Info

source	dataset	.html	.RData
eurostat	hbs_str_t221	2024-11-08	2024-10-08
insee	bdf2017	2024-11-09	2023-11-21
insee	if203	2024-11-09	2023-07-20

Data on housing

source	dataset	.html	.RData
bdf	RPP	2024-11-19	2024-11-19
bis	LONG_PP	2024-08-09	2024-05-10
bis	SELECTED_PP	2024-10-31	2024-10-31
ecb	RPP	2024-10-08	2024-10-30
eurostat	ei_hppi_q	2024-11-21	2024-11-21
eurostat	hbs_str_t223	2024-11-08	2024-11-21
eurostat	prc_hicp_midx	2024-11-01	2024-11-21
eurostat	prc_hpi_q	2024-11-05	2024-10-09
fred	housing	2024-11-21	2024-11-21
insee	IPLA-IPLNA-2015	2024-11-09	2024-11-09
oecd	housing	2024-09-15	2020-01-18
oecd	SNA_TABLE5	2024-09-11	2023-10-19

LAST_COMPILE

LAST_COMPILE
2024-11-22

Last

Code

hbs_str_t221 %>%
  group_by(time) %>%
  summarise(Nobs = n()) %>%
  arrange(desc(time)) %>%
  head(2) %>%
  print_table_conditional()

time	Nobs
2020	9785
2015	12108

coicop

All

Code

hbs_str_t221 %>%
  left_join(coicop, by = "coicop") %>%
  group_by(coicop, Coicop) %>%
  summarise(Nobs = n()) %>%
  print_table_conditional()

2-digit

Code

hbs_str_t221 %>%
  filter(nchar(coicop) == 4) %>%
  left_join(coicop, by = "coicop") %>%
  group_by(coicop, Coicop) %>%
  summarise(Nobs = n()) %>%
  print_table_conditional()

coicop	Coicop	Nobs
CP01	Food and non-alcoholic beverages	1119
CP02	Alcoholic beverages, tobacco and narcotics	1116
CP03	Clothing and footwear	1119
CP04	Housing, water, electricity, gas and other fuels	1119
CP05	Furnishings, household equipment and routine household maintenance	1119
CP06	Health	1116
CP07	Transport	1116
CP08	Communications	1119
CP09	Recreation and culture	1119
CP10	Education	1101
CP11	Restaurants and hotels	1116
CP12	Miscellaneous goods and services	1119

3-digit

Code

hbs_str_t221 %>%
  filter(nchar(coicop) == 5) %>%
  left_join(coicop, by = "coicop") %>%
  group_by(coicop, Coicop) %>%
  summarise(Nobs = n()) %>%
  print_table_conditional()

4-digit

Code

hbs_str_t221 %>%
  filter(nchar(coicop) == 6) %>%
  left_join(coicop, by = "coicop") %>%
  group_by(coicop, Coicop) %>%
  summarise(Nobs = n()) %>%
  print_table_conditional()

wstatus

Code

hbs_str_t221 %>%
  left_join(wstatus, by = "wstatus") %>%
  group_by(wstatus, Wstatus) %>%
  summarise(Nobs = n()) %>%
  print_table_conditional()

wstatus	Wstatus	Nobs
INAC_OTH	Other persons outside the labour force (former name: inactive persons)	10075
MW_IS	Manual workers in industry and services	10150
NMW_IS	Non-manual workers in industry and services	9986
NSAL	Employed persons except employees	10343
RET	Retired persons	10201
UNE	Unemployed persons	10021
UNK	Unknown	3859

geo

Code

hbs_str_t221 %>%
  left_join(geo, by = "geo") %>%
  group_by(geo, Geo) %>%
  summarise(Nobs = n()) %>%
  arrange(-Nobs) %>%
  mutate(Geo = ifelse(geo == "DE", "Germany", Geo)) %>%
  mutate(Flag = gsub(" ", "-", str_to_lower(Geo)),
         Flag = paste0('<img src="../../bib/flags/vsmall/', Flag, '.png" alt="Flag">')) %>%
  select(Flag, everything()) %>%
  {if (is_html_output()) datatable(., filter = 'top', rownames = F, escape = F) else .}

unit

Code

hbs_str_t221 %>%
  group_by(unit) %>%
  summarise(Nobs = n()) %>%
  print_table_conditional()

unit	Nobs
PM	64635

time

Code

hbs_str_t221 %>%
  group_by(time) %>%
  summarise(Nobs = n()) %>%
  print_table_conditional()

time	Nobs
1988	3310
1994	5529
1999	4870
2005	11381
2010	17652
2015	12108
2020	9785

France - Compare

2020, HBS

Code

hbs_str_t221 %>%
  filter(time == "2020",
         geo == "FR") %>%
  left_join(coicop, by = "coicop") %>%
  select_if(~ n_distinct(.) > 1) %>%
  spread(wstatus, values) %>%
  select_if(~ n_distinct(.) > 1) %>%
  print_table_conditional

2015, HBS

All

Code

hbs_str_t221 %>%
  filter(time == "2015",
         geo == "FR") %>%
  left_join(coicop, by = "coicop") %>%
  select_if(~ n_distinct(.) > 1) %>%
  spread(wstatus, values) %>%
  select_if(~ n_distinct(.) > 1) %>%
  print_table_conditional

2-digit

Code

hbs_str_t221 %>%
  filter(time == "2015",
         geo == "FR",
         nchar(coicop) == 4) %>%
  left_join(coicop, by = "coicop") %>%
  select_if(~ n_distinct(.) > 1) %>%
  spread(wstatus, values) %>%
  select_if(~ n_distinct(.) > 1) %>%
  print_table_conditional

coicop	Coicop	INAC_OTH	MW_IS	NMW_IS	NSAL	RET	UNE	UNK
CP01	Food and non-alcoholic beverages	154	145	126	125	168	152	124
CP02	Alcoholic beverages, tobacco and narcotics	39	32	22	24	23	32	23
CP03	Clothing and footwear	51	47	46	38	24	49	40
CP04	Housing, water, electricity, gas and other fuels	316	276	270	262	324	311	304
CP05	Furnishings, household equipment and routine household maintenance	39	41	47	47	56	38	45
CP06	Health	14	15	16	12	17	17	9
CP07	Transport	89	147	140	165	108	113	215
CP08	Communications	35	28	22	21	21	35	16
CP09	Recreation and culture	70	71	84	83	72	70	78
CP10	Education	24	5	8	10	1	5	3
CP11	Restaurants and hotels	52	50	72	70	29	46	46
CP12	Miscellaneous goods and services	115	143	147	142	156	133	98

All quintiles

Sums

2-digit

Code

hbs_str_t221 %>%
  filter(time == "2020",
         substr(coicop, 1, 2) == "CP",
         nchar(coicop) == 4) %>%
  left_join(geo, by = "geo") %>%
  select_if(~ n_distinct(.) > 1) %>%
  group_by(wstatus, geo, Geo) %>%
  summarise(values = sum(values)) %>%
  spread(wstatus, values) %>%
  print_table_conditional

geo	Geo	INAC_OTH	MW_IS	NMW_IS	NSAL	RET	UNE	UNK
AT	Austria	1000	1002	999	1000	1002	999	NA
BE	Belgium	1000	999	998	1000	998	999	NA
BG	Bulgaria	1000	1002	1000	999	999	1002	NA
CY	Cyprus	1001	1000	999	998	1000	1001	NA
CZ	Czechia	NA	NA	NA	NA	NA	NA	1000
DE	Germany	1000	1000	1000	999	1001	1000	NA
DK	Denmark	1001	999	1001	1000	1000	1000	1001
EE	Estonia	1002	1001	1001	1001	1000	1000	999
EL	Greece	1002	998	999	997	1000	999	NA
ES	Spain	1000	1001	1000	999	999	1002	NA
EU27_2020	European Union - 27 countries (from 2020)	1000	1001	1001	1001	1000	999	NA
FI	Finland	999	1000	1000	1000	1001	1001	NA
FR	France	998	1000	1000	999	999	1001	1001
HR	Croatia	1000	999	1000	999	999	999	NA
HU	Hungary	1001	1001	999	1002	1002	1000	1000
IT	Italy	999	1000	1000	1002	999	999	999
LT	Lithuania	1000	1001	1000	1001	1001	1000	NA
LU	Luxembourg	1001	1000	1001	999	1002	1000	NA
LV	Latvia	1000	999	1002	1002	1001	1000	NA
ME	Montenegro	1002	1001	999	999	999	1000	NA
MT	Malta	1000	1000	999	999	1000	998	1001
NL	Netherlands	999	NA	NA	1001	1000	1000	999
PL	Poland	1001	1000	998	1001	1001	1000	1000
PT	Portugal	NA	NA	NA	NA	NA	NA	1000
RO	Romania	1001	1000	998	999	1001	999	999
RS	Serbia	1000	1000	1002	1000	999	999	NA
SI	Slovenia	1001	1001	1001	1000	1000	998	1000
SK	Slovakia	1000	1002	1001	999	1000	1000	NA
TR	Türkiye	1001	999	1001	1001	1000	1001	1001

3-digit

Code

hbs_str_t221 %>%
  filter(time == "2020",
         substr(coicop, 1, 2) == "CP",
         nchar(coicop) == 5) %>%
  left_join(geo, by = "geo") %>%
  select_if(~ n_distinct(.) > 1) %>%
  group_by(wstatus, geo, Geo) %>%
  summarise(values = sum(values)) %>%
  spread(wstatus, values) %>%
  print_table_conditional

geo	Geo	INAC_OTH	MW_IS	NMW_IS	NSAL	RET	UNE	UNK
AT	Austria	1000	999	1000	1002	998	1004	NA
BE	Belgium	1001	998	998	1002	1000	998	NA
BG	Bulgaria	998	999	1001	1000	998	996	NA
CY	Cyprus	994	998	998	1000	993	996	NA
CZ	Czechia	NA	NA	NA	NA	NA	NA	1000
DE	Germany	978	988	992	988	993	999	NA
DK	Denmark	998	1002	1003	1002	999	1002	1003
EE	Estonia	984	986	989	988	984	983	978
EL	Greece	999	999	1000	1000	999	995	NA
ES	Spain	998	998	1000	1001	1000	1000	NA
FI	Finland	979	986	970	973	990	981	NA
FR	France	994	995	988	982	990	1000	998
HR	Croatia	1001	1000	1003	999	995	998	NA
HU	Hungary	1003	998	997	1003	1001	1000	1001
IT	Italy	996	1000	999	1001	998	1003	1002
LT	Lithuania	999	1001	1000	1003	999	997	NA
LU	Luxembourg	1002	1001	1001	998	997	1000	NA
LV	Latvia	998	1002	1001	997	1001	1000	NA
ME	Montenegro	989	981	987	989	982	981	NA
MT	Malta	1003	999	1001	1000	999	999	998
NL	Netherlands	998	NA	NA	1000	999	999	1002
PL	Poland	1000	997	996	996	996	1001	999
RS	Serbia	1002	996	999	1002	999	1000	NA
SI	Slovenia	999	998	1000	999	1002	996	999
SK	Slovakia	998	1000	1001	998	998	999	NA
TR	Türkiye	998	1002	999	1002	1001	997	1002

CP041, CP042, CP041_042

2015

France

Code

hbs_str_t221 %>%
  filter(coicop %in% c("CP041", "CP042"),
         time == "2015",
         geo %in% c("FR")) %>%
  spread(coicop, values) %>%
  mutate(CP041_042 = CP041 + CP042) %>%
  gather(coicop, values, CP041, CP042, CP041_042) %>%
  left_join(wstatus, by = "wstatus") %>%
  left_join(coicop, by = "coicop") %>%
  mutate(Coicop = ifelse(coicop == "CP041_042", "Imputed rentals plus actual rentals", Coicop),
         wstatus = ifelse(wstatus == "Y_GE60", "Y60+", wstatus),
         wstatus = ifelse(wstatus == "Y_LT30", "Y30-", wstatus)) %>%
  ggplot + geom_line(aes(x = Wstatus, y = values/1000, color = Coicop, group = Coicop)) +
  theme_minimal() +
  xlab("") + ylab("Weight in CPI") +
  scale_y_continuous(breaks = 0.01*seq(-30, 50, 2),
                     labels = percent_format(accuracy = 1)) +
  theme(legend.position = c(0.2, 0.9),
        legend.title = element_blank(),
        axis.text.x = element_text(angle = 45, vjust = 1, hjust = 1))