Interest payments (current LCU)
Data - WDI
François Geerolf
Info
LAST_DOWNLOAD
| source | dataset | Title | Download | Compile |
|---|---|---|---|---|
| wdi | GC.XPN.INTP.CN | Interest payments (current LCU) | 2023-03-30 | 2023-04-30 |
| 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.RV.ZS | Interest payments (% of revenue) | 2020-05-09 | 2023-03-30 |
| 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.CN %>%
left_join(NY.GDP.MKTP.CN, by = c("iso2c", "year")) %>%
left_join(iso2c, by = "iso2c") %>%
mutate(value = `GC.XPN.INTP.CN`/`NY.GDP.MKTP.CN`,
value = round(100*value, 2)) %>%
group_by(iso2c, Iso2c) %>%
summarise(Nobs = n(),
`Year 1` = first(year),
`Interest 1` = first(value),
`Year 2` = last(year),
`Interest 2` = last(value)) %>%
arrange(-Nobs) %>%
{if (is_html_output()) datatable(., filter = 'top', rownames = F) else .}Ex: Interest Payments
GC.XPN.INTP.CN %>%
left_join(NY.GDP.MKTP.CN, by = c("iso2c", "year")) %>%
filter(year == 2017) %>%
left_join(iso2c, by = "iso2c") %>%
mutate(interest_payments = round(100*`GC.XPN.INTP.CN`/`NY.GDP.MKTP.CN`, 2)) %>%
arrange(-interest_payments) %>%
mutate(interest_payments = interest_payments %>% round(digits = 2) %>% paste0(" %")) %>%
select(iso2c, Iso2c, interest_payments) %>%
{if (is_html_output()) datatable(., filter = 'top', rownames = F) else .}Argentina, Portugal, Spain
GC.XPN.INTP.CN %>%
left_join(NY.GDP.MKTP.CN, by = c("iso2c", "year")) %>%
mutate(value = `GC.XPN.INTP.CN`/`NY.GDP.MKTP.CN`) %>%
filter(iso2c %in% c("PT", "IT", "ES")) %>%
left_join(iso2c, by = "iso2c") %>%
year_to_date() %>%
group_by(Iso2c) %>%
complete(date = seq.Date(min(date), max(date), by = "year")) %>%
left_join(colors, by = c("Iso2c" = "country")) %>%
mutate(color = ifelse(iso2c == "PT", color2, color)) %>%
na.omit %>%
ggplot(.) + geom_line(aes(x = date, y = value, color = color)) +
theme_minimal() + add_3flags + scale_color_identity() +
xlab("") + ylab("Interest Payments (% of GDP)") +
theme(legend.title = element_blank(),
legend.position = c(0.85, 0.85)) +
scale_x_date(breaks = seq(1900, 2020, 5) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
scale_y_continuous(breaks = 0.01*seq(-100, 10000, 1),
labels = percent_format(a = 1)) +
geom_hline(yintercept = 0, linetype = "dashed", color = "black")
Italy, Portugal, Spain
GC.XPN.INTP.CN %>%
left_join(NY.GDP.MKTP.CN, by = c("iso2c", "year")) %>%
mutate(value = `GC.XPN.INTP.CN`/`NY.GDP.MKTP.CN`) %>%
filter(iso2c %in% c("PT", "IT", "ES")) %>%
left_join(iso2c, by = "iso2c") %>%
year_to_date() %>%
group_by(Iso2c) %>%
complete(date = seq.Date(min(date), max(date), by = "year")) %>%
left_join(colors, by = c("Iso2c" = "country")) %>%
mutate(color = ifelse(iso2c == "PT", color2, color)) %>%
ggplot(.) + geom_line(aes(x = date, y = value, color = color)) +
theme_minimal() + add_3flags + scale_color_identity() +
xlab("") + ylab("Interest Payments (% of GDP)") +
theme(legend.title = element_blank(),
legend.position = c(0.85, 0.85)) +
scale_x_date(breaks = seq(1900, 2020, 5) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
scale_y_continuous(breaks = 0.01*seq(-100, 10000, 1),
labels = percent_format(a = 1))
Greece, Iceland, Denmark
GC.XPN.INTP.CN %>%
left_join(NY.GDP.MKTP.CN, by = c("iso2c", "year")) %>%
mutate(value = `GC.XPN.INTP.CN`/`NY.GDP.MKTP.CN`) %>%
filter(iso2c %in% c("GR", "IS", "DK")) %>%
left_join(iso2c, by = "iso2c") %>%
year_to_date() %>%
#filter(date >= min(date), !is.na(GC.XPN.INTP.CN), !is.na(NY.GDP.MKTP.CN)) %>%
group_by(Iso2c) %>%
complete(date = seq.Date(min(date), max(date), by = "year")) %>%
left_join(colors, by = c("Iso2c" = "country")) %>%
mutate(color = ifelse(iso2c == "PT", color2, color)) %>%
ggplot(.) + geom_line(aes(x = date, y = value, color = color)) +
theme_minimal() + add_3flags + scale_color_identity() +
xlab("") + ylab("Interest Payments (% of GDP)") +
theme(legend.title = element_blank(),
legend.position = c(0.15, 0.85)) +
add_3flags +
scale_x_date(breaks = seq(1900, 2020, 5) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
scale_y_continuous(breaks = 0.01*seq(-100, 10000, 1),
labels = percent_format(a = 1)) +
geom_hline(yintercept = 0, linetype = "dashed", color = "grey")
France, Italy, Germany
GC.XPN.INTP.CN %>%
left_join(NY.GDP.MKTP.CN, by = c("iso2c", "year")) %>%
mutate(value = `GC.XPN.INTP.CN`/`NY.GDP.MKTP.CN`) %>%
filter(iso2c %in% c("FR", "CH", "IT")) %>%
left_join(iso2c, by = "iso2c") %>%
year_to_date() %>%
group_by(Iso2c) %>%
complete(date = seq.Date(min(date), max(date), by = "year")) %>%
left_join(colors, by = c("Iso2c" = "country")) %>%
mutate(color = ifelse(iso2c == "FR", color2, color)) %>%
na.omit %>%
ggplot(.) + geom_line(aes(x = date, y = value, color = color)) +
theme_minimal() + add_3flags + scale_color_identity() +
xlab("") + ylab("Interest Payments (% of GDP)") +
add_3flags +
theme(legend.title = element_blank(),
legend.position = c(0.85, 0.85)) +
scale_x_date(breaks = seq(1900, 2020, 5) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
scale_y_continuous(breaks = 0.01*seq(-100, 10000, 1),
labels = percent_format(a = 1))
United States, United Kingdom, Japan
GC.XPN.INTP.CN %>%
left_join(NY.GDP.MKTP.CN, by = c("iso2c", "year")) %>%
mutate(value = `GC.XPN.INTP.CN`/`NY.GDP.MKTP.CN`) %>%
filter(iso2c %in% c("JP", "US", "GB")) %>%
left_join(iso2c, by = "iso2c") %>%
year_to_date() %>%
group_by(Iso2c) %>%
complete(date = seq.Date(min(date), max(date), by = "year")) %>%
left_join(colors, by = c("Iso2c" = "country")) %>%
mutate(color = ifelse(iso2c == "FR", color2, color)) %>%
na.omit %>%
ggplot(.) + geom_line(aes(x = date, y = value, color = color)) +
theme_minimal() + add_3flags + scale_color_identity() +
xlab("") + ylab("Interest Payments (% of GDP)") +
theme(legend.title = element_blank(),
legend.position = c(0.8, 0.2)) +
scale_x_date(breaks = seq(1900, 2020, 5) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
scale_y_continuous(breaks = 0.01*seq(-100, 10000, 1),
labels = percent_format(a = 1)) +
geom_hline(yintercept = 0, linetype = "dashed", color = "grey")
Lebanon, Brazil, Jamaica
GC.XPN.INTP.CN %>%
left_join(NY.GDP.MKTP.CN, by = c("iso2c", "year")) %>%
mutate(value = `GC.XPN.INTP.CN`/`NY.GDP.MKTP.CN`) %>%
filter(iso2c %in% c("LB", "BR", "JM")) %>%
left_join(iso2c, by = "iso2c") %>%
year_to_date() %>%
group_by(Iso2c) %>%
complete(date = seq.Date(min(date), max(date), by = "year")) %>%
left_join(colors, by = c("Iso2c" = "country")) %>%
mutate(color = ifelse(iso2c == "FR", color2, color)) %>%
na.omit %>%
ggplot(.) + geom_line(aes(x = date, y = value, color = color)) +
theme_minimal() + add_3flags + scale_color_identity() +
xlab("") + ylab("Interest Payments (% of GDP)") +
theme(legend.title = element_blank(),
legend.position = c(0.85, 0.85)) +
scale_x_date(breaks = seq(1900, 2020, 5) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
scale_y_continuous(breaks = 0.01*seq(-100, 10000, 1),
labels = percent_format(a = 1)) +
geom_hline(yintercept = 0, linetype = "dashed", color = "grey")