Risk Assessment Indicators

Data - ECB

Author

François Geerolf

Info

source dataset Title .html .rData
ecb RAI Risk Assessment Indicators 2026-03-11 2026-03-11
  • Data Structure Definition. (DSD) html

Data on monetary policy

source dataset Title .html .rData
bdf FM Marché financier, taux 2026-03-11 2026-03-11
bdf MIR Taux d'intérêt - Zone euro 2026-03-11 2025-08-04
bdf MIR1 Taux d'intérêt - France 2026-03-11 2025-08-04
bis CBPOL Policy Rates, Daily 2026-01-11 2026-03-11
ecb BSI Balance Sheet Items 2026-03-11 2026-01-11
ecb BSI_PUB Balance Sheet Items - Published series 2026-03-11 2026-03-11
ecb FM Financial market data 2026-03-11 2026-03-11
ecb ILM Internal Liquidity Management 2026-03-11 2026-03-11
ecb ILM_PUB Internal Liquidity Management - Published series 2026-03-11 2026-03-11
ecb MIR MFI Interest Rate Statistics 2026-03-11 2026-03-11
ecb RAI Risk Assessment Indicators 2026-03-11 2026-03-11
ecb SUP Supervisory Banking Statistics 2025-12-19 2026-03-11
ecb YC Financial market data - yield curve 2026-03-11 2026-01-11
ecb YC_PUB Financial market data - yield curve - Published series 2026-03-11 2026-03-11
ecb liq_daily Daily Liquidity 2026-03-11 2025-06-06
eurostat ei_mfir_m Interest rates - monthly data 2026-03-11 2026-03-11
eurostat irt_st_m Money market interest rates - monthly data 2026-03-11 2026-03-11
fred r Interest Rates 2026-03-11 2026-03-11
oecd MEI Main Economic Indicators 2024-04-16 2025-07-24
oecd MEI_FIN Monthly Monetary and Financial Statistics (MEI) 2024-09-15 2025-07-24

Data on interest rates

source dataset Title .html .rData
bdf FM Marché financier, taux 2026-03-11 2026-03-11
bdf MIR Taux d'intérêt - Zone euro 2026-03-11 2025-08-04
bdf MIR1 Taux d'intérêt - France 2026-03-11 2025-08-04
bis CBPOL_D Policy Rates, Daily 2026-01-11 2025-08-20
bis CBPOL_M Policy Rates, Monthly 2026-01-11 2024-04-19
ecb FM Financial market data 2026-03-11 2026-03-11
ecb MIR MFI Interest Rate Statistics 2026-03-11 2026-03-11
eurostat ei_mfir_m Interest rates - monthly data 2026-03-11 2026-03-11
eurostat irt_lt_mcby_d EMU convergence criterion series - daily data 2026-03-11 2025-07-24
eurostat irt_st_m Money market interest rates - monthly data 2026-03-11 2026-03-11
fred r Interest Rates 2026-03-11 2026-03-11
oecd MEI Main Economic Indicators 2024-04-16 2025-07-24
oecd MEI_FIN Monthly Monetary and Financial Statistics (MEI) 2024-09-15 2025-07-24
wdi FR.INR.DPST Deposit interest rate (%) 2022-09-27 2026-03-11
wdi FR.INR.LEND Lending interest rate (%) 2026-03-11 2026-03-11
wdi FR.INR.RINR Real interest rate (%) 2026-01-11 2026-03-11

LAST_COMPILE

LAST_COMPILE
2026-03-14

Last

TIME_PERIOD FREQ Nobs
2026-01 M 373
2025-Q4 Q 118

DD_ECON_CONCEPT

Code 
RAI %>%
  left_join(DD_ECON_CONCEPT, by = "DD_ECON_CONCEPT") %>%
  group_by(DD_ECON_CONCEPT, Dd_econ_concept) %>%
  summarise(Nobs = n()) %>%
  arrange(-Nobs) %>%
  print_table_conditional()
DD_ECON_CONCEPT Dd_econ_concept Nobs
LMGBLNFCH Lending margin on new business loans to non-financial corporations and households 8328
LMGOLNFCH Lending margin on outstanding loans to non-financial corporations and households 8305
IBL1TL Share of interbank loans in total loans 8285
CT1DGGV Share of other MFIs credit to domestic general government in total assets, excluding remaining assets 7928
LC1DHHS Share of other MFIs loans to domestic households for house purchase in total credit to other domestic residents 7851
LEVR Leverage ratio 7770
NDEPFUN Non-deposit funding 7674
SVLHHNFC Share of new loans with a floating rate or an initial rate fixation period of up to one year in total new loans from MFIs to households and non-financial corporations 7581
SVLHPHH Share of new loans to households for house purchase with a floating rate or an initial rate fixation period of up to one year in total new loans from MFIs to households 7581
LMGLHH MFIs lending margins on loans for house purchase 7030
LMGLNFC MFIs lending margins on loans to non-financial corporations (NFC) 7030
GRNLHHNFC Annual growth rate of MFIs new loans to households and non-financial corporations 6675
ST1TMF Share of short-term funding in total market funding 6588
MMTCH Maturity mismatch 5789
FXL1TL Share of other MFI FX loans in total loans (excluding inter-MFI loans) 2779
LTD Loans to deposits ratio 2717
LA1STL Share of liquid assets in short term liabilities 2177
OTHOFI1 Total assets of other financial institutions (OFIs) excluding financial vehicle corporations (FVCs), outstanding amounts at the end of the period (stocks) 1903
OTHOFI4 Total assets of other financial institutions (OFIs) excluding financial vehicle corporations (FVCs), financial transactions (flows) 1901
SVLOAHH NA 1722
SVLOANFC NA 1722
IFOFI1 Total assets of MMF and non-MMF investment funds and other financial institutions (OFIs), outstanding amounts at the end of the period (stocks) 285
IFOFI4 Total assets of MMF and non-MMF investment funds and other financial institutions (OFIs), financial transactions (flows) 285
CRED1 Credit institutions (MFIs excluding the ESCB and MMFs), outstanding amounts at the end of the period (stocks) 192
CREDA Growth rate of total assets of credit institutions (MFIs excluding the ESCB and MMFs) 184
ICPFA Growth rate of total assets of insurance corporations and pension funds 182
IFOFIA Growth rate of total assets of MMF and non-MMF investment funds and other financial institutions (OFIs) 182

DD_SUFFIX

Code 
RAI %>%
  left_join(DD_SUFFIX, by = "DD_SUFFIX") %>%
  group_by(DD_SUFFIX, Dd_suffix) %>%
  summarise(Nobs = n()) %>%
  arrange(-Nobs) %>%
  print_table_conditional()
DD_SUFFIX Dd_suffix Nobs
Z Not applicable 113301
E Euro 4566
P10 Currency ratio on total currency 2779

SOURCE_DATA

Code 
RAI %>%
  left_join(SOURCE_DATA, by = "SOURCE_DATA") %>%
  group_by(SOURCE_DATA, Source_data) %>%
  summarise(Nobs = n()) %>%
  arrange(-Nobs) %>%
  print_table_conditional()
SOURCE_DATA Source_data Nobs
BSI Based on BSI data 63378
MIR Based on MIR data 52530
QSA Based on quarterly sector accounts data 4556
ICPF Based on ICPF data 182

DD_SUFFIX

Code 
RAI %>%
  left_join(DD_SUFFIX, by = "DD_SUFFIX") %>%
  group_by(DD_SUFFIX, Dd_suffix) %>%
  summarise(Nobs = n()) %>%
  arrange(-Nobs) %>%
  print_table_conditional()
DD_SUFFIX Dd_suffix Nobs
Z Not applicable 113301
E Euro 4566
P10 Currency ratio on total currency 2779

FREQ

Code 
RAI %>%
  left_join(FREQ, by = "FREQ") %>%
  group_by(FREQ, Freq) %>%
  summarise(Nobs = n()) %>%
  arrange(-Nobs) %>%
  print_table_conditional()
FREQ Freq Nobs
M Monthly 104415
Q Quarterly 16231

REF_AREA

Code 
RAI %>%
  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/vsmall/', Flag, '.png" alt="Flag">')) %>%
  select(Flag, everything()) %>%
  {if (is_html_output()) datatable(., filter = 'top', rownames = F, escape = F) else .}

Table: Average 2016-2022

Code 
RAI %>%
  filter(FREQ == "M") %>%
  left_join(REF_AREA, by = "REF_AREA") %>%
  left_join(DD_ECON_CONCEPT, by = "DD_ECON_CONCEPT") %>%
  month_to_date %>%
  filter(date >= as.Date("2016-01-01")) %>%
  group_by(DD_ECON_CONCEPT, Dd_econ_concept, REF_AREA, Ref_area) %>%
  summarise(OBS_VALUE = mean(OBS_VALUE),
            Nobs = n()) %>%
  print_table_conditional()

France

Table

Code 
RAI %>%
  filter(FREQ == "M",
         REF_AREA %in% c("FR", "U2")) %>%
  select_if(~ n_distinct(.) > 1) %>%
  left_join(REF_AREA, by = "REF_AREA") %>%
  group_by(DD_ECON_CONCEPT, Ref_area) %>%
  filter(TIME_PERIOD == max(TIME_PERIOD)) %>%
  left_join(DD_ECON_CONCEPT, by = "DD_ECON_CONCEPT") %>%
  select(Ref_area, DD_ECON_CONCEPT, OBS_VALUE) %>%
  spread(Ref_area, OBS_VALUE) %>%
  arrange(-`France`) %>%
  print_table_conditional()
DD_ECON_CONCEPT Euro area (Member States and Institutions of the Euro Area) changing composition France
MMTCH 77.213810 78.3491734
ST1TMF 68.810045 77.0352311
SVLHHNFC 63.665684 38.7279886
IBL1TL 24.804635 36.9408768
LC1DHHS NA 36.4879954
NDEPFUN 14.592488 15.6143057
GRNLHHNFC NA 9.6936034
LEVR 8.405612 7.2303894
CT1DGGV NA 4.5031068
SVLHPHH 15.056147 2.5955462
LMGLNFC NA 1.4007693
LMGBLNFCH NA 1.2648150
LMGLHH NA 0.9460608
LMGOLNFCH NA 0.2051478

SVLHHNFC, LC1DHHS

Code 
RAI %>%
  filter(DD_ECON_CONCEPT %in% c("SVLHHNFC", "LC1DHHS"),
         REF_AREA %in% c("FR", "U2")) %>%
  left_join(REF_AREA, by = "REF_AREA") %>%
  left_join(DD_ECON_CONCEPT, by = "DD_ECON_CONCEPT") %>%
  month_to_date %>%
  select_if(~n_distinct(.) > 1) %>%
  mutate(Ref_area = ifelse(REF_AREA == "U2", "Europe", Ref_area)) %>%
  left_join(colors, by = c("Ref_area" = "country")) %>%
  mutate(OBS_VALUE = OBS_VALUE/100) %>%
  ggplot(.) + theme_minimal() + xlab("") + ylab("Share of variable rate") +
  geom_line(aes(x = date, y = OBS_VALUE, color = color, linetype = Dd_econ_concept)) + 
  add_flags(3) + scale_color_identity() +
  scale_x_date(breaks = seq(1960, 2030, 2) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  theme(legend.position = c(0.7, 0.9),
        legend.title = element_blank()) +
  scale_y_continuous(breaks = 0.01*seq(-10, 100, 5),
                     labels = percent_format(accuracy = 1))

CT1DGGV, SVLHPHH

Code 
RAI %>%
  filter(DD_ECON_CONCEPT %in% c("CT1DGGV", "SVLHPHH"),
         REF_AREA %in% c("FR", "U2")) %>%
  left_join(REF_AREA, by = "REF_AREA") %>%
  left_join(DD_ECON_CONCEPT, by = "DD_ECON_CONCEPT") %>%
  month_to_date %>%
  select_if(~n_distinct(.) > 1) %>%
  mutate(Ref_area = ifelse(REF_AREA == "U2", "Europe", Ref_area)) %>%
  left_join(colors, by = c("Ref_area" = "country")) %>%
  mutate(OBS_VALUE = OBS_VALUE/100) %>%
  ggplot(.) + theme_minimal() + xlab("") + ylab("Share of variable rate") +
  geom_line(aes(x = date, y = OBS_VALUE, color = color, linetype = Dd_econ_concept)) + 
  add_flags(3) + scale_color_identity() +
  scale_x_date(breaks = seq(1960, 2030, 2) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  theme(legend.position = c(0.7, 0.9),
        legend.title = element_blank()) +
  scale_y_continuous(breaks = 0.01*seq(-10, 100, 5),
                     labels = percent_format(accuracy = 1))

SVLHPHH - Share of floating rates, Households

Table: Last Time

Code 
RAI %>%
  filter(DD_ECON_CONCEPT == "SVLHPHH",
         TIME_PERIOD %in% c(last_time)) %>%
  left_join(REF_AREA, by = "REF_AREA") %>%
  select_if(~n_distinct(.) > 1) %>%
  select(REF_AREA, Ref_area, OBS_VALUE, OBS_VALUE) %>%
  mutate(OBS_VALUE = round(OBS_VALUE, 1)) %>%
  mutate(Flag = gsub(" ", "-", str_to_lower(gsub(" ", "-", Ref_area))),
         Flag = paste0('<img src="../../icon/flag/vsmall/', Flag, '.png" alt="Flag">')) %>%
  select(Flag, everything()) %>%
  arrange(OBS_VALUE) %>%
  {if (is_html_output()) datatable(., filter = 'top', rownames = F, escape = F) else .}

Table: Many dates

Code 
RAI %>%
  filter(DD_ECON_CONCEPT == "SVLHPHH",
         TIME_PERIOD %in% c(last_time, "2020-01", "2015-01", "2010-01", "2005-01")) %>%
  left_join(REF_AREA, by = "REF_AREA") %>%
  select_if(~n_distinct(.) > 1) %>%
  select(REF_AREA, Ref_area, TIME_PERIOD, OBS_VALUE) %>%
  mutate(OBS_VALUE = round(OBS_VALUE, 1)) %>%
  spread(TIME_PERIOD, OBS_VALUE) %>%
  print_table_conditional()
REF_AREA Ref_area 2005-01 2010-01 2015-01 2020-01 2026-01
AT Austria 65.7 76.2 87.3 40.6 21.2
BE Belgium 58.2 58.4 2.4 4.5 7.5
BG Bulgaria NA 96.2 84.0 97.3 99.2
CY Cyprus NA 61.7 95.1 90.7 11.6
CZ Czech Republic NA NA 7.2 2.1 6.3
DE Germany 19.1 21.6 13.2 11.6 12.9
DK Denmark 70.1 49.2 NA NA 46.1
EE Estonia 99.3 56.7 85.4 NA 97.6
ES Spain 93.1 90.1 66.6 32.1 7.3
FI Finland 97.8 97.5 96.8 97.6 94.0
FR France 36.5 12.8 3.8 1.9 2.6
GR Greece 84.4 72.2 93.1 70.2 34.8
HR Croatia NA NA 84.9 20.5 3.7
HU Hungary 58.1 84.4 44.1 2.1 8.9
IE Ireland 93.6 84.1 66.0 25.8 10.9
IT Italy 87.3 81.3 71.7 18.0 20.4
LT Lithuania 97.9 84.7 89.8 97.8 92.8
LU Luxembourg 88.5 NA 63.9 30.1 52.7
LV Latvia 72.3 84.4 NA 94.3 94.4
MT Malta NA 88.0 77.9 47.4 33.3
NL Netherlands 43.2 24.9 19.9 17.4 16.9
PL Poland 90.9 100.0 99.9 100.0 32.1
PT Portugal 97.8 99.5 93.0 86.7 23.7
RO Romania NA NA 89.9 73.6 18.6
SE Sweden NA 85.6 85.5 64.2 88.2
SI Slovenia 99.1 98.1 93.4 55.5 1.8
SK Slovakia 62.4 36.0 6.4 1.1 0.9
U2 Euro area (Member States and Institutions of the Euro Area) changing composition 54.7 42.6 21.6 15.8 15.1

Netherlands, Germany, Belgium, France, Europe

Code 
RAI %>%
  filter(DD_ECON_CONCEPT == "SVLHPHH",
         REF_AREA %in% c("NL", "DE", "BE", "FR",  "U2")) %>%
  left_join(REF_AREA, by = "REF_AREA") %>%
  month_to_date %>%
  select_if(~n_distinct(.) > 1) %>%
  mutate(Ref_area = ifelse(REF_AREA == "U2", "Europe", Ref_area)) %>%
  left_join(colors, by = c("Ref_area" = "country")) %>%
  mutate(OBS_VALUE = OBS_VALUE/100) %>%
  ggplot(.) + theme_minimal() + xlab("") + ylab("Share of variable rate, households (%)") +
  geom_line(aes(x = date, y = OBS_VALUE, color = color)) + 
  add_flags(5) + scale_color_identity() +
  scale_x_date(breaks = seq(1960, 2030, 2) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  scale_y_continuous(breaks = 0.01*seq(-10, 100, 5),
                     labels = percent_format(accuracy = 1))

France, Spain, Portugal, latvia, Lithuania, Estonia

Code 
RAI %>%
  filter(DD_ECON_CONCEPT == "SVLHPHH",
         REF_AREA %in% c("FR", "ES", "PT", "EE", "LT", "U2")) %>%
  left_join(REF_AREA, by = "REF_AREA") %>%
  month_to_date %>%
  select_if(~n_distinct(.) > 1) %>%
  mutate(Ref_area = ifelse(REF_AREA == "U2", "Europe", Ref_area)) %>%
  left_join(colors, by = c("Ref_area" = "country")) %>%
  mutate(OBS_VALUE = OBS_VALUE/100) %>%
  ggplot(.) + theme_minimal() + xlab("") + ylab("Share of variable rate, households (%)") +
  geom_line(aes(x = date, y = OBS_VALUE, color = color)) + 
  add_flags(6) + scale_color_identity() +
  scale_x_date(breaks = seq(1960, 2030, 2) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  scale_y_continuous(breaks = 0.01*seq(-10, 100, 5),
                     labels = percent_format(accuracy = 1))

France, Spain, Portugal

Code 
RAI %>%
  filter(DD_ECON_CONCEPT == "SVLHPHH",
         REF_AREA %in% c("FR", "ES", "PT")) %>%
  left_join(REF_AREA, by = "REF_AREA") %>%
  month_to_date %>%
  select_if(~n_distinct(.) > 1) %>%
  left_join(colors, by = c("Ref_area" = "country")) %>%
  mutate(OBS_VALUE = OBS_VALUE/100) %>%
  ggplot(.) + theme_minimal() + xlab("") + ylab("Share of variable rate, households (%)") +
  geom_line(aes(x = date, y = OBS_VALUE, color = color)) + 
  add_flags(3) + scale_color_identity() +
  scale_x_date(breaks = seq(1960, 2030, 2) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  scale_y_continuous(breaks = 0.01*seq(-10, 100, 5),
                     labels = percent_format(accuracy = 1))

Italy, Sweden, Poland

Code 
RAI %>%
  filter(DD_ECON_CONCEPT == "SVLHPHH",
         REF_AREA %in% c("PL", "IT", "SE")) %>%
  left_join(REF_AREA, by = "REF_AREA") %>%
  month_to_date %>%
  select_if(~n_distinct(.) > 1) %>%
  left_join(colors, by = c("Ref_area" = "country")) %>%
  mutate(OBS_VALUE = OBS_VALUE/100) %>%
  ggplot(.) + theme_minimal() + xlab("") + ylab("Share of variable rate, households (%)") +
  geom_line(aes(x = date, y = OBS_VALUE, color = color)) + 
  add_flags(3) + scale_color_identity() +
  scale_x_date(breaks = seq(1960, 2030, 2) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  scale_y_continuous(breaks = 0.01*seq(-10, 100, 5),
                     labels = percent_format(accuracy = 1))

Netherlands, Germany, Belgium

Code 
RAI %>%
  filter(DD_ECON_CONCEPT == "SVLHPHH",
         REF_AREA %in% c("NL", "DE", "BE")) %>%
  left_join(REF_AREA, by = "REF_AREA") %>%
  month_to_date %>%
  select_if(~n_distinct(.) > 1) %>%
  left_join(colors, by = c("Ref_area" = "country")) %>%
  mutate(OBS_VALUE = OBS_VALUE/100) %>%
  ggplot(.) + theme_minimal() + xlab("") + ylab("Share of variable rate, households (%)") +
  geom_line(aes(x = date, y = OBS_VALUE, color = color)) + 
  add_flags(3) + scale_color_identity() +
  scale_x_date(breaks = seq(1960, 2030, 2) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  scale_y_continuous(breaks = 0.01*seq(-10, 100, 5),
                     labels = percent_format(accuracy = 1))

SVLHHNFC - Share of floating rates, Households, and Corporations

Table

Code 
RAI %>%
  filter(DD_ECON_CONCEPT == "SVLHHNFC",
         TIME_PERIOD %in% c(last_time, "2020-01", "2015-01", "2010-01", "2005-01")) %>%
  left_join(REF_AREA, by = "REF_AREA") %>%
  select_if(~n_distinct(.) > 1) %>%
  select(REF_AREA, Ref_area, TIME_PERIOD, OBS_VALUE) %>%
  mutate(OBS_VALUE = round(OBS_VALUE, 1)) %>%
  spread(TIME_PERIOD, OBS_VALUE) %>%
  print_table_conditional()
REF_AREA Ref_area 2005-01 2010-01 2015-01 2020-01 2026-01
AT Austria 91.7 92.3 89.7 70.1 69.1
BE Belgium 90.7 90.4 76.4 73.3 76.9
BG Bulgaria NA 98.7 95.8 95.8 98.5
CY Cyprus NA 85.4 96.1 94.2 64.0
CZ Czech Republic 80.8 76.5 51.7 48.7 34.0
DE Germany 61.5 72.6 59.4 61.3 65.6
DK Denmark 76.4 64.5 43.6 50.2 66.5
EE Estonia 92.0 71.1 80.4 83.6 93.8
ES Spain 91.5 91.4 88.1 70.8 72.2
FI Finland 94.3 NA NA 94.8 NA
FR France 64.8 45.3 39.8 32.3 38.7
GR Greece 82.9 86.1 97.7 91.6 81.0
HR Croatia NA NA 88.8 57.8 47.3
HU Hungary 95.3 94.5 75.0 52.4 34.0
IE Ireland 83.0 87.3 82.3 67.3 45.5
IT Italy 90.4 93.9 92.4 73.4 74.9
LT Lithuania 93.6 88.3 93.9 89.1 81.5
LU Luxembourg 98.4 99.1 95.0 91.3 90.1
LV Latvia 72.6 82.3 80.1 95.3 90.1
MT Malta NA 98.7 89.4 59.6 73.6
NL Netherlands 71.1 73.5 57.2 45.0 50.3
PL Poland 94.5 93.6 86.6 90.2 65.6
PT Portugal 95.7 95.9 90.9 74.8 51.4
RO Romania NA 96.6 85.9 67.3 49.7
SE Sweden NA 89.2 90.6 78.7 88.1
SI Slovenia 89.5 93.5 91.5 78.9 49.5
SK Slovakia 83.2 78.9 66.7 23.4 29.1
U2 Euro area (Member States and Institutions of the Euro Area) changing composition 79.2 81.5 69.9 60.5 63.7

France, Spain, Portugal

Code 
RAI %>%
  filter(DD_ECON_CONCEPT == "SVLHHNFC",
         REF_AREA %in% c("FR", "ES", "PT")) %>%
  left_join(REF_AREA, by = "REF_AREA") %>%
  month_to_date %>%
  select_if(~n_distinct(.) > 1) %>%
  left_join(colors, by = c("Ref_area" = "country")) %>%
  mutate(OBS_VALUE = OBS_VALUE/100) %>%
  ggplot(.) + theme_minimal() + xlab("") + ylab("Share of variable rate, households and firms (%)") +
  geom_line(aes(x = date, y = OBS_VALUE, color = color)) + 
  add_flags(3) + scale_color_identity() +
  scale_x_date(breaks = seq(1960, 2030, 2) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  scale_y_continuous(breaks = 0.01*seq(-10, 100, 5),
                     labels = percent_format(accuracy = 1))

Italy, Sweden, Poland

Code 
RAI %>%
  filter(DD_ECON_CONCEPT == "SVLHHNFC",
         REF_AREA %in% c("PL", "IT", "SE")) %>%
  left_join(REF_AREA, by = "REF_AREA") %>%
  month_to_date %>%
  select_if(~n_distinct(.) > 1) %>%
  left_join(colors, by = c("Ref_area" = "country")) %>%
  mutate(OBS_VALUE = OBS_VALUE/100) %>%
  ggplot(.) + theme_minimal() + xlab("") + ylab("Share of variable rate, households and firms (%)") +
  geom_line(aes(x = date, y = OBS_VALUE, color = color)) + 
  add_flags(3) + scale_color_identity() +
  scale_x_date(breaks = seq(1960, 2030, 2) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  scale_y_continuous(breaks = 0.01*seq(-10, 100, 5),
                     labels = percent_format(accuracy = 1))

Netherlands, Germany, Belgium

Code 
RAI %>%
  filter(DD_ECON_CONCEPT == "SVLHHNFC",
         REF_AREA %in% c("NL", "DE", "BE")) %>%
  left_join(REF_AREA, by = "REF_AREA") %>%
  month_to_date %>%
  select_if(~n_distinct(.) > 1) %>%
  left_join(colors, by = c("Ref_area" = "country")) %>%
  mutate(OBS_VALUE = OBS_VALUE/100) %>%
  ggplot(.) + theme_minimal() + xlab("") + ylab("Share of variable rate, households and firms (%)") +
  geom_line(aes(x = date, y = OBS_VALUE, color = color)) + 
  add_flags(3) + scale_color_identity() +
  scale_x_date(breaks = seq(1960, 2030, 2) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  scale_y_continuous(breaks = 0.01*seq(-10, 100, 5),
                     labels = percent_format(accuracy = 1))