Cyclically adjusted balance (% of potential GDP) - GGCB_G01_PGDP_PT

Data - IMF

Author

François Geerolf

Info

Last observation: A: 2030 (N = 98)

First observation: A: 1991 (N = 23)

Last data update: 01 aoû 2026, 21:40. Last compile: 17 aoû 2026, 23:06

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 3330

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_flags +
  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))