Risk Assessment Indicators

Data - ECB

Author

François Geerolf

Info

source dataset .html .RData

ecb

RAI

 2024-09-19 2024-10-08
  • Data Structure Definition. (DSD) html

Data on monetary policy

source dataset .html .RData

bdf

FM

 2024-07-26 2024-06-18

bdf

MIR

 2024-07-26 2024-07-01

bdf

MIR1

 2024-07-26 2024-07-01

bis

CBPOL

 2024-08-09 2024-09-15

ecb

BSI

 2024-10-08 2024-09-16

ecb

BSI_PUB

 2024-10-08 2024-10-08

ecb

FM

 2024-10-08 2024-10-08

ecb

ILM

 2024-10-08 2024-10-08

ecb

ILM_PUB

 2024-10-08 2024-09-10

ecb

liq_daily

 2024-10-08 2024-09-11

ecb

MIR

 2024-06-19 2024-10-08

ecb

RAI

 2024-09-19 2024-10-08

ecb

SUP

 2024-09-19 2024-10-08

ecb

YC

 2024-09-19 2024-09-16

ecb

YC_PUB

 2024-09-19 2024-10-08

eurostat

ei_mfir_m

 2024-09-30 2024-09-15

eurostat

irt_st_m

 2024-09-30 2024-10-08

fred

r

 2024-09-18 2024-09-18

oecd

MEI

 2024-04-16 2024-06-30

oecd

MEI_FIN

 2024-09-15 2024-05-21

Data on interest rates

source dataset .html .RData

bdf

FM

 2024-07-26 2024-06-18

bdf

MIR

 2024-07-26 2024-07-01

bdf

MIR1

 2024-07-26 2024-07-01

bis

CBPOL_D

 2024-09-13 2024-05-10

bis

CBPOL_M

 2024-08-09 2024-04-19

ecb

FM

 2024-10-08 2024-10-08

ecb

MIR

 2024-06-19 2024-10-08

eurostat

ei_mfir_m

 2024-09-30 2024-09-15

eurostat

irt_lt_mcby_d

 2024-09-30 2024-08-28

eurostat

irt_st_m

 2024-09-30 2024-10-08

fred

r

 2024-09-18 2024-09-18

oecd

MEI

 2024-04-16 2024-06-30

oecd

MEI_FIN

 2024-09-15 2024-05-21

wdi

FR.INR.RINR

 2024-08-28 2024-09-18

LAST_COMPILE

LAST_COMPILE
2024-10-09

Last

TIME_PERIOD FREQ Nobs
2024-Q2 Q 116
2024-08 M 363

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 7869
LMGOLNFCH Lending margin on outstanding loans to non-financial corporations and households 7846
IBL1TL Share of interbank loans in total loans 7809
LEVR Leverage ratio 7738
NDEPFUN Non-deposit funding 7666
CT1DGGV Share of other MFIs credit to domestic general government in total assets, excluding remaining assets 7469
LC1DHHS Share of other MFIs loans to domestic households for house purchase in total credit to other domestic residents 7392
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 6931
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 6664
LMGLHH MFIs lending margins on loans for house purchase 6571
LMGLNFC MFIs lending margins on loans to non-financial corporations (NFC) 6571
ST1TMF Share of short-term funding in total market funding 5967
GRNLHHNFC Annual growth rate of MFIs new loans to households and non-financial corporations 5881
MMTCH Maturity mismatch 5359
FXL1TL Share of other MFI FX loans in total loans (excluding inter-MFI loans) 2596
LTD Loans to deposits ratio 2549
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) 1777
OTHOFI4 Total assets of other financial institutions (OFIs) excluding financial vehicle corporations (FVCs), financial transactions (flows) 1775
SVLOAHH NA 1538
SVLOANFC NA 1538
IFOFI1 Total assets of MMF and non-MMF investment funds and other financial institutions (OFIs), outstanding amounts at the end of the period (stocks) 267
IFOFI4 Total assets of MMF and non-MMF investment funds and other financial institutions (OFIs), financial transactions (flows) 267
CRED1 Credit institutions (MFIs excluding the ESCB and MMFs), outstanding amounts at the end of the period (stocks) 180
CREDA Growth rate of total assets of credit institutions (MFIs excluding the ESCB and MMFs) 172
ICPFA Growth rate of total assets of insurance corporations and pension funds 170
IFOFIA Growth rate of total assets of MMF and non-MMF investment funds and other financial institutions (OFIs) 170

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 106047
E Euro 4266
P10 Currency ratio on total currency 2596

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 60150
MIR Based on MIR data 48333
QSA Based on quarterly sector accounts data 4256
ICPF Based on ICPF data 170

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 106047
E Euro 4266
P10 Currency ratio on total currency 2596

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 97733
Q Quarterly 15176

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 76.463227 77.9469913
ST1TMF 67.363325 76.4852145
LC1DHHS NA 37.6261300
SVLHHNFC 62.775948 37.5358524
IBL1TL 23.262852 35.4020015
NDEPFUN 15.169760 16.0111920
LEVR 8.268894 7.0421038
SVLHPHH 15.072366 4.1154210
CT1DGGV NA 4.0975728
LMGLNFC NA 1.0462225
LMGBLNFCH NA 0.5851805
LMGLHH NA -0.2255325
LMGOLNFCH NA -0.4393522
GRNLHHNFC NA -10.1965723

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 2024-08
AT Austria 65.7 76.2 87.3 40.6 24.4
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 46.9
CZ Czech Republic NA NA 7.2 2.1 NA
DE Germany 19.1 21.6 13.2 11.6 10.8
DK Denmark 70.1 49.2 NA NA NA
EE Estonia 99.3 56.7 85.4 NA 97.4
ES Spain 93.1 90.1 66.6 32.1 9.5
FI Finland 97.8 97.5 96.8 97.6 96.1
FR France 36.5 12.8 3.8 1.9 4.1
GR Greece 84.4 72.2 93.1 70.2 26.3
HR Croatia NA NA 84.9 20.5 3.9
HU Hungary 58.1 84.4 44.1 2.1 25.4
IE Ireland 93.6 84.1 66.0 25.8 33.9
IT Italy 87.3 81.3 71.7 18.0 15.4
LT Lithuania 97.9 84.7 89.8 97.8 96.8
LU Luxembourg 88.5 NA 63.9 30.1 NA
LV Latvia 72.3 84.4 NA 94.3 91.1
MT Malta NA 88.0 77.9 47.4 NA
NL Netherlands 43.2 24.9 19.9 17.4 14.9
PL Poland 90.9 100.0 99.9 100.0 25.0
PT Portugal 97.8 99.5 93.0 56.7 22.5
RO Romania NA NA 89.9 73.6 28.5
SE Sweden NA 85.6 85.5 64.2 84.9
SI Slovenia 99.1 98.1 93.4 55.5 2.0
SK Slovakia 62.4 36.0 6.4 1.1 3.3
U2 Euro area (Member States and Institutions of the Euro Area) changing composition 54.7 42.6 21.6 15.5 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 2024-08
AT Austria 91.7 92.3 89.7 70.5 63.6
BG Bulgaria NA 98.7 95.8 95.8 98.3
CY Cyprus NA 85.4 96.1 94.2 83.1
CZ Czech Republic 80.8 76.5 51.7 48.7 39.6
DE Germany 61.5 72.6 59.4 61.3 62.6
DK Denmark 76.4 64.5 43.6 50.2 65.9
EE Estonia 92.0 71.1 80.4 83.6 NA
ES Spain 91.5 91.4 88.1 70.8 74.8
FI Finland 94.3 NA NA 94.8 NA
FR France 64.8 45.3 39.8 32.3 37.5
GR Greece 82.9 86.1 97.7 91.6 NA
HR Croatia NA NA 88.8 57.8 52.3
HU Hungary 95.3 94.5 75.0 52.4 50.4
IE Ireland 83.0 87.3 82.3 67.3 65.0
IT Italy 90.4 93.9 92.4 73.4 77.8
LT Lithuania 93.6 88.3 93.9 89.1 88.7
LU Luxembourg 98.4 99.1 95.0 91.3 85.7
LV Latvia 72.6 82.3 80.1 95.3 NA
MT Malta NA 98.7 89.4 59.6 64.5
NL Netherlands 71.1 73.5 57.2 45.0 53.1
PL Poland 94.5 93.6 86.6 90.2 70.6
PT Portugal 95.7 95.9 90.9 73.9 43.0
RO Romania NA 96.6 85.9 67.3 57.4
SE Sweden NA 89.2 90.6 78.7 87.1
SI Slovenia 89.5 93.5 91.5 78.9 53.8
SK Slovakia 83.2 78.9 66.7 23.4 28.7
U2 Euro area (Member States and Institutions of the Euro Area) changing composition 79.2 81.5 69.9 60.5 62.8

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))