Risk Assessment Indicators

Data - ECB

Author

François Geerolf

Info

source dataset .html .RData
ecb RAI 2024-12-29 2025-05-18
  • Data Structure Definition. (DSD) html

Data on monetary policy

source dataset .html .RData
bdf FM 2025-03-27 2025-03-27
bdf MIR 2025-03-09 2025-01-22
bdf MIR1 2025-03-09 2025-01-22
bis CBPOL 2024-12-29 2024-12-29
ecb BSI 2025-05-18 2024-11-19
ecb BSI_PUB 2025-05-18 2025-05-18
ecb FM 2025-05-18 2025-05-18
ecb ILM 2025-05-18 2025-05-18
ecb ILM_PUB 2025-05-18 2024-09-10
ecb liq_daily 2025-05-18 2024-09-11
ecb MIR 2024-06-19 2025-05-18
ecb RAI 2024-12-29 2025-05-18
ecb SUP 2024-12-29 2025-05-18
ecb YC 2024-12-29 2024-11-19
ecb YC_PUB 2024-12-29 2025-05-18
eurostat ei_mfir_m 2025-04-28 2025-04-28
eurostat irt_st_m 2025-02-07 2025-05-18
fred r 2025-05-18 2025-05-18
oecd MEI 2024-04-16 2025-02-25
oecd MEI_FIN 2024-09-15 2025-02-25

Data on interest rates

source dataset .html .RData
bdf FM 2025-03-27 2025-03-27
bdf MIR 2025-03-09 2025-01-22
bdf MIR1 2025-03-09 2025-01-22
bis CBPOL_D 2025-02-07 2024-05-10
bis CBPOL_M 2025-02-07 2024-04-19
ecb FM 2025-05-18 2025-05-18
ecb MIR 2024-06-19 2025-05-18
eurostat ei_mfir_m 2025-04-28 2025-04-28
eurostat irt_lt_mcby_d 2025-02-25 2025-02-25
eurostat irt_st_m 2025-02-07 2025-05-18
fred r 2025-05-18 2025-05-18
oecd MEI 2024-04-16 2025-02-25
oecd MEI_FIN 2024-09-15 2025-02-25
wdi FR.INR.RINR 2025-02-07 2025-03-09

LAST_COMPILE

LAST_COMPILE
2025-05-18

Last

TIME_PERIOD FREQ Nobs
2025-Q1 Q 6
2025-03 M 369

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 8058
LMGOLNFCH Lending margin on outstanding loans to non-financial corporations and households 8035
IBL1TL Share of interbank loans in total loans 8005
LEVR Leverage ratio 7934
NDEPFUN Non-deposit funding 7862
CT1DGGV Share of other MFIs credit to domestic general government in total assets, excluding remaining assets 7658
LC1DHHS Share of other MFIs loans to domestic households for house purchase in total credit to other domestic residents 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 7125
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 7116
LMGLHH MFIs lending margins on loans for house purchase 6760
LMGLNFC MFIs lending margins on loans to non-financial corporations (NFC) 6760
ST1TMF Share of short-term funding in total market funding 6114
GRNLHHNFC Annual growth rate of MFIs new loans to households and non-financial corporations 6051
MMTCH Maturity mismatch 5506
FXL1TL Share of other MFI FX loans in total loans (excluding inter-MFI loans) 2652
LTD Loans to deposits ratio 2605
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) 1840
OTHOFI4 Total assets of other financial institutions (OFIs) excluding financial vehicle corporations (FVCs), financial transactions (flows) 1838
SVLOAHH NA 1597
SVLOANFC NA 1597
IFOFI1 Total assets of MMF and non-MMF investment funds and other financial institutions (OFIs), outstanding amounts at the end of the period (stocks) 276
IFOFI4 Total assets of MMF and non-MMF investment funds and other financial institutions (OFIs), financial transactions (flows) 276
CRED1 Credit institutions (MFIs excluding the ESCB and MMFs), outstanding amounts at the end of the period (stocks) 184
CREDA Growth rate of total assets of credit institutions (MFIs excluding the ESCB and MMFs) 176
ICPFA Growth rate of total assets of insurance corporations and pension funds 176
IFOFIA Growth rate of total assets of MMF and non-MMF investment funds and other financial institutions (OFIs) 176

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 109069
E Euro 4414
P10 Currency ratio on total currency 2652

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 61648
MIR Based on MIR data 49905
QSA Based on quarterly sector accounts data 4406
ICPF Based on ICPF data 176

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 109069
E Euro 4414
P10 Currency ratio on total currency 2652

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 100565
Q Quarterly 15570

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 76.785998 78.1483876
ST1TMF 67.891253 76.5139946
SVLHHNFC 64.374621 39.7477446
LC1DHHS NA 37.1297264
IBL1TL 23.671519 35.6165938
NDEPFUN 15.170356 16.0351466
GRNLHHNFC NA 7.4615475
LEVR 8.317707 7.1065194
CT1DGGV NA 4.3151094
SVLHPHH 13.774941 3.0640669
LMGLNFC NA 1.3044652
LMGBLNFCH NA 1.0753026
LMGLHH NA 0.5237180
LMGOLNFCH NA -0.0761804

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 2025-03
AT Austria 65.7 76.2 87.3 40.6 20.3
BE Belgium 58.2 58.4 2.4 4.5 6.5
BG Bulgaria NA 96.2 84.0 97.3 99.6
CY Cyprus NA 61.7 95.1 90.7 29.8
CZ Czech Republic NA NA 7.2 2.1 13.4
DE Germany 19.1 21.6 13.2 11.6 11.3
DK Denmark 70.1 49.2 NA NA NA
EE Estonia 99.3 56.7 85.4 NA 97.9
ES Spain 93.1 90.1 66.6 32.1 8.5
FI Finland 97.8 97.5 96.8 97.6 95.3
FR France 36.5 12.8 3.8 1.9 3.1
GR Greece 84.4 72.2 93.1 70.2 22.6
HR Croatia NA NA 84.9 20.5 2.9
HU Hungary 58.1 84.4 44.1 2.1 19.6
IE Ireland 93.6 84.1 66.0 25.8 26.5
IT Italy 87.3 81.3 71.7 18.0 7.4
LT Lithuania 97.9 84.7 89.8 97.8 98.3
LU Luxembourg 88.5 NA 63.9 30.1 36.5
LV Latvia 72.3 84.4 NA 94.3 97.0
MT Malta NA 88.0 77.9 47.4 NA
NL Netherlands 43.2 24.9 19.9 17.4 14.2
PL Poland 90.9 100.0 99.9 100.0 20.1
PT Portugal 97.8 99.5 93.0 86.7 24.9
RO Romania NA NA 89.9 73.6 28.7
SE Sweden NA 85.6 85.5 64.2 64.8
SI Slovenia 99.1 98.1 93.4 55.5 1.8
SK Slovakia 62.4 36.0 6.4 1.1 2.3
U2 Euro area (Member States and Institutions of the Euro Area) changing composition 54.7 42.6 21.6 15.8 13.8

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 2025-03
AT Austria 91.7 92.3 89.7 70.5 64.1
BE Belgium 90.7 90.4 76.4 73.3 74.6
BG Bulgaria NA 98.7 95.8 95.8 98.2
CY Cyprus NA 85.4 96.1 94.2 50.9
CZ Czech Republic 80.8 76.5 51.7 48.7 44.5
DE Germany 61.5 72.6 59.4 61.3 68.5
DK Denmark 76.4 64.5 43.6 50.2 63.3
EE Estonia 92.0 71.1 80.4 83.6 95.1
ES Spain 91.5 91.4 88.1 70.8 71.5
FI Finland 94.3 NA NA 94.8 92.8
FR France 64.8 45.3 39.8 32.3 39.7
GR Greece 82.9 86.1 97.7 91.6 NA
HR Croatia NA NA 88.8 57.8 46.4
HU Hungary 95.3 94.5 75.0 52.4 46.7
IE Ireland 83.0 87.3 82.3 67.3 63.5
IT Italy 90.4 93.9 92.4 73.4 69.9
LT Lithuania 93.6 88.3 93.9 89.1 89.1
LU Luxembourg 98.4 99.1 95.0 91.3 80.3
LV Latvia 72.6 82.3 80.1 95.3 NA
MT Malta NA 98.7 89.4 59.6 NA
NL Netherlands 71.1 73.5 57.2 45.0 52.2
PL Poland 94.5 93.6 86.6 90.2 66.7
PT Portugal 95.7 95.9 90.9 74.8 50.2
RO Romania NA 96.6 85.9 67.3 55.1
SE Sweden NA 89.2 90.6 78.7 73.0
SI Slovenia 89.5 93.5 91.5 78.9 52.1
SK Slovakia 83.2 78.9 66.7 23.4 26.3
U2 Euro area (Member States and Institutions of the Euro Area) changing composition 79.2 81.5 69.9 60.5 64.4

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))