Risk Assessment Indicators

Data - ECB

Author

François Geerolf

Info

source dataset .html .RData
ecb RAI 2024-12-28 2024-12-29
  • Data Structure Definition. (DSD) html

Data on monetary policy

source dataset .html .RData
bdf FM 2024-12-28 2024-12-28
bdf MIR 2024-07-26 2024-07-01
bdf MIR1 2024-11-29 2024-12-09
bis CBPOL 2024-12-19 2024-12-29
ecb BSI 2024-12-29 2024-11-19
ecb BSI_PUB 2024-12-29 2024-12-29
ecb FM 2024-12-29 2024-12-29
ecb ILM 2024-12-29 2024-12-29
ecb ILM_PUB 2024-12-29 2024-09-10
ecb liq_daily 2024-12-29 2024-09-11
ecb MIR 2024-06-19 2024-12-29
ecb RAI 2024-12-28 2024-12-29
ecb SUP 2024-12-28 2024-12-29
ecb YC 2024-12-28 2024-11-19
ecb YC_PUB 2024-12-28 2024-12-29
eurostat ei_mfir_m 2024-12-28 2024-12-28
eurostat irt_st_m 2024-12-28 2024-12-29
fred r 2024-12-29 2024-12-29
oecd MEI 2024-04-16 2024-06-30
oecd MEI_FIN 2024-09-15 2024-12-22

Data on interest rates

source dataset .html .RData
bdf FM 2024-12-28 2024-12-28
bdf MIR 2024-07-26 2024-07-01
bdf MIR1 2024-11-29 2024-12-09
bis CBPOL_D 2024-12-28 2024-05-10
bis CBPOL_M 2024-12-28 2024-04-19
ecb FM 2024-12-29 2024-12-29
ecb MIR 2024-06-19 2024-12-29
eurostat ei_mfir_m 2024-12-28 2024-12-28
eurostat irt_lt_mcby_d 2024-12-28 2024-12-28
eurostat irt_st_m 2024-12-28 2024-12-29
fred r 2024-12-29 2024-12-29
oecd MEI 2024-04-16 2024-06-30
oecd MEI_FIN 2024-09-15 2024-12-22
wdi FR.INR.RINR 2024-12-28 2024-12-28

LAST_COMPILE

LAST_COMPILE
2024-12-29

Last

TIME_PERIOD FREQ Nobs
2024-Q3 Q 116
2024-10 M 366

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 7923
LMGOLNFCH Lending margin on outstanding loans to non-financial corporations and households 7900
IBL1TL Share of interbank loans in total loans 7865
LEVR Leverage ratio 7794
NDEPFUN Non-deposit funding 7722
CT1DGGV Share of other MFIs credit to domestic general government in total assets, excluding remaining assets 7523
LC1DHHS Share of other MFIs loans to domestic households for house purchase in total credit to other domestic residents 7446
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 6987
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 6718
LMGLHH MFIs lending margins on loans for house purchase 6625
LMGLNFC MFIs lending margins on loans to non-financial corporations (NFC) 6625
ST1TMF Share of short-term funding in total market funding 6009
GRNLHHNFC Annual growth rate of MFIs new loans to households and non-financial corporations 5931
MMTCH Maturity mismatch 5401
FXL1TL Share of other MFI FX loans in total loans (excluding inter-MFI loans) 2624
LTD Loans to deposits ratio 2577
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) 1798
OTHOFI4 Total assets of other financial institutions (OFIs) excluding financial vehicle corporations (FVCs), financial transactions (flows) 1796
SVLOAHH NA 1566
SVLOANFC NA 1566
IFOFI1 Total assets of MMF and non-MMF investment funds and other financial institutions (OFIs), outstanding amounts at the end of the period (stocks) 270
IFOFI4 Total assets of MMF and non-MMF investment funds and other financial institutions (OFIs), financial transactions (flows) 270
CRED1 Credit institutions (MFIs excluding the ESCB and MMFs), outstanding amounts at the end of the period (stocks) 182
CREDA Growth rate of total assets of credit institutions (MFIs excluding the ESCB and MMFs) 174
ICPFA Growth rate of total assets of insurance corporations and pension funds 172
IFOFIA Growth rate of total assets of MMF and non-MMF investment funds and other financial institutions (OFIs) 172

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 106873
E Euro 4316
P10 Currency ratio on total currency 2624

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 60626
MIR Based on MIR data 48709
QSA Based on quarterly sector accounts data 4306
ICPF Based on ICPF data 172

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 106873
E Euro 4316
P10 Currency ratio on total currency 2624

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 98469
Q Quarterly 15344

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 76.280995 77.4975287
ST1TMF 67.183436 76.0626582
SVLHHNFC 63.355829 41.2072859
LC1DHHS NA 37.2657108
IBL1TL 23.335726 35.7373371
NDEPFUN 15.310631 16.2601517
LEVR 8.314453 7.0913574
SVLHPHH 14.613695 4.2515295
CT1DGGV NA 4.1117422
LMGLNFC NA 1.1712065
LMGBLNFCH NA 0.8567785
LMGLHH NA 0.0401372
LMGOLNFCH NA -0.3805586
GRNLHHNFC NA -6.8548724

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 2024-10
AT Austria 65.7 76.2 87.3 40.6 16.7
BE Belgium 58.2 58.4 2.4 4.5 7.1
BG Bulgaria NA 96.2 84.0 97.3 99.5
CY Cyprus NA 61.7 95.1 90.7 37.1
CZ Czech Republic NA NA 7.2 2.1 NA
DE Germany 19.1 21.6 13.2 11.6 11.1
DK Denmark 70.1 49.2 NA NA NA
EE Estonia 99.3 56.7 85.4 NA 96.3
ES Spain 93.1 90.1 66.6 32.1 8.9
FI Finland 97.8 97.5 96.8 97.6 96.3
FR France 36.5 12.8 3.8 1.9 4.3
GR Greece 84.4 72.2 93.1 70.2 26.0
HR Croatia NA NA 84.9 20.5 3.5
HU Hungary 58.1 84.4 44.1 2.1 24.5
IE Ireland 93.6 84.1 66.0 25.8 26.1
IT Italy 87.3 81.3 71.7 18.0 6.5
LT Lithuania 97.9 84.7 89.8 97.8 97.8
LU Luxembourg 88.5 NA 63.9 30.1 35.1
LV Latvia 72.3 84.4 NA 94.3 94.3
MT Malta NA 88.0 77.9 47.4 NA
NL Netherlands 43.2 24.9 19.9 17.4 16.4
PL Poland 90.9 100.0 99.9 100.0 14.8
PT Portugal 97.8 99.5 93.0 86.7 21.3
RO Romania NA NA 89.9 73.6 26.9
SE Sweden NA 85.6 85.5 64.2 76.4
SI Slovenia 99.1 98.1 93.4 55.5 2.0
SK Slovakia 62.4 36.0 6.4 1.1 2.9
U2 Euro area (Member States and Institutions of the Euro Area) changing composition 54.7 42.6 21.6 15.8 14.6

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 2024-10
AT Austria 91.7 92.3 89.7 70.5 65.7
BG Bulgaria NA 98.7 95.8 95.8 98.4
CY Cyprus NA 85.4 96.1 94.2 63.9
CZ Czech Republic 80.8 76.5 51.7 48.7 39.6
DE Germany 61.5 72.6 59.4 61.3 66.4
DK Denmark 76.4 64.5 43.6 50.2 63.2
EE Estonia 92.0 71.1 80.4 83.6 NA
ES Spain 91.5 91.4 88.1 70.8 72.8
FI Finland 94.3 NA NA 94.8 93.2
FR France 64.8 45.3 39.8 32.3 41.2
GR Greece 82.9 86.1 97.7 91.6 89.4
HR Croatia NA NA 88.8 57.8 51.2
HU Hungary 95.3 94.5 75.0 52.4 47.0
IE Ireland 83.0 87.3 82.3 67.3 55.4
IT Italy 90.4 93.9 92.4 73.4 68.9
LT Lithuania 93.6 88.3 93.9 89.1 85.3
LU Luxembourg 98.4 99.1 95.0 91.3 77.2
LV Latvia 72.6 82.3 80.1 95.3 90.2
MT Malta NA 98.7 89.4 59.6 68.4
NL Netherlands 71.1 73.5 57.2 45.0 51.7
PL Poland 94.5 93.6 86.6 90.2 68.1
PT Portugal 95.7 95.9 90.9 74.8 47.3
RO Romania NA 96.6 85.9 67.3 56.4
SE Sweden NA 89.2 90.6 78.7 78.3
SI Slovenia 89.5 93.5 91.5 78.9 55.1
SK Slovakia 83.2 78.9 66.7 23.4 32.2
U2 Euro area (Member States and Institutions of the Euro Area) changing composition 79.2 81.5 69.9 60.5 63.4

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))