy OECD.Stat APIs to the new OECD Data API. pdf

GDP Per capita, US vs. EU

Data

Code

QNA_EXPENDITURE_CAPITA <- "https://sdmx.oecd.org/public/rest/data/OECD.SDD.NAD,DSD_NAMAIN1@DF_QNA_EXPENDITURE_CAPITA/Q..USA+EA........LR.." %>%
  readSDMX() %>%
  as_tibble

Metadata

Download

Code

QNA_EXPENDITURE_CAPITA_var <- "https://sdmx.oecd.org/public/rest/dataflow/OECD.SDD.NAD/DSD_NAMAIN1@DF_QNA_EXPENDITURE_CAPITA/1.0?references=all" %>%
  readSDMX()

English

Code

metadata_load <- function(code, CL_code, data = QNA_EXPENDITURE_CAPITA_var){
  assign(code, as.data.frame(data@codelists, codelistId = CL_code) %>%
           select(id, label.en) %>%
           setNames(c(code, str_to_title(code))),
         envir = .GlobalEnv)
}
metadata_load("REF_AREA", "CL_AREA")
REF_AREA %>%
  print_table_conditional()

French

Code

metadata_load_fr <- function(code, CL_code, data = QNA_EXPENDITURE_CAPITA_var){
  assign(code, as.data.frame(data@codelists, codelistId = CL_code) %>%
           select(id, label.fr) %>%
           setNames(c(code, str_to_title(code))),
         envir = .GlobalEnv)
}
metadata_load_fr("REF_AREA", "CL_AREA")
REF_AREA %>%
  print_table_conditional()

Plot

Code

QNA_EXPENDITURE_CAPITA %>%
  mutate(date = zoo::as.yearqtr(obsTime, format = "%Y-Q%q") %>% as.Date()) %>%
  filter(year(date) >= 1999) %>%
  left_join(REF_AREA, by = "REF_AREA") %>%
  group_by(Ref_area) %>%
  arrange(date) %>%
  mutate(obsValue = 100 * obsValue / obsValue[1]) %>%
  ggplot(.) + theme_minimal() + xlab("") + ylab("PIB par habitant (1999T1 = 100)") +
  geom_line(aes(x = date, y = obsValue, color = Ref_area)) + 
  scale_color_manual(values = c("#B22234", "#003399")) +
  geom_rect(data = nber_recessions %>%
              filter(Peak > as.Date("1999-01-01")), 
            aes(xmin = Peak, xmax = Trough, ymin = 0, ymax = +Inf), 
            fill = '#B22234', alpha = 0.1)  +
  geom_rect(data = cepr_recessions %>%
              filter(Peak > as.Date("1999-01-01")), 
            aes(xmin = Peak, xmax = Trough, ymin = 0, ymax = +Inf), 
            fill = '#003399', alpha = 0.1)  +
  scale_x_date(breaks = c(seq(1998, 2100, 2)) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  theme(legend.position = c(0.26, 0.8),
        legend.title = element_blank()) +
  scale_y_log10(breaks = seq(50, 200, 5))