Cyclically adjusted balance (% of potential GDP)

Data - IMF - FM

Author

François Geerolf

Info

source dataset .html .RData

imf

GGCB_G01_PGDP_PT

 2024-10-29 2024-10-29

imf

GGCBP_G01_PGDP_PT

 2024-10-29 2024-10-29

imf

GGXCNL_G01_GDP_PT

 2024-10-29 2024-10-29

imf

GGXONLB_G01_GDP_PT

 2024-10-29 2024-10-29

Data on public debt

Title source dataset .html .RData
Interest rates - monthly data

eurostat

ei_mfir_m

 2024-10-23 2024-10-08
Quarterly government debt

eurostat

gov_10q_ggdebt

 2024-10-23 2024-10-08
Interest Rates

fred

r

 2024-10-24 2024-10-24
Saving - saving

fred

saving

 2024-10-24 2024-10-24
Debt

gfd

debt

 2021-08-22 2021-03-01
Fiscal Monitor

imf

FM

 2024-06-20 2020-03-13
Net lending/borrowing (also referred as overall balance) (% of GDP)

imf

GGXCNL_G01_GDP_PT

 2024-10-29 2024-10-29
Primary net lending/borrowing (also referred as primary balance) (% of GDP)

imf

GGXONLB_G01_GDP_PT

 2024-10-29 2024-10-29
Net debt (% of GDP)

imf

GGXWDN_G01_GDP_PT

 2024-10-29 2024-05-06
Historical Public Debt Database

imf

HPDD

 2024-06-20 NA
Quarterly Sector Accounts - Public Sector Debt, consolidated, nominal value

oecd

QASA_TABLE7PSD

 2024-09-15 2024-04-15
Central government debt, total (% of GDP)

wdi

GC.DOD.TOTL.GD.ZS

 2023-06-18 2024-09-18
Interest payments (current LCU)

wdi

GC.XPN.INTP.CN

 2023-06-18 2024-09-18
Interest payments (% of revenue)

wdi

GC.XPN.INTP.RV.ZS

 2023-06-18 2024-09-18
Interest payments (% of expense)

wdi

GC.XPN.INTP.ZS

 2024-09-18 2024-09-18

LAST_COMPILE

LAST_COMPILE
2024-10-29

Last

TIME_PERIOD FREQ Nobs
2029 A 98

FREQ

Code 
GGCB_G01_PGDP_PT %>%
  left_join(FREQ, by =  "FREQ") %>%
  group_by(FREQ, Freq) %>%
  summarise(Nobs = n()) %>%
  arrange(-Nobs) %>%
  print_table_conditional()
FREQ Freq Nobs
A Annual 3239

REF_AREA

Code 
GGCB_G01_PGDP_PT %>%
  left_join(REF_AREA, by = "REF_AREA") %>%
  group_by(REF_AREA, Ref_area) %>%
  summarise(Nobs = n()) %>%
  arrange(-Nobs) %>%
  mutate(Flag = gsub(" ", "-", str_to_lower(gsub(" ", "-", Ref_area))),
         Flag = paste0('<img src="../../icon/flag/round/vsmall/', Flag, '.png" alt="Flag">')) %>%
  select(Flag, everything()) %>%
  {if (is_html_output()) datatable(., filter = 'top', rownames = F, escape = F) else .}

TIME_PERIOD

Code 
GGCB_G01_PGDP_PT %>%
  group_by(TIME_PERIOD) %>%
  summarise(Nobs = n()) %>%
  arrange(desc(TIME_PERIOD)) %>%
  print_table_conditional()

Table

Code 
GGCB_G01_PGDP_PT %>%
  filter(INDICATOR == "GGCB_G01_PGDP_PT",
         TIME_PERIOD == "2018") %>%
  left_join(REF_AREA, by = "REF_AREA") %>%
  select(REF_AREA, Ref_area, OBS_VALUE) %>%
  arrange(-OBS_VALUE) %>%
  na.omit %>%
  mutate_at(vars(3), funs(paste0(round(as.numeric(.), 1), " %"))) %>%
  mutate(Flag = gsub(" ", "-", str_to_lower(gsub(" ", "-", Ref_area))),
         Flag = paste0('<img src="../../icon/flag/round/vsmall/', Flag, '.png" alt="Flag">')) %>%
  select(Flag, everything()) %>%
  {if (is_html_output()) datatable(., filter = 'top', rownames = F, escape = F) else .}

Italy, France, Germany, United States

Code 
GGCB_G01_PGDP_PT %>%
  filter(INDICATOR == "GGCB_G01_PGDP_PT",
         REF_AREA %in% c("IT", "FR", "DE", "US")) %>%
  left_join(REF_AREA, by = "REF_AREA") %>%
  left_join(colors, by = c("Ref_area" = "country")) %>%
  year_to_date2 %>%
  mutate(OBS_VALUE = OBS_VALUE/100) %>%
  rename(Counterpart_area = Ref_area) %>%
  ggplot() + 
  geom_line(aes(x = date, y = OBS_VALUE, color = color)) +
  scale_color_identity() + theme_minimal() + add_4flags +
  scale_x_date(breaks = seq(1920, 2100, 5) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  scale_y_continuous(breaks = 0.01*seq(-60, 60, 1),
                     labels = scales::percent_format(accuracy = 1)) +
  ylab("Cyclically adj. balance (% of potential GDP)") + xlab("")

Individual Countries

Italy

Code 
GGCB_G01_PGDP_PT %>%
  filter(INDICATOR == "GGCB_G01_PGDP_PT",
         REF_AREA == "IT") %>%
  select(INDICATOR, TIME_PERIOD, OBS_VALUE) %>%
  mutate_at(vars(3), funs(paste0(round(as.numeric(.), 1), " %"))) %>%
  {if (is_html_output()) datatable(., filter = 'top', rownames = F) else .}

Netherlands

Code 
GGCB_G01_PGDP_PT %>%
  filter(INDICATOR == "GGCB_G01_PGDP_PT",
         REF_AREA == "NL") %>%
  select(INDICATOR, TIME_PERIOD, OBS_VALUE) %>%
  mutate_at(vars(3), funs(paste0(round(as.numeric(.), 1), " %"))) %>%
  {if (is_html_output()) datatable(., filter = 'top', rownames = F) else .}

Average Primary Surpluses

6 Countries

Code 
GGCB_G01_PGDP_PT %>%
  filter(INDICATOR == "GGCB_G01_PGDP_PT",
         REF_AREA %in% c("NL", "IT", "DE", "FR", "ES", "GR")) %>%
  year_to_date2 %>%
  filter(date >= as.Date("1995-01-01")) %>%
  left_join(REF_AREA, by = "REF_AREA") %>%
  group_by(Ref_area) %>%
  summarise(`Primary Surplus (1995-2020)` = mean(OBS_VALUE)) %>%
  mutate_at(vars(2), funs(paste0(round(as.numeric(.), 3), " %"))) %>%
  {if (is_html_output()) datatable(., filter = 'top', rownames = F) else .}

All Countries

Code 
GGCB_G01_PGDP_PT %>%
  year_to_date2 %>%
  filter(date >= as.Date("1995-01-01"),
         date <= as.Date("2019-01-01")) %>%
  left_join(REF_AREA, by = "REF_AREA") %>%
  group_by(REF_AREA, Ref_area) %>%
  summarise(`Average Primary Surplus (1995-2019)` = mean(OBS_VALUE)) %>%
  arrange(-`Average Primary Surplus (1995-2019)`) %>%
  mutate_at(vars(3), funs(paste0(round(as.numeric(.), 3), " %"))) %>%
  mutate(Flag = gsub(" ", "-", str_to_lower(gsub(" ", "-", Ref_area))),
         Flag = paste0('<img src="../../icon/flag/round/vsmall/', Flag, '.png" alt="Flag">')) %>%
  select(Flag, everything()) %>%
  {if (is_html_output()) datatable(., filter = 'top', rownames = F, escape = F) else .}

Code

Code 
i_g("bib/imf/FM-data.jpeg")

Average Primary Surpluses

1995-2019

Code 
GGCB_G01_PGDP_PT %>%
  year_to_date2 %>%
  filter(date >= as.Date("1995-01-01"),
         date <= as.Date("2019-01-01")) %>%
  left_join(REF_AREA, by = "REF_AREA") %>%
  group_by(REF_AREA, Ref_area) %>%
  summarise(`Average Primary Surplus (1995-2019)` = mean(OBS_VALUE)) %>%
  arrange(-`Average Primary Surplus (1995-2019)`) %>%
  mutate_at(vars(3), funs(paste0(round(as.numeric(.), 3), " %"))) %>%
  mutate(Flag = gsub(" ", "-", str_to_lower(gsub(" ", "-", Ref_area))),
         Flag = paste0('<img src="../../icon/flag/round/vsmall/', Flag, '.png" alt="Flag">')) %>%
  select(Flag, everything()) %>%
  {if (is_html_output()) datatable(., filter = 'top', rownames = F, escape = F) else .}

Average Primary Surpluses (1995-2019) - Underlying Data

1992-

Code 
GGCB_G01_PGDP_PT %>%
  year_to_date2() %>%
  filter(date >= as.Date("1992-01-01")) %>%
  left_join(REF_AREA, by = "REF_AREA") %>%
  group_by(REF_AREA, Ref_area) %>%
  summarise(`Primary Surplus (1992-2020)` = mean(OBS_VALUE)) %>%
  arrange(-`Primary Surplus (1992-2020)`) %>%
  mutate_at(vars(3), funs(paste0(round(as.numeric(.), 3), " %"))) %>%
  mutate(Flag = gsub(" ", "-", str_to_lower(gsub(" ", "-", Ref_area))),
         Flag = paste0('<img src="../../icon/flag/round/vsmall/', Flag, '.png" alt="Flag">')) %>%
  select(Flag, everything()) %>%
  {if (is_html_output()) datatable(., filter = 'top', rownames = F, escape = F) else .}

1992-

Code 
GGCB_G01_PGDP_PT %>%
  year_to_date2 %>%
  filter(date >= as.Date("1992-01-01")) %>%
  group_by(iso2c = REF_AREA) %>%
  summarise(OBS_VALUE = mean(OBS_VALUE)) %>%
  right_join(world, by = "iso2c") %>%
  ggplot() + theme_void() +
  geom_polygon(aes(long, lat, group = group, fill = OBS_VALUE/100), 
               colour = alpha("black", 1/2), size = 0.1)  +
  scale_fill_viridis_c(name = "Primary Surplus (%)",
                       labels = scales::percent_format(accuracy = 1),
                       breaks = seq(-0.10, 0.2, 0.01),
                       values = c(0, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 1)) +
  theme(legend.position = c(0.1, 0.4),
        legend.title = element_text(size = 10))

1995-2019

Code 
GGCB_G01_PGDP_PT %>%
  year_to_date2 %>%
  filter(date >= as.Date("1995-01-01"),
         date <= as.Date("2019-01-01")) %>%
  group_by(iso2c = REF_AREA) %>%
  summarise(OBS_VALUE = mean(OBS_VALUE)) %>%
  right_join(world, by = "iso2c") %>%
  ggplot() + theme_void() +
  geom_polygon(aes(long, lat, group = group, fill = OBS_VALUE/100), 
               colour = alpha("black", 1/2), size = 0.1)  +
  scale_fill_viridis_c(name = "Primary Surplus",
                       labels = scales::percent_format(accuracy = 1),
                       breaks = seq(-0.10, 0.2, 0.01),
                       values = c(0, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 1)) +
  theme(legend.position = c(0.12, 0.4),
        legend.title = element_text(size = 10))