Structure of consumption expenditure by age of the reference person and COICOP consumption purpose
Data - Eurostat
Info
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_t225 group_by(time) %>%
summarise(Nobs = n()) %>%
arrange(desc(time)) %>%
head(2) %>%
print_table_conditional()
time | Nobs |
---|---|
2020 | 6634 |
2015 | 8072 |
coicop
All
Code
%>%
hbs_str_t225 left_join(coicop, by = "coicop") %>%
group_by(coicop, Coicop) %>%
summarise(Nobs = n()) %>%
print_table_conditional()
2-digit
Code
%>%
hbs_str_t225 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 | 735 |
CP02 | Alcoholic beverages, tobacco and narcotics | 735 |
CP03 | Clothing and footwear | 735 |
CP04 | Housing, water, electricity, gas and other fuels | 735 |
CP05 | Furnishings, household equipment and routine household maintenance | 735 |
CP06 | Health | 735 |
CP07 | Transport | 735 |
CP08 | Communications | 735 |
CP09 | Recreation and culture | 735 |
CP10 | Education | 728 |
CP11 | Restaurants and hotels | 735 |
CP12 | Miscellaneous goods and services | 735 |
3-digit
Code
%>%
hbs_str_t225 filter(nchar(coicop) == 5) %>%
left_join(coicop, by = "coicop") %>%
group_by(coicop, Coicop) %>%
summarise(Nobs = n()) %>%
print_table_conditional()
4-digit
Code
%>%
hbs_str_t225 filter(nchar(coicop) == 6) %>%
left_join(coicop, by = "coicop") %>%
group_by(coicop, Coicop) %>%
summarise(Nobs = n()) %>%
print_table_conditional()
age
Code
%>%
hbs_str_t225 left_join(age, by = "age") %>%
group_by(age, Age) %>%
summarise(Nobs = n()) %>%
print_table_conditional()
age | Age | Nobs |
---|---|---|
UNK | Unknown | 174 |
Y30-44 | From 30 to 44 years | 10571 |
Y45-59 | From 45 to 59 years | 10574 |
Y_GE60 | 60 years or over | 10546 |
Y_LT30 | Less than 30 years | 10525 |
geo
Code
%>%
hbs_str_t225 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_t225 group_by(unit) %>%
summarise(Nobs = n()) %>%
print_table_conditional()
unit | Nobs |
---|---|
PM | 42390 |
time
Code
%>%
hbs_str_t225 group_by(time) %>%
summarise(Nobs = n()) %>%
print_table_conditional()
time | Nobs |
---|---|
1988 | 2168 |
1994 | 3904 |
1999 | 3554 |
2005 | 6550 |
2010 | 11508 |
2015 | 8072 |
2020 | 6634 |
France - Compare
2020, HBS
Code
%>%
hbs_str_t225 filter(time == "2020",
== "FR") %>%
geo left_join(coicop, by = "coicop") %>%
select_if(~ n_distinct(.) > 1) %>%
spread(age, values) %>%
select_if(~ n_distinct(.) > 1) %>%
print_table_conditional
2015, HBS
All
Code
%>%
hbs_str_t225 filter(time == "2015",
== "FR") %>%
geo left_join(coicop, by = "coicop") %>%
select_if(~ n_distinct(.) > 1) %>%
spread(age, values) %>%
select_if(~ n_distinct(.) > 1) %>%
print_table_conditional
2-digit
Code
%>%
hbs_str_t225 filter(time == "2015",
== "FR",
geo nchar(coicop) == 4) %>%
left_join(coicop, by = "coicop") %>%
select_if(~ n_distinct(.) > 1) %>%
spread(age, values) %>%
select_if(~ n_distinct(.) > 1) %>%
print_table_conditional
coicop | Coicop | Y_GE60 | Y_LT30 | Y30-44 | Y45-59 |
---|---|---|---|---|---|
CP01 | Food and non-alcoholic beverages | 165 | 98 | 127 | 146 |
CP02 | Alcoholic beverages, tobacco and narcotics | 24 | 26 | 24 | 27 |
CP03 | Clothing and footwear | 25 | 54 | 50 | 42 |
CP04 | Housing, water, electricity, gas and other fuels | 325 | 265 | 270 | 275 |
CP05 | Furnishings, household equipment and routine household maintenance | 55 | 40 | 45 | 45 |
CP06 | Health | 17 | 14 | 15 | 16 |
CP07 | Transport | 111 | 155 | 136 | 144 |
CP08 | Communications | 21 | 30 | 24 | 24 |
CP09 | Recreation and culture | 72 | 96 | 78 | 77 |
CP10 | Education | 1 | 10 | 5 | 11 |
CP11 | Restaurants and hotels | 30 | 72 | 71 | 61 |
CP12 | Miscellaneous goods and services | 154 | 142 | 156 | 133 |
All quintiles
Sums
2-digit
Code
%>%
hbs_str_t225 filter(time == "2020",
substr(coicop, 1, 2) == "CP",
nchar(coicop) == 4) %>%
left_join(geo, by = "geo") %>%
select_if(~ n_distinct(.) > 1) %>%
group_by(age, geo, Geo) %>%
summarise(values = sum(values)) %>%
spread(age, values) %>%
print_table_conditional
geo | Geo | UNK | Y_GE60 | Y_LT30 | Y30-44 | Y45-59 |
---|---|---|---|---|---|---|
AT | Austria | NA | 1000 | 999 | 999 | 998 |
BE | Belgium | NA | 1001 | 1001 | 1001 | 1002 |
BG | Bulgaria | NA | 1000 | 1001 | 1000 | 1000 |
CY | Cyprus | NA | 998 | 1001 | 1000 | 1000 |
CZ | Czechia | NA | 1000 | 1000 | 1001 | 999 |
DE | Germany | NA | 1000 | 1000 | 1001 | 998 |
DK | Denmark | NA | 1000 | 999 | 999 | 1001 |
EE | Estonia | NA | 1000 | 998 | 1000 | 1000 |
EL | Greece | NA | 1000 | 1001 | 1001 | 1000 |
ES | Spain | NA | 999 | 1000 | 1000 | 1000 |
EU27_2020 | European Union - 27 countries (from 2020) | NA | 1000 | 1002 | 1000 | 1001 |
FI | Finland | NA | 999 | 1000 | 1000 | 1001 |
FR | France | NA | 1000 | 1002 | 1001 | 1001 |
HR | Croatia | NA | 1001 | NA | 1000 | 1000 |
HU | Hungary | NA | 1001 | 1000 | 1000 | 1001 |
IE | Ireland | NA | 998 | 1000 | 999 | 1001 |
IT | Italy | NA | 1000 | 1001 | 1000 | 1000 |
LT | Lithuania | NA | 999 | 999 | 1000 | 1002 |
LU | Luxembourg | NA | 999 | 998 | 1002 | 1002 |
LV | Latvia | NA | 1001 | 1000 | 1000 | 1001 |
ME | Montenegro | NA | 999 | NA | 1000 | 1000 |
MT | Malta | NA | 1000 | 1001 | 1001 | 1000 |
NL | Netherlands | NA | 1000 | 999 | 999 | 1000 |
PL | Poland | NA | 999 | 1001 | 1000 | 1000 |
PT | Portugal | NA | 999 | 1000 | 1002 | 1001 |
RO | Romania | NA | 1000 | 1000 | 1000 | 999 |
RS | Serbia | NA | 999 | 1000 | 999 | 1000 |
SI | Slovenia | NA | 1000 | 999 | 999 | 999 |
SK | Slovakia | NA | 1000 | 999 | 998 | 1000 |
TR | Türkiye | NA | 999 | 997 | 1002 | 1000 |
3-digit
Code
%>%
hbs_str_t225 filter(time == "2020",
substr(coicop, 1, 2) == "CP",
nchar(coicop) == 5) %>%
left_join(geo, by = "geo") %>%
select_if(~ n_distinct(.) > 1) %>%
group_by(age, geo, Geo) %>%
summarise(values = sum(values)) %>%
spread(age, values) %>%
print_table_conditional
geo | Geo | UNK | Y_GE60 | Y_LT30 | Y30-44 | Y45-59 |
---|---|---|---|---|---|---|
AT | Austria | NA | 1000 | 1001 | 999 | 1001 |
BE | Belgium | NA | 998 | 1002 | 995 | 1001 |
BG | Bulgaria | NA | 1000 | 1000 | 998 | 999 |
CY | Cyprus | NA | 994 | 1001 | 998 | 996 |
CZ | Czechia | NA | 1000 | 1003 | 998 | 998 |
DE | Germany | NA | 996 | 977 | 990 | 986 |
DK | Denmark | NA | 995 | 998 | 1000 | 1002 |
EE | Estonia | NA | 983 | 979 | 984 | 989 |
EL | Greece | NA | 1001 | 1001 | 1001 | 1001 |
ES | Spain | NA | 1000 | 1000 | 1002 | 999 |
FI | Finland | NA | 990 | 973 | 973 | 979 |
FR | France | NA | 986 | 991 | 989 | 983 |
HR | Croatia | NA | 1000 | NA | 1000 | 1003 |
HU | Hungary | NA | 997 | 999 | 999 | 1004 |
IE | Ireland | NA | 1002 | 1002 | 1003 | 1000 |
IT | Italy | NA | 1001 | 1000 | 1000 | 997 |
LT | Lithuania | NA | 1000 | 999 | 1001 | 999 |
LU | Luxembourg | NA | 999 | 998 | 1000 | 998 |
LV | Latvia | NA | 998 | 1000 | 996 | 1000 |
ME | Montenegro | NA | 985 | NA | 983 | 985 |
MT | Malta | NA | 1002 | 1001 | 1001 | 1000 |
NL | Netherlands | NA | 1002 | 1002 | 1002 | 998 |
PL | Poland | NA | 998 | 1002 | 996 | 999 |
RS | Serbia | NA | 998 | 999 | 1001 | 998 |
SI | Slovenia | NA | 1000 | 1001 | 1000 | 1000 |
SK | Slovakia | NA | 1001 | 999 | 998 | 998 |
TR | Türkiye | NA | 1001 | 1001 | 1000 | 998 |
CP041, CP042, CP041_042
2015
France
Code
%>%
hbs_str_t225 filter(coicop %in% c("CP041", "CP042"),
== "2015",
time %in% c("FR")) %>%
geo spread(coicop, values) %>%
mutate(CP041_042 = CP041 + CP042) %>%
gather(coicop, values, CP041, CP042, CP041_042) %>%
left_join(geo, by = "geo") %>%
left_join(coicop, by = "coicop") %>%
left_join(colors, by = c("Geo" = "country")) %>%
mutate(Coicop = ifelse(coicop == "CP041_042", "Imputed rentals plus actual rentals", Coicop),
age = ifelse(age == "Y_GE60", "Y60+", age),
age = ifelse(age == "Y_LT30", "Y30-", age)) %>%
+ geom_line(aes(x = age, y = values/1000, color = Coicop, group = Coicop)) +
ggplot 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())
France
Actual + Imputed
Code
%>%
hbs_str_t225 filter(coicop %in% c("CP041", "CP042"),
== "2015",
time %in% c("FR")) %>%
geo mutate(Coicop = factor(coicop, levels = c("CP042", "CP041"), labels = c("Loyers imputés (propriétaires)", "Loyers réels (locataires)")),
age = ifelse(age == "Y_GE60", "Y60+", age),
age = ifelse(age == "Y_LT30", "Y30-", age)) %>%
+ geom_col(aes(x = age, y = values/1000, fill = Coicop)) +
ggplot theme_minimal() +
xlab("") + ylab("Poids dans l'Indice des Prix") +
scale_y_continuous(breaks = 0.01*seq(-30, 50, 5),
labels = percent_format(accuracy = 1)) +
theme(legend.position = "top",
legend.direction = "horizontal",
legend.title = element_blank())
Tous
Code
load_data("eurostat/deg_urb_fr.RData")
<- hbs_str_t225 %>%
data rename(category = age) %>%
mutate(type = "age") %>%
bind_rows(hbs_str_t223 %>%
rename(category = quantile) %>%
mutate(type = "quantile")) %>%
bind_rows(hbs_str_t226 %>%
rename(category = deg_urb) %>%
mutate(type = "deg_urb")) %>%
filter(coicop %in% c("CP041", "CP042"),
== "2020",
time == "FR") %>%
geo select_if(~ n_distinct(.) > 1) %>%
select(type, everything(.)) %>%
arrange(coicop)
%>%
data spread(coicop, values) %>%
mutate(CP041_042 = CP041 + CP042) %>%
print_table_conditional()
type | category | CP041 | CP042 | CP041_042 |
---|---|---|---|---|
age | Y_GE60 | 44 | 190 | 234 |
age | Y_LT30 | 144 | 56 | 200 |
age | Y30-44 | 73 | 132 | 205 |
age | Y45-59 | 55 | 149 | 204 |
deg_urb | DEG1 | 85 | 138 | 223 |
deg_urb | DEG2 | 62 | 148 | 210 |
deg_urb | DEG3 | 33 | 168 | 201 |
quantile | QUINTILE1 | 175 | 69 | 244 |
quantile | QUINTILE2 | 112 | 125 | 237 |
quantile | QUINTILE3 | 71 | 154 | 225 |
quantile | QUINTILE4 | 41 | 167 | 208 |
quantile | QUINTILE5 | 25 | 169 | 194 |
Graph
Code
<- data %>%
data_1 mutate(Coicop = factor(coicop, levels = c("CP042", "CP041"), labels = c("Loyers imputés (propriétaires occupants)", "Loyers réels (locataires)")),
Category = factor(category, levels = c("Y_LT30", "Y30-44", "Y45-59", "Y_GE60",
"DEG1", "DEG2", "DEG3",
"QUINTILE1", "QUINTILE2", "QUINTILE3", "QUINTILE4", "QUINTILE5"),
labels = c("- de 30 ans", "De 30 à 44 ans", "De 45 à 59 ans", "+ de 60 ans",
"Villes", "Villes - peuplées\net banlieues", "Zones rurales",
"1er\n(+ pauvres)", "2è", "3è", "4è", "5è\n(+ riches)")),
Type = factor(type, levels = c("age", "deg_urb", "quantile"),
labels = c("Âge", "Commune de résidence", "Cinquième de niveau de vie"))) %>%
arrange(Coicop) %>%
mutate(values = values/1000) %>%
select(type, Type, category, Category, coicop, Coicop, values)
write.csv(data_1, file = "~/Desktop/graphique5.csv")
%>%
data_1 + geom_col(aes(x = Category, y = values, fill = Coicop)) +
ggplot theme_minimal() +
xlab("") + ylab("Poids budgétaire des loyers (%)") +
scale_y_continuous(breaks = 0.01*seq(-30, 50, 2),
labels = percent_format(accuracy = 1)) +
theme(legend.position = "bottom",
legend.title = element_blank(),
legend.margin=margin(t=-35),
axis.text.x = element_text(angle = 45, vjust = 1, hjust = 1)) +
scale_fill_manual(values = c("#005DA4", "#F59C00")) +
facet_wrap(~ Type, scales = "free")