Data - OECD
This dataset contains Purchasing Power Parities (PPPs) for all OECD countries. PPPs are the rates of currency conversion that eliminate the differences in price levels between countries. Per capita volume indices based on PPP converted data reflect only differences in the volume of goods and services produced. Comparative price levels are defined as the ratios of PPPs to exchange rates. They provide measures of the differences in price levels between countries. The PPPs are given in national currency units per US dollar. The price levels and volume indices derived using these PPPs have been rebased on the OECD average
inflation |>
arrange(-(dataset == "PPP2017")) |>
print_table_conditional_30()| source | dataset |
|---|---|
| oecd | PPP2017 |
| bis | CPI |
| ecb | CES |
| eurostat | nama_10_co3_p3 |
| eurostat | prc_hicp_cow |
| eurostat | prc_hicp_ctrb |
| eurostat | prc_hicp_inw |
| eurostat | prc_hicp_manr |
| eurostat | prc_hicp_midx |
| eurostat | prc_hicp_mmor |
| eurostat | prc_ppp_ind |
| eurostat | sts_inpp_m |
| eurostat | sts_inppd_m |
| eurostat | sts_inppnd_m |
| fred | cpi |
| fred | inflation |
| imf | CPI |
| oecd | MEI_PRICES_PPI |
| oecd | PRICES_CPI |
| wdi | FP.CPI.TOTL.ZG |
| wdi | NY.GDP.DEFL.KD.ZG |
| obsTime | Nobs |
|---|---|
| 2017 | 56680 |
PPP2017 |>
left_join(PPP2017_var$TAB, by = "TAB") |>
group_by(TAB, Tab) |>
summarise(Nobs = n()) |>
arrange(-Nobs) |>
print_table_conditional()| TAB | Tab | Nobs |
|---|---|---|
| TAB1_1 | Table 1.1: Nominal expenditure in national currency (millions) | 2548 |
| TAB1_3 | Table 1.3: Nominal expenditure in US dollars (millions) | 2548 |
| TAB1_4 | Table 1.4: Nominal expenditure per capita in US dollars | 2548 |
| TAB2_1 | Table 2.1: Nominal expenditure in national currency (millions) | 2548 |
| TAB2_3 | Table 2.3: Nominal expenditure in euros (millions) | 2548 |
| TAB2_4 | Table 2.4: Nominal expenditure per capita in euros | 2548 |
| TAB1_11 | Table 1.11: Price level indices (OECD=100) | 2496 |
| TAB1_12 | Table 1.12: Purchasing Power Parities (USA=1) | 2496 |
| TAB1_2 | Table 1.2: Purchasing Power Parities (OECD=1) | 2496 |
| TAB2_11 | Table 2.11: Price level indices (EU28=100) | 2496 |
| TAB2_2 | Table 2.2: Purchasing Power Parities (EU28=1) | 2496 |
| TAB1_7 | Table 1.7: Real expenditure in PPPs (International US dollars: OECD PPP=1) | 2444 |
| TAB1_8 | Table 1.8: Real expenditure per capita in PPPs (International US dollars: OECD PPP=1) | 2444 |
| TAB2_7 | Table 2.7: Real expenditure in PPPs (EU28 PPP=1) | 2444 |
| TAB2_8 | Table 2.8: Real expenditure per capita in PPPs (EU28 PPP=1) | 2444 |
| TAB1_10 | Table 1.10: Volume indices of real expenditure per capita (OECD=100) | 2392 |
| TAB1_5 | Table 1.5: Indices of nominal expenditure (OECD=100) | 2392 |
| TAB1_6 | Table 1.6: Indices of nominal expenditure per capita (OECD=100) | 2392 |
| TAB1_9 | Table 1.9: Volume indices of real expenditure (OECD=100) | 2392 |
| TAB2_10 | Table 2.10: Volume indices of real expenditure per capita (EU28=100) | 2392 |
| TAB2_5 | Table 2.5: Indices of nominal expenditure (EU28=100) | 2392 |
| TAB2_6 | Table 2.6: Indices of nominal expenditure per capita (EU28=100) | 2392 |
| TAB2_9 | Table 2.9: Volume indices of real expenditure (EU28=100) | 2392 |
PPP2017 |>
left_join(PPP2017_var$CAT, by = "CAT") |>
group_by(CAT, Cat) |>
summarise(Nobs = n()) |>
arrange(-Nobs) |>
print_table_conditional()PPP2017 |>
left_join(PPP2017_var$UNIT, by = "UNIT") |>
group_by(UNIT, Unit) |>
summarise(Nobs = n()) |>
print_table_conditional()| UNIT | Unit | Nobs |
|---|---|---|
| EUIDX | European Union = 100 | 12064 |
| EUR | Euro | 9984 |
| NATCUR | National currency | 5096 |
| NATEU | National currency per Euro | 2496 |
| NATUSD | National currency per US dollar | 4992 |
| OECDIDX | OECD=100 | 12064 |
| USD | US Dollar | 9984 |
PPP2017 |>
left_join(PPP2017_var$LOCATION, by = "LOCATION") |>
group_by(LOCATION, Location) |>
summarise(Nobs = n()) |>
arrange(-Nobs) |>
mutate(Flag = gsub(" ", "-", str_to_lower(gsub(" ", "-", Location))),
Flag = paste0('<img src="../../icon/flag/vsmall/', Flag, '.png" alt="Flag">')) |>
select(Flag, everything()) %>%
{if (is_html_output()) datatable(., filter = 'top', rownames = F, escape = F) else .}PPP |>
filter(TAB == "TAB1_11",
LOCATION %in% c("FRA", "ITA", "USA"),
obsTime == "2017") |>
left_join(PPP2017_var$CAT, by = "CAT") |>
left_join(PPP2017_var$LOCATION, by = "LOCATION") |>
select(-LOCATION) %>%
select_if(~ n_distinct(.) > 1) |>
spread(Location, obsValue) |>
arrange(-`France`) |>
print_table_conditional()PPP |>
filter(TAB == "TAB1_11",
LOCATION %in% c("FRA", "ITA", "USA", "DEU", "EA19"),
CAT == "A0104") |>
left_join(PPP2017_var$LOCATION, by = "LOCATION") |>
mutate(Location = ifelse(LOCATION == "EA19", "Europe", Location)) |>
year_to_date() |>
left_join(colors, by = c("Location" = "country")) |>
ggplot() + theme_minimal() + scale_color_identity() +
geom_line(aes(x = date, y = obsValue, color = color)) +
xlab("") + ylab("A0104, Price level indices (OECD = 100)") + add_4flags +
scale_x_date(breaks = seq(2005, 2100,3) |> paste0("-01-01") |> as.Date(),
labels = date_format("%Y")) +
scale_y_log10(breaks = seq(0, 200, 10))
PPP |>
filter(TAB == "TAB1_11",
LOCATION %in% c("FRA", "USA"),
obsTime %in% c("2008", "2017")) |>
spread(obsTime, obsValue) |>
transmute(CAT, LOCATION, obsValue = `2017`/`2008`) |>
spread(LOCATION, obsValue) |>
mutate(Difference = USA-FRA) |>
arrange(-Difference) |>
left_join(PPP2017_var$CAT, by = "CAT") |>
select(CAT, Cat, everything()) |>
print_table_conditional()