Mean consumption expenditure by income quintile
Data - Eurostat
François Geerolf
Info
| source | dataset | .html | .RData |
|---|---|---|---|
| eurostat | hbs_str_t211 | 2024-05-09 | 2024-05-09 |
| eurostat | hbs_str_t223 | 2024-04-18 | 2024-05-09 |
| insee | bdf2017 | 2024-05-09 | 2023-11-21 |
| insee | if203 | 2024-05-09 | 2023-07-20 |
Data on housing
| source | dataset | .html | .RData |
|---|---|---|---|
| bdf | RPP | 2024-05-08 | 2024-05-08 |
| bis | LONG_PP | 2024-04-19 | 2024-04-19 |
| bis | SELECTED_PP | 2024-04-19 | 2024-04-19 |
| ecb | RPP | 2024-04-19 | 2024-04-19 |
| eurostat | ei_hppi_q | 2024-05-09 | 2024-05-09 |
| eurostat | hbs_str_t223 | 2024-04-18 | 2024-05-09 |
| eurostat | prc_hicp_midx | 2024-04-18 | 2024-05-09 |
| eurostat | prc_hpi_q | 2024-04-18 | 2024-05-09 |
| fred | housing | 2024-04-26 | 2024-04-26 |
| insee | IPLA-IPLNA-2015 | 2024-05-09 | 2024-05-09 |
| oecd | housing | 2024-04-16 | 2020-01-18 |
| oecd | SNA_TABLE5 | 2024-04-16 | 2023-10-19 |
Données sur l’inflation en France
| source | dataset | .html | .RData |
|---|---|---|---|
| insee | bdf2017 | 2024-05-09 | 2023-11-21 |
| insee | ILC-ILAT-ICC | 2024-05-09 | 2024-05-09 |
| insee | INDICES_LOYERS | 2024-05-09 | 2024-05-09 |
| insee | IPC-1970-1980 | 2024-05-09 | 2024-05-09 |
| insee | IPC-1990 | 2024-05-09 | 2024-05-09 |
| insee | IPC-2015 | 2024-05-09 | 2024-04-09 |
| insee | IPC-PM-2015 | 2024-05-09 | 2024-05-09 |
| insee | IPCH-2015 | 2024-05-09 | 2024-05-09 |
| insee | IPGD-2015 | 2024-05-09 | 2024-03-20 |
| insee | IPLA-IPLNA-2015 | 2024-05-09 | 2024-05-09 |
| insee | IPPI-2015 | 2024-05-09 | 2024-03-30 |
| insee | IRL | 2024-05-09 | 2024-05-09 |
| insee | SERIES_LOYERS | 2024-05-09 | 2024-05-09 |
| insee | T_CONSO_EFF_FONCTION | 2024-05-09 | 2024-04-01 |
Data on inflation
| source | dataset | .html | .RData |
|---|---|---|---|
| bis | CPI | 2024-04-19 | 2022-01-20 |
| ecb | CES | 2024-04-19 | 2024-01-12 |
| eurostat | nama_10_co3_p3 | 2024-04-18 | 2024-05-09 |
| eurostat | prc_hicp_cow | 2024-04-18 | 2024-05-09 |
| eurostat | prc_hicp_ctrb | 2024-04-18 | 2024-05-09 |
| eurostat | prc_hicp_inw | 2024-04-18 | 2024-05-09 |
| eurostat | prc_hicp_manr | 2024-04-18 | 2024-05-09 |
| eurostat | prc_hicp_midx | 2024-04-18 | 2024-05-09 |
| eurostat | prc_hicp_mmor | 2024-04-18 | 2024-05-09 |
| eurostat | prc_ppp_ind | 2024-04-18 | 2024-05-09 |
| eurostat | sts_inpp_m | 2024-04-18 | 2024-05-09 |
| eurostat | sts_inppd_m | 2024-04-18 | 2024-05-09 |
| eurostat | sts_inppnd_m | 2024-04-18 | 2024-05-09 |
| fred | cpi | 2024-04-19 | 2024-04-19 |
| fred | inflation | 2024-05-07 | 2024-05-07 |
| imf | CPI | 2024-01-06 | 2020-03-13 |
| oecd | MEI_PRICES_PPI | 2024-04-16 | 2024-04-15 |
| oecd | PPP2017 | 2024-04-16 | 2023-07-25 |
| oecd | PRICES_CPI | 2024-04-16 | 2024-04-15 |
| wdi | FP.CPI.TOTL.ZG | 2023-01-15 | 2024-04-14 |
| wdi | NY.GDP.DEFL.KD.ZG | 2024-04-14 | 2024-04-14 |
LAST_COMPILE
| LAST_COMPILE |
|---|
| 2024-05-09 |
Last
hbs_str_t223 %>%
group_by(time) %>%
summarise(Nobs = n()) %>%
arrange(desc(time)) %>%
head(2) %>%
print_table_conditional()| time | Nobs |
|---|---|
| 2020 | 7200 |
| 2015 | 9866 |
coicop
All
2-digit
hbs_str_t223 %>%
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 | 922 |
| CP02 | Alcoholic beverages, tobacco and narcotics | 922 |
| CP03 | Clothing and footwear | 922 |
| CP04 | Housing, water, electricity, gas and other fuels | 922 |
| CP05 | Furnishings, household equipment and routine household maintenance | 922 |
| CP06 | Health | 922 |
| CP07 | Transport | 922 |
| CP08 | Communications | 922 |
| CP09 | Recreation and culture | 922 |
| CP10 | Education | 917 |
| CP11 | Restaurants and hotels | 922 |
| CP12 | Miscellaneous goods and services | 922 |
3-digit
quantile
hbs_str_t223 %>%
left_join(quantile, by = "quantile") %>%
group_by(quantile, Quantile) %>%
summarise(Nobs = n()) %>%
print_table_conditional()| quantile | Quantile | Nobs |
|---|---|---|
| QUINTILE1 | First quintile | 10751 |
| QUINTILE2 | Second quintile | 10763 |
| QUINTILE3 | Third quintile | 10771 |
| QUINTILE4 | Fourth quintile | 10785 |
| QUINTILE5 | Fifth quintile | 10778 |
| UNK | Unknown | 71 |
geo
hbs_str_t223 %>%
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
| unit | Nobs |
|---|---|
| PM | 53919 |
time
| time | Nobs |
|---|---|
| 1988 | 2710 |
| 1994 | 4880 |
| 1999 | 4449 |
| 2005 | 11102 |
| 2010 | 13712 |
| 2015 | 9866 |
| 2020 | 7200 |
France
Table
Sum
hbs_str_t211 %>%
mutate(quantile = "TOTAL") %>%
bind_rows(hbs_str_t223) %>%
left_join(coicop, by = "coicop") %>%
filter(geo == "FR",
substr(coicop, 1, 2) == "CP",
coicop != "CP00",
time %in% c("2020")) %>%
mutate(coicop_nchar = nchar(coicop)) %>%
group_by(coicop_nchar, quantile) %>%
summarise(Nobs = n(),
sum = sum(values)) %>%
print_table_conditional()| coicop_nchar | quantile | Nobs | sum |
|---|---|---|---|
| 4 | QUINTILE1 | 12 | 1000 |
| 4 | QUINTILE2 | 12 | 1000 |
| 4 | QUINTILE3 | 12 | 1001 |
| 4 | QUINTILE4 | 12 | 999 |
| 4 | QUINTILE5 | 12 | 1001 |
| 4 | TOTAL | 12 | 1002 |
| 5 | QUINTILE1 | 47 | 993 |
| 5 | QUINTILE2 | 47 | 992 |
| 5 | QUINTILE3 | 47 | 993 |
| 5 | QUINTILE4 | 47 | 988 |
| 5 | QUINTILE5 | 47 | 981 |
| 5 | TOTAL | 47 | 991 |
| 6 | TOTAL | 115 | 896 |
2-digit and 3-digit
`table1` <- hbs_str_t211 %>%
mutate(quantile = "TOTAL") %>%
bind_rows(hbs_str_t223) %>%
left_join(coicop, by = "coicop") %>%
filter(geo == "FR",
substr(coicop, 1, 2) == "CP",
nchar(coicop) %in% c(4, 5),
coicop != "CP00",
time %in% c("2020")) %>%
select(-unit, -time) %>%
select(-geo) %>%
spread(quantile, values)
`table1` %>%
gt::gt() %>%
gt::gtsave(filename = "hbs_str_t223_files/figure-html/table1-1.png")
`table1` %>%
print_table_conditional()Missing: CP091+CP092+CP093+CP094 = 61 et pas 66
2-digit
`table1-2digit` <- hbs_str_t211 %>%
mutate(quantile = "TOTAL") %>%
bind_rows(hbs_str_t223) %>%
left_join(coicop, by = "coicop") %>%
filter(geo == "FR",
substr(coicop, 1, 2) == "CP",
nchar(coicop) == 4,
coicop != "CP00",
time %in% c("2020")) %>%
select(-unit, -time) %>%
select(-geo) %>%
spread(quantile, values)
`table1-2digit` %>%
gt::gt() %>%
gt::gtsave(filename = "hbs_str_t223_files/figure-html/table1-2digit-1.png")
`table1-2digit` %>%
print_table_conditional()| freq | coicop | Coicop | QUINTILE1 | QUINTILE2 | QUINTILE3 | QUINTILE4 | QUINTILE5 | TOTAL |
|---|---|---|---|---|---|---|---|---|
| A | CP01 | Food and non-alcoholic beverages | 147 | 150 | 154 | 150 | 128 | 143 |
| A | CP02 | Alcoholic beverages, tobacco and narcotics | 35 | 30 | 28 | 24 | 20 | 25 |
| A | CP03 | Clothing and footwear | 43 | 37 | 38 | 39 | 41 | 40 |
| A | CP04 | Housing, water, electricity, gas and other fuels | 347 | 328 | 305 | 279 | 255 | 289 |
| A | CP05 | Furnishings, household equipment and routine household maintenance | 34 | 40 | 43 | 47 | 58 | 48 |
| A | CP06 | Health | 15 | 16 | 18 | 16 | 15 | 16 |
| A | CP07 | Transport | 102 | 113 | 121 | 139 | 149 | 132 |
| A | CP08 | Communications | 35 | 28 | 25 | 23 | 18 | 24 |
| A | CP09 | Recreation and culture | 66 | 66 | 72 | 76 | 89 | 77 |
| A | CP10 | Education | 8 | 4 | 3 | 4 | 9 | 6 |
| A | CP11 | Restaurants and hotels | 40 | 41 | 44 | 52 | 71 | 55 |
| A | CP12 | Miscellaneous goods and services | 128 | 147 | 150 | 150 | 148 | 147 |
3-digit
FR_3digit_weights2020 <- hbs_str_t211 %>%
mutate(quantile = "TOTAL") %>%
bind_rows(hbs_str_t223) %>%
filter(geo == "FR",
substr(coicop, 1, 2) == "CP",
nchar(coicop) == 5,
coicop != "CP00",
time %in% c("2020")) %>%
select(-unit, -geo, -time)
dir.create("hbs_str_t223_files/data-RData")
do.call(save, list("FR_3digit_weights2020", file = "hbs_str_t223_files/data-RData/FR_3digit_weights2020.RData"))
FR_3digit_weights2020 %>%
spread(quantile, values) %>%
print_table_conditional()France - Compare
2020, HBS
2015, HBS
All
2-digit
hbs_str_t223 %>%
filter(time == "2015",
geo == "FR",
nchar(coicop) == 4) %>%
left_join(coicop, by = "coicop") %>%
select_if(~ n_distinct(.) > 1) %>%
spread(quantile, values) %>%
select_if(~ n_distinct(.) > 1) %>%
print_table_conditional| coicop | Coicop | QUINTILE1 | QUINTILE2 | QUINTILE3 | QUINTILE4 | QUINTILE5 |
|---|---|---|---|---|---|---|
| CP01 | Food and non-alcoholic beverages | 147 | 150 | 154 | 150 | 128 |
| CP02 | Alcoholic beverages, tobacco and narcotics | 35 | 30 | 28 | 24 | 20 |
| CP03 | Clothing and footwear | 43 | 37 | 38 | 39 | 41 |
| CP04 | Housing, water, electricity, gas and other fuels | 347 | 328 | 305 | 279 | 255 |
| CP05 | Furnishings, household equipment and routine household maintenance | 34 | 40 | 43 | 47 | 58 |
| CP06 | Health | 15 | 16 | 18 | 16 | 15 |
| CP07 | Transport | 102 | 113 | 121 | 139 | 149 |
| CP08 | Communications | 35 | 28 | 25 | 23 | 18 |
| CP09 | Recreation and culture | 66 | 66 | 72 | 76 | 89 |
| CP10 | Education | 8 | 4 | 3 | 4 | 9 |
| CP11 | Restaurants and hotels | 40 | 41 | 44 | 52 | 71 |
| CP12 | Miscellaneous goods and services | 128 | 147 | 150 | 150 | 148 |
France
Actual + Imputed
hbs_str_t223 %>%
filter(coicop %in% c("CP041", "CP042"),
time == "2015",
geo %in% c("FR")) %>%
mutate(Coicop = factor(coicop, levels = c("CP042", "CP041"), labels = c("Loyers imputés (propriétaires)", "Loyers réels (locataires)"))) %>%
ggplot + geom_col(aes(x = quantile, y = values/1000, fill = Coicop)) +
theme_minimal() +
xlab("Quintile") + 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
hbs_str_t225 %>%
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"),
time == "2015",
geo %in% c("FR")) %>%
mutate(Coicop = factor(coicop, levels = c("CP042", "CP041"), labels = c("Loyers imputés (propriétaires)", "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("Moins de 30 ans", "De 30 à 44 ans", "De 45 à 59 ans", "Plus de 60 ans",
"Villes", "Villes - peuplées\net banlieues", "Zones rurales",
"1er", "2è", "3è", "4è", "5è")),
Type = factor(type, levels = c("age", "deg_urb", "quantile"),
labels = c("Age", "Commune de résidence", "Cinquième"))) %>%
ggplot + geom_col(aes(x = Category, y = values/1000, fill = Coicop)) +
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(),
axis.text.x = element_text(angle = 45, vjust = 1, hjust = 1)) +
facet_wrap(~ Type, scales = "free")
All quintiles
Sums
2-digit
CP041, CP042, CP041_042
2015
Germany, France, Spain
hbs_str_t223 %>%
filter(coicop %in% c("CP041", "CP042"),
time == "2015",
geo %in% c("ES", "FR", "DE")) %>%
spread(coicop, values) %>%
mutate(CP041_042 = CP041 + CP042) %>%
gather(coicop, values, CP041, CP042, CP041_042) %>%
mutate(quantile = substr(quantile, 9, 9) %>% as.numeric) %>%
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)) %>%
ggplot + geom_line(aes(x = quantile, y = values/1000, color = color, linetype = Coicop)) +
scale_color_identity() + theme_minimal() +
geom_image(data = . %>%
filter(quantile == 1) %>%
mutate(image = paste0("../../icon/flag/round/", str_to_lower(gsub(" ", "-", Geo)), ".png")),
aes(x = quantile, y = values/1000, image = image), asp = 1.5) +
xlab("Quintile") + ylab("Weight in CPI") +
scale_y_continuous(breaks = 0.01*seq(-30, 50, 5),
labels = percent_format(accuracy = 1)) +
theme(legend.position = c(0.8, 0.9),
legend.title = element_blank()) +
scale_x_continuous(breaks = seq(0, 5, 1))
Netherlands, Austria, Greece
hbs_str_t223 %>%
filter(coicop %in% c("CP041", "CP042"),
time == "2015",
geo %in% c("EL", "NL", "AT")) %>%
spread(coicop, values) %>%
mutate(CP041_042 = CP041 + CP042) %>%
gather(coicop, values, CP041, CP042, CP041_042) %>%
mutate(quantile = substr(quantile, 9, 9) %>% as.numeric) %>%
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)) %>%
ggplot + geom_line(aes(x = quantile, y = values/1000, color = color, linetype = Coicop)) +
scale_color_identity() + theme_minimal() +
geom_image(data = . %>%
filter(quantile == 1) %>%
mutate(image = paste0("../../icon/flag/round/", str_to_lower(gsub(" ", "-", Geo)), ".png")),
aes(x = quantile, y = values/1000, image = image), asp = 1.5) +
xlab("Quintile") + ylab("Weight in CPI") +
scale_y_continuous(breaks = 0.01*seq(-30, 50, 5),
labels = percent_format(accuracy = 1)) +
theme(legend.position = c(0.8, 0.9),
legend.title = element_blank()) +
scale_x_continuous(breaks = seq(0, 5, 1))
CP041, CP042
2015
All
Germany, France, Spain
hbs_str_t223 %>%
filter(coicop %in% c("CP041", "CP042"),
time == "2015",
geo %in% c("ES", "FR", "DE")) %>%
mutate(quantile = substr(quantile, 9, 9) %>% as.numeric) %>%
left_join(geo, by = "geo") %>%
left_join(coicop, by = "coicop") %>%
left_join(colors, by = c("Geo" = "country")) %>%
ggplot + geom_line(aes(x = quantile, y = values/1000, color = color, linetype = Coicop)) +
scale_color_identity() + theme_minimal() +
geom_image(data = . %>%
filter(quantile == 1) %>%
mutate(image = paste0("../../icon/flag/round/", str_to_lower(gsub(" ", "-", Geo)), ".png")),
aes(x = quantile, y = values/1000, image = image), asp = 1.5) +
xlab("Quintile") + ylab("Weight in CPI") +
scale_y_continuous(breaks = 0.01*seq(-30, 50, 5),
labels = percent_format(accuracy = 1)) +
theme(legend.position = c(0.85, 0.85),
legend.title = element_blank()) +
scale_x_continuous(breaks = seq(0, 5, 1))
Netherlands, Austria, Greece
hbs_str_t223 %>%
filter(coicop %in% c("CP041", "CP042"),
time == "2015",
geo %in% c("EL", "NL", "AT")) %>%
mutate(quantile = substr(quantile, 9, 9) %>% as.numeric) %>%
left_join(geo, by = "geo") %>%
left_join(coicop, by = "coicop") %>%
left_join(colors, by = c("Geo" = "country")) %>%
ggplot + geom_line(aes(x = quantile, y = values/1000, color = color, linetype = Coicop)) +
scale_color_identity() + theme_minimal() +
geom_image(data = . %>%
filter(quantile == 1) %>%
mutate(image = paste0("../../icon/flag/round/", str_to_lower(gsub(" ", "-", Geo)), ".png")),
aes(x = quantile, y = values/1000, image = image), asp = 1.5) +
xlab("Quintile") + ylab("Weight in CPI") +
scale_y_continuous(breaks = 0.01*seq(-30, 50, 5),
labels = percent_format(accuracy = 1)) +
theme(legend.position = c(0.8, 0.8),
legend.title = element_blank()) +
scale_x_continuous(breaks = seq(0, 5, 1))
2020
All
Germany, France, Spain
hbs_str_t223 %>%
filter(coicop %in% c("CP041", "CP042"),
time == "2020",
geo %in% c("ES", "FR", "DE")) %>%
mutate(quantile = substr(quantile, 9, 9) %>% as.numeric) %>%
left_join(geo, by = "geo") %>%
left_join(coicop, by = "coicop") %>%
left_join(colors, by = c("Geo" = "country")) %>%
ggplot + geom_line(aes(x = quantile, y = values/1000, color = color, linetype = Coicop)) +
scale_color_identity() + theme_minimal() +
geom_image(data = . %>%
filter(quantile == 1) %>%
mutate(image = paste0("../../icon/flag/round/", str_to_lower(gsub(" ", "-", Geo)), ".png")),
aes(x = quantile, y = values/1000, image = image), asp = 1.5) +
xlab("Quintile") + ylab("Weight in CPI") +
scale_y_continuous(breaks = 0.01*seq(-30, 50, 5),
labels = percent_format(accuracy = 1)) +
theme(legend.position = c(0.8, 0.8),
legend.title = element_blank()) +
scale_x_continuous(breaks = seq(0, 5, 1))
Netherlands, Austria, Greece
hbs_str_t223 %>%
filter(coicop %in% c("CP041", "CP042"),
time == "2020",
geo %in% c("EL", "NL", "AT")) %>%
mutate(quantile = substr(quantile, 9, 9) %>% as.numeric) %>%
left_join(geo, by = "geo") %>%
left_join(coicop, by = "coicop") %>%
left_join(colors, by = c("Geo" = "country")) %>%
ggplot + geom_line(aes(x = quantile, y = values/1000, color = color, linetype = Coicop)) +
scale_color_identity() + theme_minimal() +
geom_image(data = . %>%
filter(quantile == 1) %>%
mutate(image = paste0("../../icon/flag/round/", str_to_lower(gsub(" ", "-", Geo)), ".png")),
aes(x = quantile, y = values/1000, image = image), asp = 1.5) +
xlab("Quintile") + ylab("Weight in CPI") +
scale_y_continuous(breaks = 0.01*seq(-30, 50, 5),
labels = percent_format(accuracy = 1)) +
theme(legend.position = c(0.85, 0.85),
legend.title = element_blank()) +
scale_x_continuous(breaks = seq(0, 5, 1))
CP011, CP012
2015
All
Germany, France, Spain
All
hbs_str_t223 %>%
filter(coicop %in% c("CP011", "CP01"),
time == "2015",
geo %in% c("ES", "FR", "DE")) %>%
mutate(quantile = substr(quantile, 9, 9) %>% as.numeric) %>%
left_join(geo, by = "geo") %>%
left_join(coicop, by = "coicop") %>%
left_join(colors, by = c("Geo" = "country")) %>%
ggplot + geom_line(aes(x = quantile, y = values/1000, color = color, linetype = Coicop)) +
scale_color_identity() + theme_minimal() +
geom_image(data = . %>%
filter(quantile == 2) %>%
mutate(image = paste0("../../icon/flag/round/", str_to_lower(gsub(" ", "-", Geo)), ".png")),
aes(x = quantile, y = values/1000, image = image), asp = 1.5) +
xlab("Quintile") + ylab("Weight in CPI") +
scale_y_continuous(breaks = 0.01*seq(-30, 50, 1),
labels = percent_format(accuracy = 1)) +
theme(legend.position = c(0.75, 0.85),
legend.title = element_blank()) +
scale_x_continuous(breaks = seq(0, 5, 1))