Fiscal Monitor

Data - IMF

Author

Info

source	dataset	.html	.RData
imf	GGXCNL_G01_GDP_PT	2024-06-20	2024-05-06
imf	GGXONLB_G01_GDP_PT	2024-06-20	2024-05-06

Data on public debt

source	dataset	.html	.RData
eurostat	ei_mfir_m	2024-06-20	2024-06-08
eurostat	gov_10q_ggdebt	2024-06-20	2024-06-08
fred	r	2024-06-20	2024-06-07
fred	saving	2024-06-20	2024-06-07
gfd	debt	2021-08-22	2021-03-01
imf	FM	2024-06-20	2020-03-13
imf	GGXCNL_G01_GDP_PT	2024-06-20	2024-05-06
imf	GGXONLB_G01_GDP_PT	2024-06-20	2024-05-06
imf	GGXWDN_G01_GDP_PT	2024-06-20	2024-05-06
imf	HPDD	2024-06-20	NA
oecd	QASA_TABLE7PSD	2024-06-19	2024-04-15
wdi	GC.DOD.TOTL.GD.ZS	2023-06-18	2024-04-14
wdi	GC.XPN.INTP.CN	2023-06-18	2024-04-14
wdi	GC.XPN.INTP.RV.ZS	2023-06-18	2024-04-14
wdi	GC.XPN.INTP.ZS	2024-06-20	2024-04-14

LAST_COMPILE

LAST_COMPILE
2024-06-20

Last

Code

FM %>%
  group_by(date) %>%
  summarise(Nobs = n()) %>%
  arrange(desc(date)) %>%
  head(1) %>%
  print_table_conditional()

date	Nobs
2024-01-01	880

Main Datasets

Javascript

Code

INDICATOR %>%
  filter(INDICATOR %in% gsub(".qmd$", "", list.files(pattern = "\\.qmd$"))) %>%
  mutate(html = paste0('<a  target=_blank href=', INDICATOR, '.html > html </a>')) %>%
  {if (is_html_output()) datatable(., filter = 'top', rownames = F, escape = F) else .}

Flat

Code

INDICATOR %>%
  filter(INDICATOR %in% gsub(".qmd$", "", list.files(pattern = "\\.qmd$"))) %>%
  mutate(html = paste0("[html](", INDICATOR, '.html)')) %>%
  {if (is_html_output()) print_table(.) else .}

INDICATOR	Indicator	html
GGCB_G01_PGDP_PT	Cyclically adjusted balance (% of potential GDP)	[html]
GGCBP_G01_PGDP_PT	Cyclically adjusted primary balance (% of potential GDP)	[html]
G_XWDG_G01_GDP_PT	Gross debt (% of GDP)	[html]
GGXWDN_G01_GDP_PT	Net debt (% of GDP)	[html]
GGXCNL_G01_GDP_PT	Net lending/borrowing (also referred as overall balance) (% of GDP)	[html]
GGXONLB_G01_GDP_PT	Primary net lending/borrowing (also referred as primary balance) (% of GDP)	[html]

Other Datasets

Code

INDICATOR %>%
  filter(!(INDICATOR %in% gsub(".qmd$", "", list.files(pattern = "\\.qmd$")))) %>%
  {if (is_html_output()) datatable(., filter = 'top', rownames = F) else .}

Interest payment

Italy, France, Germany

Code

GGXCNL_G01_GDP_PT %>%
  bind_rows(GGXONLB_G01_GDP_PT) %>%
  filter(REF_AREA %in% c("IT", "FR", "DE")) %>%
  left_join(REF_AREA, by = "REF_AREA") %>%
  year_to_date2() %>%
  spread(INDICATOR, OBS_VALUE) %>%
  transmute(date, Ref_area, OBS_VALUE = GGXCNL_G01_GDP_PT - GGXONLB_G01_GDP_PT) %>%
  left_join(colors, by = c("Ref_area" = "country")) %>%
  mutate(OBS_VALUE = OBS_VALUE/100) %>%
  rename(Counterpart_area = Ref_area) %>%
  #filter(date <= as.Date("2021-01-01")) %>%
  ggplot(.) + geom_line(aes(x = date, y = OBS_VALUE, color = color)) + 
  theme_minimal() + xlab("") + ylab("Interest payments, % of GDP") +
  scale_color_identity() + add_3flags +
  scale_x_date(breaks = seq(1920, 2100, 5) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  theme(legend.position = c(0.63, 0.9),
        legend.title = element_blank()) +
  scale_y_continuous(breaks = 0.01*seq(-60, 60, 1),
                     labels = scales::percent_format(accuracy = 1)) +
  geom_hline(yintercept = 0, linetype = "dashed", color = "black")

Portugal, Spain, Greece

Code

GGXCNL_G01_GDP_PT %>%
  bind_rows(GGXONLB_G01_GDP_PT) %>%
  filter(REF_AREA %in% c("PT", "ES", "GR")) %>%
  left_join(REF_AREA, by = "REF_AREA") %>%
  year_to_date2() %>%
  spread(INDICATOR, OBS_VALUE) %>%
  transmute(date, Ref_area, OBS_VALUE = GGXCNL_G01_GDP_PT - GGXONLB_G01_GDP_PT) %>%
  left_join(colors, by = c("Ref_area" = "country")) %>%
  mutate(OBS_VALUE = OBS_VALUE/100) %>%
  rename(Counterpart_area = Ref_area) %>%
  #filter(date <= as.Date("2021-01-01")) %>%
  ggplot(.) + geom_line(aes(x = date, y = OBS_VALUE, color = color)) + 
  theme_minimal() + xlab("") + ylab("Interest payments, % of GDP") +
  scale_color_identity() + add_3flags +
  scale_x_date(breaks = seq(1920, 2100, 5) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  theme(legend.position = c(0.63, 0.9),
        legend.title = element_blank()) +
  scale_y_continuous(breaks = 0.01*seq(-60, 60, 1),
                     labels = scales::percent_format(accuracy = 1)) +
  geom_hline(yintercept = 0, linetype = "dashed", color = "black")

Primary / Total lending borrowing

Italy, France, Germany

Same

Code

GGXCNL_G01_GDP_PT %>%
  bind_rows(GGXONLB_G01_GDP_PT) %>%
  filter(REF_AREA %in% c("IT", "FR", "DE")) %>%
  left_join(REF_AREA, by = "REF_AREA") %>%
  year_to_date2() %>%
  left_join(colors, by = c("Ref_area" = "country")) %>%
  left_join(INDICATOR, by = "INDICATOR") %>%
  mutate(OBS_VALUE = OBS_VALUE/100) %>%
  rename(Counterpart_area = Ref_area) %>%
  #filter(date <= as.Date("2021-01-01")) %>%
  ggplot(.) + geom_line(aes(x = date, y = OBS_VALUE, color = color, linetype = Indicator)) + 
  theme_minimal() + xlab("") + ylab("Deficit, % of GDP") +
  scale_color_identity() + add_6flags +
  scale_x_date(breaks = seq(1920, 2100, 5) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  theme(legend.position = c(0.63, 0.9),
        legend.title = element_blank()) +
  scale_y_continuous(breaks = 0.01*seq(-60, 60, 1),
                     labels = scales::percent_format(accuracy = 1)) +
  geom_hline(yintercept = 0, linetype = "dashed", color = "black")

Facet Indicator

Without interest payments

Code

GGXCNL_G01_GDP_PT %>%
  bind_rows(GGXONLB_G01_GDP_PT) %>%
  filter(REF_AREA %in% c("IT", "FR", "DE")) %>%
  left_join(REF_AREA, by = "REF_AREA") %>%
  year_to_date2() %>%
  left_join(colors, by = c("Ref_area" = "country")) %>%
  left_join(INDICATOR, by = "INDICATOR") %>%
  mutate(OBS_VALUE = OBS_VALUE/100) %>%
  rename(Counterpart_area = Ref_area) %>%
  mutate(Indicator = gsub("\\(also", "\n \\(also", Indicator)) %>%
  ggplot(.) + geom_line(aes(x = date, y = OBS_VALUE, color = color)) + 
  theme_minimal() + xlab("") + ylab("Deficit, % of GDP") +
  scale_color_identity() + add_6flags +
  scale_x_date(breaks = seq(1920, 2100, 5) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  theme(legend.position = c(0.63, 0.9),
        legend.title = element_blank()) +
  scale_y_continuous(breaks = 0.01*seq(-60, 60, 1),
                     labels = scales::percent_format(accuracy = 1)) +
  geom_hline(yintercept = 0, linetype = "dashed", color = "black") +
  facet_wrap(~ Indicator)

With interest payments

Code

GGXCNL_G01_GDP_PT %>%
  bind_rows(GGXONLB_G01_GDP_PT) %>%
  filter(REF_AREA %in% c("IT", "FR", "DE")) %>%
  left_join(REF_AREA, by = "REF_AREA") %>%
  year_to_date2() %>%
  left_join(colors, by = c("Ref_area" = "country")) %>%
  spread(INDICATOR, OBS_VALUE) %>%
  mutate(GGXCIP_G01_GDP_PT = GGXCNL_G01_GDP_PT - GGXONLB_G01_GDP_PT) %>%
  gather(INDICATOR, OBS_VALUE, GGXCIP_G01_GDP_PT, GGXCIP_G01_GDP_PT, GGXCNL_G01_GDP_PT, GGXONLB_G01_GDP_PT) %>%
  left_join(tribble(~ INDICATOR, ~ Indicator,
                    "GGXCIP_G01_GDP_PT", "Interest payments (% of GDP)",
                    "GGXCNL_G01_GDP_PT", "Overall balance (% of GDP)",
                    "GGXONLB_G01_GDP_PT", "Primary balance (% of GDP)"), by = "INDICATOR") %>%
  mutate(Indicator = factor(Indicator, levels = c("Overall balance (% of GDP)", "Interest payments (% of GDP)", "Primary balance (% of GDP)"))) %>%
  mutate(OBS_VALUE = OBS_VALUE/100) %>%
  rename(Counterpart_area = Ref_area) %>%
  ggplot(.) + geom_line(aes(x = date, y = OBS_VALUE, color = color)) + 
  theme_minimal() + xlab("") + ylab("% of GDP") +
  scale_color_identity() + add_9flags +
  scale_x_date(breaks = seq(1920, 2100, 5) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  theme(legend.position = c(0.63, 0.9),
        legend.title = element_blank(),
        axis.text.x = element_text(angle = 45, vjust = 1, hjust = 1)) +
  scale_y_continuous(breaks = 0.01*seq(-60, 60, 1),
                     labels = scales::percent_format(accuracy = 1)) +
  geom_hline(yintercept = 0, linetype = "dashed", color = "black") +
  facet_wrap(~ Indicator)

Facet country

Without interest payments

Code

GGXCNL_G01_GDP_PT %>%
  bind_rows(GGXONLB_G01_GDP_PT) %>%
  filter(REF_AREA %in% c("IT", "FR", "DE")) %>%
  left_join(REF_AREA, by = "REF_AREA") %>%
  year_to_date2() %>%
  left_join(INDICATOR, by = "INDICATOR") %>%
  mutate(OBS_VALUE = OBS_VALUE/100) %>%
  ggplot(.) + geom_line(aes(x = date, y = OBS_VALUE, color = Indicator)) + 
  theme_minimal() + xlab("") + ylab("Deficit, % of GDP") +
  scale_x_date(breaks = seq(1920, 2100, 5) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  theme(legend.position = c(0.35, 0.9),
        legend.title = element_blank(),
        axis.text.x = element_text(angle = 45, vjust = 1, hjust = 1)) +
  scale_y_continuous(breaks = 0.01*seq(-60, 60, 1),
                     labels = scales::percent_format(accuracy = 1)) +
  geom_hline(yintercept = 0, linetype = "dashed", color = "black") +
  facet_wrap(~ Ref_area)

With interest payments

Code

GGXCNL_G01_GDP_PT %>%
  bind_rows(GGXONLB_G01_GDP_PT) %>%
  filter(REF_AREA %in% c("IT", "FR", "DE")) %>%
  left_join(REF_AREA, by = "REF_AREA") %>%
  year_to_date2() %>%
  spread(INDICATOR, OBS_VALUE) %>%
  mutate(GGXCIP_G01_GDP_PT = GGXCNL_G01_GDP_PT - GGXONLB_G01_GDP_PT) %>%
  gather(INDICATOR, OBS_VALUE, GGXCIP_G01_GDP_PT, GGXCIP_G01_GDP_PT, GGXCNL_G01_GDP_PT, GGXONLB_G01_GDP_PT) %>%
  left_join(tribble(~ INDICATOR, ~ Indicator,
                    "GGXCIP_G01_GDP_PT", "Interest payments (% of GDP)",
                    "GGXCNL_G01_GDP_PT", "Overall balance (% of GDP)",
                    "GGXONLB_G01_GDP_PT", "Primary balance (% of GDP)"), by = "INDICATOR") %>%
  mutate(Indicator = factor(Indicator, levels = c("Overall balance (% of GDP)", "Interest payments (% of GDP)", "Primary balance (% of GDP)"))) %>%
  mutate(OBS_VALUE = OBS_VALUE/100) %>%
  ggplot(.) + geom_line(aes(x = date, y = OBS_VALUE, color = Indicator)) + 
  theme_minimal() + xlab("") + ylab("Deficit, % of GDP") +
  scale_x_date(breaks = seq(1920, 2100, 5) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  theme(legend.position = c(0.35, 0.9),
        legend.title = element_blank(),
        axis.text.x = element_text(angle = 45, vjust = 1, hjust = 1)) +
  scale_y_continuous(breaks = 0.01*seq(-60, 60, 1),
                     labels = scales::percent_format(accuracy = 1)) +
  geom_hline(yintercept = 0, linetype = "dashed", color = "black") +
  facet_wrap(~ Ref_area)

Interest Payments, Primary / Total lending

9 Biggest

Original

All

Code

GGXCNL_G01_GDP_PT %>%
  bind_rows(GGXONLB_G01_GDP_PT) %>%
  filter(REF_AREA %in% c("DE","FR","IT",  "ES", "NL", "BE", "AT", "PT", "FI")) %>%
  left_join(REF_AREA, by = "REF_AREA") %>%
  year_to_date2() %>%
  spread(INDICATOR, OBS_VALUE) %>%
  mutate(GGXCIP_G01_GDP_PT = GGXCNL_G01_GDP_PT - GGXONLB_G01_GDP_PT) %>%
  gather(INDICATOR, OBS_VALUE, GGXCIP_G01_GDP_PT, GGXCIP_G01_GDP_PT, GGXCNL_G01_GDP_PT, GGXONLB_G01_GDP_PT) %>%
  left_join(tribble(~ INDICATOR, ~ Indicator,
                    "GGXCIP_G01_GDP_PT", "Interest payments (% of GDP)",
                    "GGXCNL_G01_GDP_PT", "Overall balance (% of GDP)",
                    "GGXONLB_G01_GDP_PT", "Primary balance (% of GDP)"), by = "INDICATOR") %>%
  mutate(Indicator = factor(Indicator, levels = c("Overall balance (% of GDP)", "Interest payments (% of GDP)", "Primary balance (% of GDP)"))) %>%
  mutate(OBS_VALUE = OBS_VALUE/100) %>%
  ggplot(.) + geom_line(aes(x = date, y = OBS_VALUE, color = Indicator)) + 
  theme_minimal() + xlab("") + ylab("Deficit, % of GDP") +
  scale_x_date(breaks = seq(1920, 2100, 5) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  theme(legend.position = "top",
        legend.title = element_blank(),
        axis.text.x = element_text(angle = 45, vjust = 1, hjust = 1)) +
  scale_y_continuous(breaks = 0.01*seq(-60, 60, 5),
                     labels = scales::percent_format(accuracy = 1)) +
  geom_hline(yintercept = 0, linetype = "dashed", color = "black") +
  facet_wrap(~ Ref_area)

2005-2015

Code

GGXCNL_G01_GDP_PT %>%
  bind_rows(GGXONLB_G01_GDP_PT) %>%
  filter(REF_AREA %in% c("DE","FR","IT",  "ES", "NL", "BE", "AT", "PT", "FI")) %>%
  left_join(REF_AREA, by = "REF_AREA") %>%
  year_to_date2() %>%
  filter(date >= as.Date("2007-01-01"),
         date <= as.Date("2014-01-01")) %>%
  spread(INDICATOR, OBS_VALUE) %>%
  mutate(GGXCIP_G01_GDP_PT = GGXCNL_G01_GDP_PT - GGXONLB_G01_GDP_PT) %>%
  gather(INDICATOR, OBS_VALUE, GGXCIP_G01_GDP_PT, GGXCIP_G01_GDP_PT, GGXCNL_G01_GDP_PT, GGXONLB_G01_GDP_PT) %>%
  left_join(tribble(~ INDICATOR, ~ Indicator,
                    "GGXCIP_G01_GDP_PT", "Interest payments (% of GDP)",
                    "GGXCNL_G01_GDP_PT", "Overall balance (% of GDP)",
                    "GGXONLB_G01_GDP_PT", "Primary balance (% of GDP)"), by = "INDICATOR") %>%
  mutate(Indicator = factor(Indicator, levels = c("Overall balance (% of GDP)", "Interest payments (% of GDP)", "Primary balance (% of GDP)"))) %>%
  mutate(OBS_VALUE = OBS_VALUE/100) %>%
  ggplot(.) + geom_line(aes(x = date, y = OBS_VALUE, color = Indicator)) + 
  theme_minimal() + xlab("") + ylab("Deficit, % of GDP") +
  scale_x_date(breaks = seq(1920, 2100, 1) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  theme(legend.position = "top",
        legend.title = element_blank(),
        axis.text.x = element_text(angle = 45, vjust = 1, hjust = 1)) +
  scale_y_continuous(breaks = 0.01*seq(-60, 60, 5),
                     labels = scales::percent_format(accuracy = 1)) +
  geom_hline(yintercept = 0, linetype = "dashed", color = "black") +
  facet_wrap(~ Ref_area)

Only interest payments

Code

GGXCNL_G01_GDP_PT %>%
  bind_rows(GGXONLB_G01_GDP_PT) %>%
  filter(REF_AREA %in% c("DE","FR","IT",  "ES", "NL", "BE", "AT", "PT", "FI")) %>%
  left_join(REF_AREA, by = "REF_AREA") %>%
  year_to_date2() %>%
  filter(date >= as.Date("2007-01-01"),
         date <= as.Date("2014-01-01")) %>%
  spread(INDICATOR, OBS_VALUE) %>%
  mutate(GGXCIP_G01_GDP_PT = GGXCNL_G01_GDP_PT - GGXONLB_G01_GDP_PT) %>%
  select(-GGXCNL_G01_GDP_PT, -GGXONLB_G01_GDP_PT) %>%
  gather(INDICATOR, OBS_VALUE, GGXCIP_G01_GDP_PT) %>%
  left_join(tribble(~ INDICATOR, ~ Indicator,
                    "GGXCIP_G01_GDP_PT", "Interest payments (% of GDP)",
                    "GGXCNL_G01_GDP_PT", "Overall balance (% of GDP)",
                    "GGXONLB_G01_GDP_PT", "Primary balance (% of GDP)"), by = "INDICATOR") %>%
  mutate(Indicator = factor(Indicator, levels = c("Overall balance (% of GDP)", "Interest payments (% of GDP)", "Primary balance (% of GDP)"))) %>%
  mutate(OBS_VALUE = OBS_VALUE/100) %>%
  ggplot(.) + geom_line(aes(x = date, y = OBS_VALUE, color = Indicator)) + 
  theme_minimal() + xlab("") + ylab("Deficit, % of GDP") +
  scale_x_date(breaks = seq(1920, 2100, 1) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  theme(legend.position = "top",
        legend.title = element_blank(),
        axis.text.x = element_text(angle = 45, vjust = 1, hjust = 1)) +
  scale_y_continuous(breaks = 0.01*seq(-60, 60, 5),
                     labels = scales::percent_format(accuracy = 1)) +
  geom_hline(yintercept = 0, linetype = "dashed", color = "black") +
  facet_wrap(~ Ref_area)

Cyclically Adjusted

Code

GGCBP_G01_PGDP_PT %>%
  bind_rows(GGCB_G01_PGDP_PT) %>%
  filter(REF_AREA %in% c("DE","FR","IT",  "ES", "NL", "BE", "AT", "PT", "FI")) %>%
  left_join(REF_AREA, by = "REF_AREA") %>%
  year_to_date2() %>%
  spread(INDICATOR, OBS_VALUE) %>%
  mutate(GGCBIP_G01_PGDP_PT = GGCB_G01_PGDP_PT - GGCBP_G01_PGDP_PT) %>%
  gather(INDICATOR, OBS_VALUE, GGCBIP_G01_PGDP_PT, GGCB_G01_PGDP_PT, GGCBP_G01_PGDP_PT) %>%
  left_join(tribble(~ INDICATOR, ~ Indicator,
                    "GGCBIP_G01_PGDP_PT", "Interest payments (% of CA GDP)",
                    "GGCB_G01_PGDP_PT", "CA Overall balance (% of CA GDP)",
                    "GGCBP_G01_PGDP_PT", "CA Primary balance (% of CA GDP)"), by = "INDICATOR") %>%
  mutate(Indicator = factor(Indicator, levels = c("CA Overall balance (% of CA GDP)", "Interest payments (% of CA GDP)", "CA Primary balance (% of CA GDP)"))) %>%
  mutate(OBS_VALUE = OBS_VALUE/100) %>%
  ggplot(.) + geom_line(aes(x = date, y = OBS_VALUE, color = Indicator)) + 
  theme_minimal() + xlab("") + ylab("Deficit, % of GDP") +
  scale_x_date(breaks = seq(1920, 2100, 5) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  theme(legend.position = "top",
        legend.title = element_blank(),
        axis.text.x = element_text(angle = 45, vjust = 1, hjust = 1)) +
  scale_y_continuous(breaks = 0.01*seq(-60, 60, 5),
                     labels = scales::percent_format(accuracy = 1)) +
  geom_hline(yintercept = 0, linetype = "dashed", color = "black") +
  facet_wrap(~ Ref_area)

9 smallest

Code

GGXCNL_G01_GDP_PT %>%
  bind_rows(GGXONLB_G01_GDP_PT) %>%
  filter(REF_AREA %in% c("SK","LT",  "SI", "LU", "LV", "EE", "CY", "MT")) %>%
  left_join(REF_AREA, by = "REF_AREA") %>%
  year_to_date2() %>%
  spread(INDICATOR, OBS_VALUE) %>%
  mutate(GGXCIP_G01_GDP_PT = GGXCNL_G01_GDP_PT - GGXONLB_G01_GDP_PT) %>%
  gather(INDICATOR, OBS_VALUE, GGXCIP_G01_GDP_PT, GGXCIP_G01_GDP_PT, GGXCNL_G01_GDP_PT, GGXONLB_G01_GDP_PT) %>%
  left_join(tribble(~ INDICATOR, ~ Indicator,
                    "GGXCIP_G01_GDP_PT", "Interest payments (% of GDP)",
                    "GGXCNL_G01_GDP_PT", "Overall balance (% of GDP)",
                    "GGXONLB_G01_GDP_PT", "Primary balance (% of GDP)"), by = "INDICATOR") %>%
  mutate(Indicator = factor(Indicator, levels = c("Overall balance (% of GDP)", "Interest payments (% of GDP)", "Primary balance (% of GDP)"))) %>%
  mutate(OBS_VALUE = OBS_VALUE/100) %>%
  ggplot(.) + geom_line(aes(x = date, y = OBS_VALUE, color = Indicator)) + 
  theme_minimal() + xlab("") + ylab("Deficit, % of GDP") +
  scale_x_date(breaks = seq(1920, 2100, 5) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  theme(legend.position = "top",
        legend.title = element_blank(),
        axis.text.x = element_text(angle = 45, vjust = 1, hjust = 1)) +
  scale_y_continuous(breaks = 0.01*seq(-60, 60, 5),
                     labels = scales::percent_format(accuracy = 1)) +
  geom_hline(yintercept = 0, linetype = "dashed", color = "black") +
  facet_wrap(~ Ref_area)

Large deficits: Ireland, Greece

Code

GGXCNL_G01_GDP_PT %>%
  bind_rows(GGXONLB_G01_GDP_PT) %>%
  filter(REF_AREA %in% c("IE","GR")) %>%
  left_join(REF_AREA, by = "REF_AREA") %>%
  year_to_date2() %>%
  spread(INDICATOR, OBS_VALUE) %>%
  mutate(GGXCIP_G01_GDP_PT = GGXCNL_G01_GDP_PT - GGXONLB_G01_GDP_PT) %>%
  gather(INDICATOR, OBS_VALUE, GGXCIP_G01_GDP_PT, GGXCIP_G01_GDP_PT, GGXCNL_G01_GDP_PT, GGXONLB_G01_GDP_PT) %>%
  left_join(tribble(~ INDICATOR, ~ Indicator,
                    "GGXCIP_G01_GDP_PT", "Interest payments (% of GDP)",
                    "GGXCNL_G01_GDP_PT", "Overall balance (% of GDP)",
                    "GGXONLB_G01_GDP_PT", "Primary balance (% of GDP)"), by = "INDICATOR") %>%
  mutate(Indicator = factor(Indicator, levels = c("Overall balance (% of GDP)", "Interest payments (% of GDP)", "Primary balance (% of GDP)"))) %>%
  mutate(OBS_VALUE = OBS_VALUE/100) %>%
  ggplot(.) + geom_line(aes(x = date, y = OBS_VALUE, color = Indicator)) + 
  theme_minimal() + xlab("") + ylab("Deficit, % of GDP") +
  scale_x_date(breaks = seq(1920, 2100, 5) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  theme(legend.position = "top",
        legend.title = element_blank(),
        axis.text.x = element_text(angle = 45, vjust = 1, hjust = 1)) +
  scale_y_continuous(breaks = 0.01*seq(-60, 60, 5),
                     labels = scales::percent_format(accuracy = 1)) +
  geom_hline(yintercept = 0, linetype = "dashed", color = "black") +
  facet_wrap(~ Ref_area)

Net lending/borrowing - GGXCNL_G01_GDP_PT

Italy, France, Germany

Code

ig_d("imf", "GGXCNL_G01_GDP_PT", "IT-FR-DE")

Australia, United Kingdom, United States

Code

ig_d("imf", "GGXCNL_G01_GDP_PT", "GB-US-AU")

Primary net lending/borrowing - GGXONLB_G01_GDP_PT

Italy, France, Germany

Code

ig_d("imf", "GGXONLB_G01_GDP_PT", "IT-FR-DE")

Australia, United Kingdom, United States

Code

ig_d("imf", "GGXONLB_G01_GDP_PT", "GB-US-AU")

Cyclically adjusted primary balance (% of potential GDP)

Table

Code

FM %>%
  filter(INDICATOR == "GGCBP_G01_PGDP_PT",
         date == as.Date("2018-01-01")) %>%
  left_join(CL_AREA_FM %>% rename(iso2c = AREA), by = "iso2c") %>%
  select(iso2c, AREA_desc, value) %>%
  arrange(-value) %>%
  na.omit %>%
  mutate_at(vars(3), funs(paste0(round(as.numeric(.), 1), " %"))) %>%
  {if (is_html_output()) datatable(., filter = 'top', rownames = F) else .}

Italy, France, Germany, United States

1990-

Code

FM %>%
  filter(INDICATOR == "GGCBP_G01_PGDP_PT",
         iso2c %in% c("IT", "FR", "DE", "US", "GR", "ES"),
         date <= as.Date("2019-01-01")) %>%
  left_join(CL_AREA_FM %>% rename(iso2c = AREA), by = "iso2c") %>%
  left_join(colors, by = c("AREA_desc" = "country")) %>%
  mutate(value = value/100,
         color = ifelse(iso2c == "US", color2, color)) %>%
  ggplot() + 
  geom_line(aes(x = date, y = value, color = color)) +
  scale_color_identity() + theme_minimal() +
  geom_image(data = . %>%
               filter(date == as.Date("2013-01-01")) %>%
               mutate(image = paste0("../../icon/flag/", str_to_lower(gsub(" ", "-", AREA_desc)), ".png")),
             aes(x = date, y = value, image = image), asp = 1.5) +
  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. primary balance (% of potential GDP)") + xlab("")

2005-

Code

FM %>%
  filter(INDICATOR == "GGCBP_G01_PGDP_PT",
         iso2c %in% c("IT", "FR", "DE", "US", "GR", "ES"),
         date <= as.Date("2019-01-01"),
         date >= as.Date("2006-01-01")) %>%
  left_join(CL_AREA_FM %>% rename(iso2c = AREA), by = "iso2c") %>%
  left_join(colors, by = c("AREA_desc" = "country")) %>%
  mutate(value = value/100,
         color = ifelse(iso2c == "US", color2, color)) %>%
  ggplot() + 
  geom_line(aes(x = date, y = value, color = color)) +
  scale_color_identity() + theme_minimal() +
  geom_image(data = . %>%
               filter(date == as.Date("2013-01-01")) %>%
               mutate(image = paste0("../../icon/flag/", str_to_lower(gsub(" ", "-", AREA_desc)), ".png")),
             aes(x = date, y = value, image = image), asp = 1.5) +
  scale_x_date(breaks = seq(1920, 2100, 2) %>% 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. primary balance (% of potential GDP)") + xlab("")

Denmark, Netherlands, Sweden

Code

FM %>%
  filter(INDICATOR == "GGCBP_G01_PGDP_PT",
         iso2c %in% c("DK", "SE", "NL"),
         date <= as.Date("2019-01-01")) %>%
  left_join(CL_AREA_FM %>% rename(iso2c = AREA), by = "iso2c") %>%
  left_join(colors, by = c("AREA_desc" = "country")) %>%
  mutate(OBS_VALUE = value / 100,
         Counterpart_area = AREA_desc) %>%
  ggplot() + 
  geom_line(aes(x = date, y = OBS_VALUE, color = color)) +
  scale_color_identity() + theme_minimal() + add_3flags +
  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. primary balance (% of potential GDP)") + xlab("")

Germany, Italy, Netherlands

Code

FM %>%
  filter(INDICATOR == "GGCBP_G01_PGDP_PT",
         iso2c %in% c("DE", "IT", "NL"),
         date >= as.Date("1995-01-01"),
         date <= as.Date("2019-01-01")) %>%
  left_join(CL_AREA_FM %>% rename(iso2c = AREA), by = "iso2c") %>%
  left_join(colors, by = c("AREA_desc" = "country")) %>%
  ggplot() + 
  geom_line(aes(x = date, y = value/100, color = color)) +
  scale_color_identity() + theme_minimal() +
  geom_image(data = . %>%
               filter(date == as.Date("2010-01-01")) %>%
               mutate(image = paste0("../../icon/flag/", str_to_lower(gsub(" ", "-", AREA_desc)), ".png")),
             aes(x = date, y = value/100, image = image), asp = 1.5) +
  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. primary balance (% of potential GDP)") + xlab("")

U.S., France, China

Code

FM %>%
  filter(INDICATOR == "GGCBP_G01_PGDP_PT",
         iso2c %in% c("US", "FR", "CN"),
         date >= as.Date("1995-01-01"),
         date <= as.Date("2019-01-01")) %>%
  left_join(CL_AREA_FM %>% rename(iso2c = AREA), by = "iso2c") %>%
  left_join(colors, by = c("AREA_desc" = "country")) %>%
  ggplot() + 
  geom_line(aes(x = date, y = value/100, color = color)) +
  scale_color_identity() + theme_minimal() +
  geom_image(data = . %>%
               filter(date == as.Date("2010-01-01")) %>%
               mutate(image = paste0("../../icon/flag/", str_to_lower(gsub(" ", "-", AREA_desc)), ".png")),
             aes(x = date, y = value/100, image = image), asp = 1.5) +
  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-Adjusted Primary Balance (CAPB)") + xlab("") +
  geom_hline(yintercept = 0, linetype = "dashed", color = "black")

Netherlands, UK, Italy

Code

FM %>%
  filter(INDICATOR == "GGCBP_G01_PGDP_PT",
         iso2c %in% c("NL", "GB", "IT"),
         date >= as.Date("1995-01-01"),
         date <= as.Date("2019-01-01")) %>%
  left_join(CL_AREA_FM %>% rename(iso2c = AREA), by = "iso2c") %>%
  left_join(colors, by = c("AREA_desc" = "country")) %>%
  mutate(value = value/100) %>%
  ggplot() + 
  geom_line(aes(x = date, y = value, color = color)) +
  scale_color_identity() + theme_minimal() +
  geom_image(data = . %>%
               filter(date == as.Date("2010-01-01")) %>%
               mutate(image = paste0("../../icon/flag/", str_to_lower(gsub(" ", "-", AREA_desc)), ".png")),
             aes(x = date, y = value, image = image), asp = 1.5) +
  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-Adjusted Primary Balance (CAPB)") + xlab("") +
  geom_hline(yintercept = 0, linetype = "dashed", color = "black")

Austria, Poland, Switzerland

Code

FM %>%
  filter(INDICATOR == "GGCBP_G01_PGDP_PT",
         iso2c %in% c("AT", "PL", "CH"),
         date >= as.Date("1995-01-01"),
         date <= as.Date("2019-01-01")) %>%
  left_join(CL_AREA_FM %>% rename(iso2c = AREA), by = "iso2c") %>%
  left_join(colors, by = c("AREA_desc" = "country")) %>%
  mutate(value = value/100) %>%
  ggplot() + 
  geom_line(aes(x = date, y = value, color = color)) +
  scale_color_identity() + theme_minimal() +
  geom_image(data = . %>%
               filter(date == as.Date("2010-01-01")) %>%
               mutate(image = paste0("../../icon/flag/", str_to_lower(gsub(" ", "-", AREA_desc)), ".png")),
             aes(x = date, y = value, image = image), asp = 1.5) +
  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-Adjusted Primary Balance (CAPB)") + xlab("") +
  geom_hline(yintercept = 0, linetype = "dashed", color = "black")

Belgium, Spain, Czechia

Code

FM %>%
  filter(INDICATOR == "GGCBP_G01_PGDP_PT",
         iso2c %in% c("BE", "ES", "CZ"),
         date >= as.Date("1995-01-01"),
         date <= as.Date("2019-01-01")) %>%
  left_join(CL_AREA_FM %>% rename(iso2c = AREA), by = "iso2c") %>%
  left_join(colors, by = c("AREA_desc" = "country")) %>%
  ggplot() + 
  geom_line(aes(x = date, y = value/100, color = color)) +
  scale_color_identity() + theme_minimal() +
  geom_image(data = . %>%
               filter(date == as.Date("2010-01-01")) %>%
               mutate(image = paste0("../../icon/flag/", str_to_lower(gsub(" ", "-", AREA_desc)), ".png")),
             aes(x = date, y = value/100, image = image), asp = 1.5) +
  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-Adjusted Primary Balance (CAPB)") + xlab("") +
  geom_hline(yintercept = 0, linetype = "dashed", color = "black")

Cyclically adjusted balance (% of potential GDP)

Table

Code

FM %>%
  filter(INDICATOR == "GGCB_G01_PGDP_PT",
         date == as.Date("2018-01-01")) %>%
  left_join(CL_AREA_FM %>% rename(iso2c = AREA), by = "iso2c") %>%
  select(iso2c, AREA_desc, value) %>%
  arrange(-value) %>%
  na.omit %>%
  mutate_at(vars(3), funs(paste0(round(as.numeric(.), 1), " %"))) %>%
  {if (is_html_output()) datatable(., filter = 'top', rownames = F) else .}

Italy, France, Germany, United States

Code

FM %>%
  filter(INDICATOR == "GGCB_G01_PGDP_PT",
         iso2c %in% c("IT", "FR", "DE", "US"),
         date <= as.Date("2019-01-01")) %>%
  left_join(CL_AREA_FM %>% rename(iso2c = AREA), by = "iso2c") %>%
  left_join(colors, by = c("AREA_desc" = "country")) %>%
  ggplot() + 
  geom_line(aes(x = date, y = value/100, color = color)) +
  scale_color_identity() + theme_minimal() +
  geom_image(data = . %>%
               filter(date == as.Date("2017-01-01")) %>%
               mutate(image = paste0("../../icon/flag/", str_to_lower(gsub(" ", "-", AREA_desc)), ".png")),
             aes(x = date, y = value/100, image = image), asp = 1.5) +
  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("")

Denmark, Netherlands, Sweden

Code

FM %>%
  filter(INDICATOR == "GGCB_G01_PGDP_PT",
         iso2c %in% c("DK", "SE", "NL"),
         date <= as.Date("2019-01-01")) %>%
  left_join(CL_AREA_FM %>% rename(iso2c = AREA), by = "iso2c") %>%
  left_join(colors, by = c("AREA_desc" = "country")) %>%
  ggplot() + 
  geom_line(aes(x = date, y = value/100, color = color)) +
  scale_color_identity() + theme_minimal() +
  geom_image(data = . %>%
               filter(date == as.Date("2010-01-01")) %>%
               mutate(image = paste0("../../icon/flag/", str_to_lower(gsub(" ", "-", AREA_desc)), ".png")),
             aes(x = date, y = value/100, image = image), asp = 1.5) +
  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("")

Germany, Italy, Netherlands

Code

FM %>%
  filter(INDICATOR == "GGCB_G01_PGDP_PT",
         iso2c %in% c("DE", "IT", "NL"),
         date >= as.Date("1995-01-01"),
         date <= as.Date("2019-01-01")) %>%
  left_join(CL_AREA_FM %>% rename(iso2c = AREA), by = "iso2c") %>%
  left_join(colors, by = c("AREA_desc" = "country")) %>%
  ggplot() + 
  geom_line(aes(x = date, y = value/100, color = color)) +
  scale_color_identity() + theme_minimal() +
  geom_image(data = . %>%
               filter(date == as.Date("2010-01-01")) %>%
               mutate(image = paste0("../../icon/flag/", str_to_lower(gsub(" ", "-", AREA_desc)), ".png")),
             aes(x = date, y = value/100, image = image), asp = 1.5) +
  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("")

Germany: Average Demand of Main Trading Partners

Germany

Code

FM %>%
  filter(INDICATOR == "GGCB_G01_PGDP_PT",
         date >= as.Date("1995-01-01"),
         date <= as.Date("2020-01-01")) %>%
  select(iso2c, date, value) %>%
  inner_join(WTS %>%
              mutate(date = paste0(TIME_PERIOD, "-01-01") %>% as.Date) %>%
              filter(REF_AREA == "DE",
                     TRADE_WEIGHT == "O", # Overall trade weight
                     CURRENCY_TRANS == "H10", # Euro area-19 countries and EER-38 group of trading partners
                     date == as.Date("1995-01-01")) %>%
              select(iso2c = COUNT_AREA, share_1995 = OBS_VALUE) %>%
               mutate(share_1995 = share_1995/sum(share_1995)), 
            by = "iso2c") %>%
  inner_join(WTS %>%
              mutate(date = paste0(TIME_PERIOD, "-01-01") %>% as.Date) %>%
              filter(REF_AREA == "DE",
                     TRADE_WEIGHT == "O", # Overall trade weight
                     CURRENCY_TRANS == "H10", # Euro area-19 countries and EER-38 group of trading partners
                     date == as.Date("2015-01-01")) %>%
              select(iso2c = COUNT_AREA, share_2015 = OBS_VALUE) %>%
               mutate(share_2015 = share_2015/sum(share_2015)), 
            by = "iso2c") %>%
  group_by(date) %>%
  summarise(`Germany's Partners CAPB (1995 shares)` = sum(share_1995*value),
            `Germany's Partners CAPB (2015 shares)` = sum(share_2015*value)) %>%
  left_join(FM %>%
              filter(INDICATOR == "GGCBP_G01_PGDP_PT",
                     iso2c %in% c("DE"),
                     date >= as.Date("1995-01-01"),
                     date <= as.Date("2019-01-01")) %>%
              select(date, `Germany's CAPB` = value),
            by = "date") %>%
  gather(variable, value, -date) %>%
  ggplot() + 
  geom_line(aes(x = date, y = value/100, color = variable, linetype = variable)) +
  scale_color_manual(values = viridis(5)[1:4]) +
  theme_minimal() +
  scale_x_date(breaks = seq(1920, 2100, 5) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  theme(legend.position = c(0.3, 0.9),
        legend.title = element_blank()) +
  scale_y_continuous(breaks = 0.01*seq(-60, 60, 1),
                     labels = scales::percent_format(accuracy = 1)) +
  ylab("Cyclically-Adjusted Primary Balance (CAPB)") + xlab("") +
  geom_hline(yintercept = 0, linetype = "dashed", color = "black")