Risk Assessment Indicators

Data - ECB

Author

François Geerolf

Info

source dataset .html .RData
ecb RAI 2025-08-28 2025-08-28
  • Data Structure Definition. (DSD) html

Data on monetary policy

source dataset .html .RData
bdf FM 2025-08-28 2025-08-28
bdf MIR 2025-08-28 2025-08-04
bdf MIR1 2025-08-28 2025-08-04
bis CBPOL 2025-08-28 2025-08-28
ecb BSI 2025-08-29 NA
ecb BSI_PUB 2025-08-29 NA
ecb FM 2025-08-29 2025-08-28
ecb ILM 2025-08-29 NA
ecb ILM_PUB 2025-08-29 2024-09-10
ecb liq_daily 2025-08-29 2025-06-06
ecb MIR 2025-08-29 2025-08-28
ecb RAI 2025-08-28 2025-08-28
ecb SUP 2025-08-28 2025-08-28
ecb YC 2025-08-29 NA
ecb YC_PUB 2025-08-28 NA
eurostat ei_mfir_m 2025-08-28 2025-08-28
eurostat irt_st_m 2025-08-28 2025-08-28
fred r 2025-08-28 2025-08-28
oecd MEI 2024-04-16 2025-07-24
oecd MEI_FIN 2024-09-15 2025-07-24

Data on interest rates

source dataset .html .RData
bdf FM 2025-08-28 2025-08-28
bdf MIR 2025-08-28 2025-08-04
bdf MIR1 2025-08-28 2025-08-04
bis CBPOL_D 2025-08-28 2025-08-20
bis CBPOL_M 2025-08-28 2024-04-19
ecb FM 2025-08-29 2025-08-28
ecb MIR 2025-08-29 2025-08-28
eurostat ei_mfir_m 2025-08-28 2025-08-28
eurostat irt_lt_mcby_d 2025-08-28 2025-07-24
eurostat irt_st_m 2025-08-28 2025-08-28
fred r 2025-08-28 2025-08-28
oecd MEI 2024-04-16 2025-07-24
oecd MEI_FIN 2024-09-15 2025-07-24
wdi FR.INR.RINR 2025-05-24 2025-08-24

LAST_COMPILE

LAST_COMPILE
2025-08-29

Last

TIME_PERIOD FREQ Nobs
2025-Q1 Q 168
2025-06 M 370

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 8139
LMGOLNFCH Lending margin on outstanding loans to non-financial corporations and households 8116
IBL1TL Share of interbank loans in total loans 8089
LEVR Leverage ratio 8018
NDEPFUN Non-deposit funding 7946
CT1DGGV Share of other MFIs credit to domestic general government in total assets, excluding remaining assets 7739
LC1DHHS Share of other MFIs loans to domestic households for house purchase in total credit to other domestic residents 7662
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 7337
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 7334
LMGLHH MFIs lending margins on loans for house purchase 6841
LMGLNFC MFIs lending margins on loans to non-financial corporations (NFC) 6841
GRNLHHNFC Annual growth rate of MFIs new loans to households and non-financial corporations 6486
ST1TMF Share of short-term funding in total market funding 6177
MMTCH Maturity mismatch 5569
FXL1TL Share of other MFI FX loans in total loans (excluding inter-MFI loans) 2680
LTD Loans to deposits ratio 2633
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) 1861
OTHOFI4 Total assets of other financial institutions (OFIs) excluding financial vehicle corporations (FVCs), financial transactions (flows) 1859
SVLOAHH NA 1622
SVLOANFC NA 1622
IFOFI1 Total assets of MMF and non-MMF investment funds and other financial institutions (OFIs), outstanding amounts at the end of the period (stocks) 279
IFOFI4 Total assets of MMF and non-MMF investment funds and other financial institutions (OFIs), financial transactions (flows) 279
CRED1 Credit institutions (MFIs excluding the ESCB and MMFs), outstanding amounts at the end of the period (stocks) 186
CREDA Growth rate of total assets of credit institutions (MFIs excluding the ESCB and MMFs) 178
ICPFA Growth rate of total assets of insurance corporations and pension funds 178
IFOFIA Growth rate of total assets of MMF and non-MMF investment funds and other financial institutions (OFIs) 178

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 110882
E Euro 4464
P10 Currency ratio on total currency 2680

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 62298
MIR Based on MIR data 51094
QSA Based on quarterly sector accounts data 4456
ICPF Based on ICPF data 178

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 110882
E Euro 4464
P10 Currency ratio on total currency 2680

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 102294
Q Quarterly 15732

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 77.00690 78.1225112
ST1TMF 68.30008 76.7018564
SVLHHNFC 65.26886 38.5483064
LC1DHHS NA 36.8116844
IBL1TL 24.44481 36.4483399
NDEPFUN 14.90036 15.7785709
GRNLHHNFC NA 9.8080121
LEVR 8.25833 7.0578840
CT1DGGV NA 4.3923904
SVLHPHH 14.07156 3.2932879
LMGLNFC NA 1.4116778
LMGBLNFCH NA 1.2309992
LMGLHH NA 0.8377957
LMGOLNFCH NA 0.0752667

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 2025-06
AT Austria 65.7 76.2 87.3 40.6 20.2
BE Belgium 58.2 58.4 2.4 4.5 7.3
BG Bulgaria NA 96.2 84.0 97.3 99.7
CY Cyprus NA 61.7 95.1 90.7 19.8
CZ Czech Republic NA NA 7.2 2.1 NA
DE Germany 19.1 21.6 13.2 11.6 12.5
DK Denmark 70.1 49.2 NA NA 58.5
EE Estonia 99.3 56.7 85.4 NA 98.0
ES Spain 93.1 90.1 66.6 32.1 8.4
FI Finland 97.8 97.5 96.8 97.6 95.5
FR France 36.5 12.8 3.8 1.9 3.3
GR Greece 84.4 72.2 93.1 70.2 38.0
HR Croatia NA NA 84.9 20.5 1.6
HU Hungary 58.1 84.4 44.1 2.1 19.6
IE Ireland 93.6 84.1 66.0 25.8 19.5
IT Italy 87.3 81.3 71.7 18.0 8.8
LT Lithuania 97.9 84.7 89.8 97.8 95.5
LU Luxembourg 88.5 NA 63.9 30.1 NA
LV Latvia 72.3 84.4 NA 94.3 95.4
MT Malta NA 88.0 77.9 47.4 NA
NL Netherlands 43.2 24.9 19.9 17.4 15.5
PL Poland 90.9 100.0 99.9 100.0 25.7
PT Portugal 97.8 99.5 93.0 86.7 24.7
RO Romania NA NA 89.9 73.6 25.5
SE Sweden NA 85.6 85.5 64.2 85.6
SI Slovenia 99.1 98.1 93.4 55.5 1.1
SK Slovakia 62.4 36.0 6.4 1.1 2.2
U2 Euro area (Member States and Institutions of the Euro Area) changing composition 54.7 42.6 21.6 15.8 14.1

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 2025-06
AT Austria 91.7 92.3 89.7 70.5 68.1
BE Belgium 90.7 90.4 76.4 73.3 NA
BG Bulgaria NA 98.7 95.8 95.8 98.6
CY Cyprus NA 85.4 96.1 94.2 54.5
CZ Czech Republic 80.8 76.5 51.7 48.7 45.9
DE Germany 61.5 72.6 59.4 61.3 68.3
DK Denmark 76.4 64.5 43.6 50.2 73.2
EE Estonia 92.0 71.1 80.4 83.6 NA
ES Spain 91.5 91.4 88.1 70.8 71.9
FI Finland 94.3 NA NA 94.8 93.0
FR France 64.8 45.3 39.8 32.3 38.5
GR Greece 82.9 86.1 97.7 91.6 NA
HR Croatia NA NA 88.8 57.8 44.2
HU Hungary 95.3 94.5 75.0 52.4 43.2
IE Ireland 83.0 87.3 82.3 67.3 47.3
IT Italy 90.4 93.9 92.4 73.4 74.3
LT Lithuania 93.6 88.3 93.9 89.1 87.1
LU Luxembourg 98.4 99.1 95.0 91.3 82.2
LV Latvia 72.6 82.3 80.1 95.3 92.5
MT Malta NA 98.7 89.4 59.6 NA
NL Netherlands 71.1 73.5 57.2 45.0 54.4
PL Poland 94.5 93.6 86.6 90.2 65.6
PT Portugal 95.7 95.9 90.9 74.8 55.8
RO Romania NA 96.6 85.9 67.3 57.0
SE Sweden NA 89.2 90.6 78.7 87.5
SI Slovenia 89.5 93.5 91.5 78.9 60.0
SK Slovakia 83.2 78.9 66.7 23.4 32.7
U2 Euro area (Member States and Institutions of the Euro Area) changing composition 79.2 81.5 69.9 60.5 65.3

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))