Interest payments (% of revenue)
Data - WDI
François Geerolf
Info
LAST_DOWNLOAD
| source | dataset | Title | Download | Compile |
|---|---|---|---|---|
| wdi | GC.XPN.INTP.RV.ZS | Interest payments (% of revenue) | 2020-05-09 | 2023-03-30 |
| eurostat | ei_mfir_m | Interest rates - monthly data | 2023-06-05 | 2023-06-05 |
| eurostat | gov_10q_ggdebt | Quarterly government debt | 2023-06-03 | 2023-06-01 |
| fred | r | Interest Rates | 2023-06-05 | 2023-06-05 |
| gfd | debt | Debt | 2021-03-01 | 2021-08-22 |
| imf | FM | Fiscal Monitor | 2020-03-13 | 2023-03-29 |
| imf | GGXCNL_G01_GDP_PT | Net lending/borrowing (also referred as overall balance) (% of GDP) | 2023-06-03 | 2023-06-01 |
| imf | GGXONLB_G01_GDP_PT | Primary net lending/borrowing (also referred as primary balance) (% of GDP) | 2023-06-03 | 2023-06-03 |
| imf | GGXWDN_G01_GDP_PT | Net debt (% of GDP) | 2021-04-01 | 2023-04-01 |
| imf | HPDD | Historical Public Debt Database | NA | 2023-03-24 |
| wdi | GC.DOD.TOTL.GD.ZS | Central government debt, total (% of GDP) | 2023-03-29 | 2023-06-18 |
| wdi | GC.XPN.INTP.CN | Interest payments (current LCU) | 2023-03-30 | 2023-06-18 |
| wdi | GC.XPN.INTP.ZS | Interest payments (% of expense) | 2020-05-09 | 2023-03-30 |
LAST_COMPILE
| LAST_COMPILE |
|---|
| 2023-06-18 |
Nobs - Javascript
GC.XPN.INTP.RV.ZS %>%
left_join(iso2c, by = "iso2c") %>%
group_by(iso2c, Iso2c) %>%
rename(value = `GC.XPN.INTP.RV.ZS`) %>%
mutate(value = round(value, 2)) %>%
summarise(Nobs = n(),
`Year 1` = first(year),
`HH Consumption 1 (%)` = first(value),
`Year 2` = last(year),
`HH Consumption 2 (%)` = last(value)) %>%
arrange(-Nobs) %>%
{if (is_html_output()) datatable(., filter = 'top', rownames = F) else .}France, Germany, Italy
GC.XPN.INTP.RV.ZS %>%
filter(iso2c %in% c("IT", "FR", "DE")) %>%
left_join(iso2c, by = "iso2c") %>%
year_to_enddate %>%
ggplot(.) + theme_minimal() + geom_line() +
aes(x = date, y = GC.XPN.INTP.RV.ZS/100, color = Iso2c, linetype = Iso2c) +
xlab("") + ylab("Expense (% of GDP)") +
scale_color_manual(values = viridis(4)[1:3]) +
theme(legend.title = element_blank(),
legend.position = c(0.1, 0.9)) +
scale_x_date(breaks = seq(1950, 2020, 5) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%y")) +
scale_y_continuous(breaks = 0.01*seq(-60, 100, 2),
labels = scales::percent_format(accuracy = 1))
Japan, Switzerland
GC.XPN.INTP.RV.ZS %>%
filter(iso2c %in% c("JP", "CH")) %>%
left_join(iso2c, by = "iso2c") %>%
year_to_enddate %>%
ggplot(.) + theme_minimal() + geom_line() +
aes(x = date, y = GC.XPN.INTP.RV.ZS/100, color = Iso2c, linetype = Iso2c) +
xlab("") + ylab("Expense (% of GDP)") +
scale_color_manual(values = viridis(4)[1:3]) +
theme(legend.title = element_blank(),
legend.position = c(0.8, 0.9)) +
scale_x_date(breaks = seq(1950, 2020, 5) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%y")) +
scale_y_continuous(breaks = 0.01*seq(-60, 60, 2),
labels = scales::percent_format(accuracy = 1))
China, United States
GC.XPN.INTP.RV.ZS %>%
filter(iso2c %in% c("CN", "US")) %>%
left_join(iso2c, by = "iso2c") %>%
year_to_enddate %>%
ggplot(.) + theme_minimal() + geom_line() +
aes(x = date, y = GC.XPN.INTP.RV.ZS/100, color = Iso2c, linetype = Iso2c) +
xlab("") + ylab("Expense (% of GDP)") +
scale_color_manual(values = viridis(4)[1:3]) +
theme(legend.title = element_blank(),
legend.position = c(0.2, 0.9)) +
scale_x_date(breaks = seq(1950, 2020, 5) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%y")) +
scale_y_continuous(breaks = 0.01*seq(-60, 100, 2),
labels = scales::percent_format(accuracy = 1))
China, France, Germany
GC.XPN.INTP.RV.ZS %>%
filter(iso2c %in% c("CN", "FR", "DE")) %>%
left_join(iso2c, by = "iso2c") %>%
year_to_enddate %>%
ggplot(.) + theme_minimal() + geom_line() +
aes(x = date, y = GC.XPN.INTP.RV.ZS/100, color = Iso2c, linetype = Iso2c) +
xlab("") + ylab("Expense (% of GDP)") +
scale_color_manual(values = viridis(4)[1:3]) +
theme(legend.title = element_blank(),
legend.position = c(0.15, 0.9)) +
scale_x_date(breaks = seq(1950, 2020, 5) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%y")) +
scale_y_continuous(breaks = 0.01*seq(-60, 100, 2),
labels = scales::percent_format(accuracy = 1))
Spain, United Kingdom, United States
GC.XPN.INTP.RV.ZS %>%
filter(iso2c %in% c("US", "GB", "ES")) %>%
left_join(iso2c, by = "iso2c") %>%
year_to_enddate %>%
ggplot(.) + theme_minimal() + geom_line() +
aes(x = date, y = GC.XPN.INTP.RV.ZS/100, color = Iso2c, linetype = Iso2c) +
xlab("") + ylab("Expense (% of GDP)") +
scale_color_manual(values = viridis(4)[1:3]) +
theme(legend.title = element_blank(),
legend.position = c(0.4, 0.9)) +
scale_x_date(breaks = seq(1950, 2020, 5) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%y")) +
scale_y_continuous(breaks = 0.01*seq(-60, 100, 2),
labels = scales::percent_format(accuracy = 1))
Argentina, Chile, Venezuela
GC.XPN.INTP.RV.ZS %>%
filter(iso2c %in% c("AR", "CL", "VE")) %>%
left_join(iso2c, by = "iso2c") %>%
year_to_enddate %>%
ggplot(.) + theme_minimal() + geom_line() +
aes(x = date, y = GC.XPN.INTP.RV.ZS/100, color = Iso2c, linetype = Iso2c) +
xlab("") + ylab("Expense (% of GDP)") +
scale_color_manual(values = viridis(4)[1:3]) +
theme(legend.title = element_blank(),
legend.position = c(0.8, 0.9)) +
scale_x_date(breaks = seq(1950, 2020, 5) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%y")) +
scale_y_continuous(breaks = 0.01*seq(-60, 100, 2),
labels = scales::percent_format(accuracy = 1))
Greece, Hong Kong, Mexico
GC.XPN.INTP.RV.ZS %>%
filter(iso2c %in% c("GR", "HK", "MX")) %>%
left_join(iso2c, by = "iso2c") %>%
year_to_enddate %>%
ggplot(.) + theme_minimal() + geom_line() +
aes(x = date, y = GC.XPN.INTP.RV.ZS/100, color = Iso2c, linetype = Iso2c) +
xlab("") + ylab("Expense (% of GDP)") +
scale_color_manual(values = viridis(4)[1:3]) +
theme(legend.title = element_blank(),
legend.position = c(0.8, 0.9)) +
scale_x_date(breaks = seq(1950, 2020, 5) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%y")) +
scale_y_continuous(breaks = 0.01*seq(-60, 100, 2),
labels = scales::percent_format(accuracy = 1))