Risk Assessment Indicators

Data - ECB

Author

François Geerolf

Info

source dataset Title .html .rData
ecb RAI Risk Assessment Indicators 2026-02-08 2026-02-08
  • Data Structure Definition. (DSD) html

Data on monetary policy

source dataset Title .html .rData
bdf FM Marché financier, taux 2026-02-08 2026-02-08
bdf MIR Taux d'intérêt - Zone euro 2026-02-08 2025-08-04
bdf MIR1 Taux d'intérêt - France 2026-02-08 2025-08-04
bis CBPOL Policy Rates, Daily 2026-01-11 2026-02-08
ecb BSI Balance Sheet Items 2026-02-08 2026-01-11
ecb BSI_PUB Balance Sheet Items - Published series 2026-02-08 2026-02-08
ecb FM Financial market data 2026-02-08 2026-02-08
ecb ILM Internal Liquidity Management 2026-02-08 2026-02-08
ecb ILM_PUB Internal Liquidity Management - Published series 2026-02-08 2026-02-08
ecb MIR MFI Interest Rate Statistics 2026-02-08 2026-02-08
ecb RAI Risk Assessment Indicators 2026-02-08 2026-02-08
ecb SUP Supervisory Banking Statistics 2025-12-19 2026-02-08
ecb YC Financial market data - yield curve 2026-02-08 2026-01-11
ecb YC_PUB Financial market data - yield curve - Published series 2026-02-08 2026-02-08
ecb liq_daily Daily Liquidity 2026-02-08 2025-06-06
eurostat ei_mfir_m Interest rates - monthly data 2026-02-08 2026-02-08
eurostat irt_st_m Money market interest rates - monthly data 2026-02-08 2026-02-08
fred r Interest Rates 2026-02-08 2026-02-08
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 2026-02-08 2026-02-08
bdf MIR Taux d'intérêt - Zone euro 2026-02-08 2025-08-04
bdf MIR1 Taux d'intérêt - France 2026-02-08 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 2026-02-08 2026-02-08
ecb MIR MFI Interest Rate Statistics 2026-02-08 2026-02-08
eurostat ei_mfir_m Interest rates - monthly data 2026-02-08 2026-02-08
eurostat irt_lt_mcby_d EMU convergence criterion series - daily data 2026-02-08 2025-07-24
eurostat irt_st_m Money market interest rates - monthly data 2026-02-08 2026-02-08
fred r Interest Rates 2026-02-08 2026-02-08
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 2026-02-08
wdi FR.INR.LEND Lending interest rate (%) 2026-02-08 2026-02-08
wdi FR.INR.RINR Real interest rate (%) 2026-01-11 2026-02-08

LAST_COMPILE

LAST_COMPILE
2026-02-08

Last

TIME_PERIOD FREQ Nobs
2025-Q3 Q 168
2025-12 M 373

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 8301
LMGOLNFCH Lending margin on outstanding loans to non-financial corporations and households 8278
IBL1TL Share of interbank loans in total loans 8257
CT1DGGV Share of other MFIs credit to domestic general government in total assets, excluding remaining assets 7901
LC1DHHS Share of other MFIs loans to domestic households for house purchase in total credit to other domestic residents 7824
LEVR Leverage ratio 7742
NDEPFUN Non-deposit funding 7646
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 7505
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 7505
LMGLHH MFIs lending margins on loans for house purchase 7003
LMGLNFC MFIs lending margins on loans to non-financial corporations (NFC) 7003
GRNLHHNFC Annual growth rate of MFIs new loans to households and non-financial corporations 6648
ST1TMF Share of short-term funding in total market funding 6566
MMTCH Maturity mismatch 5767
FXL1TL Share of other MFI FX loans in total loans (excluding inter-MFI loans) 2736
LTD Loans to deposits ratio 2689
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 1678
SVLOANFC NA 1678
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) 190
CREDA Growth rate of total assets of credit institutions (MFIs excluding the ESCB and MMFs) 182
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 112714
E Euro 4564
P10 Currency ratio on total currency 2736

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 63033
MIR Based on MIR data 52243
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 112714
E Euro 4564
P10 Currency ratio on total currency 2736

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 103946
Q Quarterly 16068

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 77.105298 78.2316721
ST1TMF 67.849537 76.5227861
SVLHHNFC 65.595269 43.7700377
LC1DHHS NA 36.6196307
IBL1TL 24.339464 36.0679636
NDEPFUN 14.734977 15.7341663
GRNLHHNFC NA 10.5127339
LEVR 8.486102 7.2678837
CT1DGGV NA 4.5297820
SVLHPHH 14.032824 3.1566278
LMGLNFC NA 1.3877009
LMGBLNFCH NA 1.2421133
LMGLHH NA 0.8235101
LMGOLNFCH NA 0.2123519

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 2025-12
AT Austria 65.7 76.2 87.3 40.6 20.2
BE Belgium 58.2 58.4 2.4 4.5 7.1
BG Bulgaria NA 96.2 84.0 97.3 99.3
CY Cyprus NA 61.7 95.1 90.7 14.0
CZ Czech Republic NA NA 7.2 2.1 6.5
DE Germany 19.1 21.6 13.2 11.6 12.5
DK Denmark 70.1 49.2 NA NA NA
EE Estonia 99.3 56.7 85.4 NA 98.6
ES Spain 93.1 90.1 66.6 32.1 6.0
FI Finland 97.8 97.5 96.8 97.6 95.8
FR France 36.5 12.8 3.8 1.9 3.2
GR Greece 84.4 72.2 93.1 70.2 36.0
HR Croatia NA NA 84.9 20.5 3.7
HU Hungary 58.1 84.4 44.1 2.1 8.0
IE Ireland 93.6 84.1 66.0 25.8 10.2
IT Italy 87.3 81.3 71.7 18.0 18.5
LT Lithuania 97.9 84.7 89.8 97.8 94.6
LU Luxembourg 88.5 NA 63.9 30.1 NA
LV Latvia 72.3 84.4 NA 94.3 96.1
MT Malta NA 88.0 77.9 47.4 43.5
NL Netherlands 43.2 24.9 19.9 17.4 17.9
PL Poland 90.9 100.0 99.9 100.0 32.4
PT Portugal 97.8 99.5 93.0 86.7 22.5
RO Romania NA NA 89.9 73.6 24.0
SE Sweden NA 85.6 85.5 64.2 88.0
SI Slovenia 99.1 98.1 93.4 55.5 1.2
SK Slovakia 62.4 36.0 6.4 1.1 1.2
U2 Euro area (Member States and Institutions of the Euro Area) changing composition 54.7 42.6 21.6 15.8 14.0

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 2025-12
AT Austria 91.7 92.3 89.7 70.1 72.1
BE Belgium 90.7 90.4 76.4 73.3 76.0
BG Bulgaria NA 98.7 95.8 95.8 98.7
CY Cyprus NA 85.4 96.1 94.2 60.0
CZ Czech Republic 80.8 76.5 51.7 48.7 58.2
DE Germany 61.5 72.6 59.4 61.3 67.2
DK Denmark 76.4 64.5 43.6 50.2 60.7
EE Estonia 92.0 71.1 80.4 83.6 95.0
ES Spain 91.5 91.4 88.1 70.8 73.3
FI Finland 94.3 NA NA 94.8 NA
FR France 64.8 45.3 39.8 32.3 43.8
GR Greece 82.9 86.1 97.7 91.6 94.8
HR Croatia NA NA 88.8 57.8 51.5
HU Hungary 95.3 94.5 75.0 52.4 19.4
IE Ireland 83.0 87.3 82.3 67.3 48.4
IT Italy 90.4 93.9 92.4 73.4 75.8
LT Lithuania 93.6 88.3 93.9 89.1 89.4
LU Luxembourg 98.4 99.1 95.0 91.3 90.2
LV Latvia 72.6 82.3 80.1 95.3 NA
MT Malta NA 98.7 89.4 59.6 82.5
NL Netherlands 71.1 73.5 57.2 45.0 52.8
PL Poland 94.5 93.6 86.6 90.2 68.1
PT Portugal 95.7 95.9 90.9 74.8 56.0
RO Romania NA 96.6 85.9 67.3 68.8
SE Sweden NA 89.2 90.6 78.7 87.8
SI Slovenia 89.5 93.5 91.5 78.9 53.9
SK Slovakia 83.2 78.9 66.7 23.4 36.8
U2 Euro area (Member States and Institutions of the Euro Area) changing composition 79.2 81.5 69.9 60.5 65.6

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))