HICP - country weights

Data - Eurostat

Author

François Geerolf

Info

source dataset Title .html .rData
eurostat prc_hicp_cow HICP - country weights 2026-01-31 2026-01-31

Données sur l’inflation en France

source dataset Title .html .rData
insee ILC-ILAT-ICC Indices pour la révision d’un bail commercial ou professionnel 2026-01-31 2026-01-31
insee INDICES_LOYERS Indices des loyers d'habitation (ILH) 2026-01-31 2026-01-31
insee IPC-1970-1980 Indice des prix à la consommation - Base 1970, 1980 2026-01-31 2026-01-31
insee IPC-1990 Indices des prix à la consommation - Base 1990 2026-01-31 2026-01-31
insee IPC-2015 Indice des prix à la consommation - Base 2015 2026-01-31 2026-01-31
insee IPC-PM-2015 Prix moyens de vente de détail 2026-01-31 2026-01-31
insee IPCH-2015 Indices des prix à la consommation harmonisés 2026-01-31 2026-01-31
insee IPCH-IPC-2015-ensemble Indices des prix à la consommation harmonisés 2026-01-31 2026-01-31
insee IPGD-2015 Indice des prix dans la grande distribution 2026-01-31 2025-12-20
insee IPLA-IPLNA-2015 Indices des prix des logements neufs et Indices Notaires-Insee des prix des logements anciens 2026-01-31 2026-01-31
insee IPPI-2015 Indices de prix de production et d'importation dans l'industrie 2026-01-31 2026-01-31
insee IRL Indice pour la révision d’un loyer d’habitation 2026-01-08 2026-01-31
insee SERIES_LOYERS Variation des loyers 2026-01-31 2026-01-31
insee T_CONSO_EFF_FONCTION Consommation effective des ménages par fonction 2026-01-31 2025-12-22
insee bdf2017 Budget de famille 2017 2026-01-31 2023-11-21
insee echantillon-agglomerations-IPC-2024 Échantillon d’agglomérations enquêtées de l’IPC en 2024 2026-01-27 2026-01-27
insee echantillon-agglomerations-IPC-2025 Échantillon d’agglomérations enquêtées de l’IPC en 2025 2026-01-27 2026-01-27
insee liste-varietes-IPC-2024 Liste des variétés pour la mesure de l'IPC en 2024 2026-01-27 2025-04-02
insee liste-varietes-IPC-2025 Liste des variétés pour la mesure de l'IPC en 2025 2026-01-27 2026-01-27
insee ponderations-elementaires-IPC-2024 Pondérations élémentaires 2024 intervenant dans le calcul de l’IPC 2026-01-27 2025-04-02
insee ponderations-elementaires-IPC-2025 Pondérations élémentaires 2025 intervenant dans le calcul de l’IPC 2026-01-27 2026-01-27
insee table_conso_moyenne_par_categorie_menages Montants de consommation selon différentes catégories de ménages 2026-01-31 2026-01-27
insee table_poste_au_sein_sous_classe_ecoicopv2_france_entiere_ Ventilation de chaque sous-classe (niveau 4 de la COICOP v2) en postes et leurs pondérations 2026-01-31 2026-01-27
insee tranches_unitesurbaines Poids de chaque tranche d’unités urbaines dans la consommation 2026-01-31 2026-01-27

Data on inflation

source dataset Title .html .rData
eurostat prc_hicp_cow HICP - country weights 2026-01-31 2026-01-31
bis CPI Consumer Price Index 2026-01-31 2026-01-31
ecb CES Consumer Expectations Survey 2025-08-28 2025-05-24
eurostat nama_10_co3_p3 Final consumption expenditure of households by consumption purpose (COICOP 3 digit) 2026-01-31 2026-01-31
eurostat prc_hicp_ctrb Contributions to euro area annual inflation (in percentage points) 2026-01-31 2026-01-31
eurostat prc_hicp_inw HICP - item weights 2026-01-31 2026-01-31
eurostat prc_hicp_manr HICP (2015 = 100) - monthly data (annual rate of change) 2026-01-31 2026-01-31
eurostat prc_hicp_midx HICP (2015 = 100) - monthly data (index) 2026-01-31 2026-01-31
eurostat prc_hicp_mmor HICP (2015 = 100) - monthly data (monthly rate of change) 2026-01-29 2026-01-31
eurostat prc_ppp_ind Purchasing power parities (PPPs), price level indices and real expenditures for ESA 2010 aggregates 2026-01-31 2026-01-31
eurostat sts_inpp_m Producer prices in industry, total - monthly data 2026-01-31 2026-01-31
eurostat sts_inppd_m Producer prices in industry, domestic market - monthly data 2026-01-31 2026-01-31
eurostat sts_inppnd_m Producer prices in industry, non domestic market - monthly data 2024-06-24 2026-01-31
fred cpi Consumer Price Index 2026-01-31 2026-01-31
fred inflation Inflation 2026-01-31 2026-01-31
imf CPI Consumer Price Index - CPI 2026-01-31 2020-03-13
oecd MEI_PRICES_PPI Producer Prices - MEI_PRICES_PPI 2026-01-16 2024-04-15
oecd PPP2017 2017 PPP Benchmark results 2024-04-16 2023-07-25
oecd PRICES_CPI Consumer price indices (CPIs) 2024-04-16 2024-04-15
wdi FP.CPI.TOTL.ZG Inflation, consumer prices (annual %) 2026-01-31 2026-01-31
wdi NY.GDP.DEFL.KD.ZG Inflation, GDP deflator (annual %) 2026-01-31 2026-01-31

LAST_COMPILE

LAST_COMPILE
2026-02-03

Last

Code 
prc_hicp_cow %>%
  group_by(time) %>%
  summarise(Nobs = n()) %>%
  arrange(desc(time)) %>%
  head(1) %>%
  print_table_conditional()
time Nobs
2025 91

statinfo

Code 
prc_hicp_cow %>%
  left_join(statinfo, by = "statinfo") %>%
  group_by(statinfo, Statinfo) %>%
  summarise(Nobs = n()) %>%
  arrange(-Nobs) %>%
  {if (is_html_output()) print_table(.) else .}
statinfo Statinfo Nobs
COWEA19 Country weights for EA19 (euro area 2015-2022) 600
COWEA20 Country weights for EA20 (euro area from 2023) 546
COWEA Country weights for the euro area (EA11-1999, EA12-2001, EA13-2007, EA15-2008, EA16-2009, EA17-2011, EA18-2014, EA19-2015, EA20-2023) 498
COWEEA Country weights for EEA (European Economic Area) 397
COWEU Country weights for European Union (EU6-1958, EU9-1973, EU10-1981, EU12-1986, EU15-1995, EU25-2004, EU27-2007, EU28-2013, EU27-2020) 337
COWEU27_2020 Country weights for EU27 (from 2020) 330
COWEU28 Country weights for EU28 (2013-2020) 304

geo

Code 
prc_hicp_cow %>%
  left_join(geo, by = "geo") %>%
  group_by(geo, Geo) %>%
  summarise(Nobs = n()) %>%
  arrange(-Nobs) %>%
  mutate(Flag = gsub(" ", "-", str_to_lower(gsub(" ", "-", Geo))),
         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 .}

time

Code 
prc_hicp_cow %>%
  group_by(time) %>%
  summarise(Nobs = n()) %>%
  arrange(desc(time)) %>%
  print_table_conditional()
time Nobs
2025 91
2024 91
2023 91
2022 93
2021 93
2020 104
2019 106
2018 106
2017 106
2016 106
2015 106
2014 109
2013 112
2012 110
2011 110
2010 113
2009 113
2008 116
2007 122
2006 121
2005 121
2004 121
2003 101
2002 101
2001 101
2000 104
1999 46
1998 66
1997 66
1996 66

Compare series

France

Code 
prc_hicp_cow %>%
  filter(geo %in% c("FR")) %>%
  year_to_date %>%
  left_join(statinfo, by = "statinfo") %>%
  mutate(values = values/1000) %>%
  ggplot(.) + geom_line(aes(x = date, y = values, color = Statinfo)) + 
  theme_minimal() + xlab("") + ylab("") +
  scale_x_date(breaks = seq(1960, 2100, 2) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  scale_y_continuous(breaks = 0.01*seq(-100, 100, 1),
                     labels = percent_format(a = 1)) + 
  theme(legend.position = c(0.7, 0.5),
        legend.title = element_blank())

Germany

Code 
prc_hicp_cow %>%
  filter(geo %in% c("DE")) %>%
  year_to_date %>%
  left_join(statinfo, by = "statinfo") %>%
  mutate(values = values/1000) %>%
  ggplot(.) + geom_line(aes(x = date, y = values, color = Statinfo)) + 
  theme_minimal() + xlab("") + ylab("") +
  scale_x_date(breaks = seq(1960, 2100, 2) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  scale_y_continuous(breaks = 0.01*seq(-100, 100, 1),
                     labels = percent_format(a = 1)) + 
  theme(legend.position = c(0.7, 0.5),
        legend.title = element_blank())

Italy

Code 
prc_hicp_cow %>%
  filter(geo %in% c("IT")) %>%
  year_to_date %>%
  left_join(statinfo, by = "statinfo") %>%
  mutate(values = values/1000) %>%
  ggplot(.) + geom_line(aes(x = date, y = values, color = Statinfo)) + 
  theme_minimal() + xlab("") + ylab("") +
  scale_x_date(breaks = seq(1960, 2100, 2) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  scale_y_continuous(breaks = 0.01*seq(-100, 100, 1),
                     labels = percent_format(a = 1)) + 
  theme(legend.position = c(0.7, 0.5),
        legend.title = element_blank())

EA20

time

Code 
prc_hicp_cow %>%
  filter(statinfo == "COWEA20",
         geo != "EA19") %>%
  select(-statinfo) %>%
  group_by(time) %>%
  summarise(Nobs = n()) %>%
  arrange(desc(time)) %>%
  print_table_conditional()
time Nobs
2025 21
2024 21
2023 21
2022 21
2021 21
2020 21
2019 21
2018 21
2017 21
2016 21
2015 21
2014 21
2013 21
2012 21
2011 21
2010 21
2009 21
2008 21
2007 21
2006 21
2005 21
2004 21
2003 21
2002 21
2001 21
2000 21

2022 Table

Code 
prc_hicp_cow %>%
  filter(time == "2022",
         statinfo == "COWEA20",
         geo != "EA19") %>%
  left_join(geo, by = "geo") %>%
  select(-time) %>%
  spread(statinfo, values) %>%
  arrange(-COWEA20) %>%
  mutate(cumsum = cumsum(COWEA20)) %>%
  mutate(Flag = gsub(" ", "-", str_to_lower(gsub(" ", "-", Geo))),
         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 .}

2019 Table

Code 
prc_hicp_cow %>%
  filter(time == "2019",
         statinfo == "COWEA20",
         geo != "EA19") %>%
  left_join(geo, by = "geo") %>%
  select(-time) %>%
  spread(statinfo, values) %>%
  arrange(-COWEA20) %>%
  mutate(cumsum = cumsum(COWEA20)) %>%
  mutate(Flag = gsub(" ", "-", str_to_lower(gsub(" ", "-", Geo))),
         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 .}

Germany, France, Italy, Spain

Code 
prc_hicp_cow %>%
  filter(statinfo == "COWEA20",
         geo %in% c("DE", "FR", "IT", "ES")) %>%
  year_to_date %>%
  left_join(geo, by = "geo") %>%
  left_join(colors, by = c("Geo" = "country")) %>%
  mutate(values = values/1000) %>%
  ggplot(.) + geom_line(aes(x = date, y = values, color = color)) + 
  theme_minimal() + xlab("") + ylab("") +
  scale_x_date(breaks = seq(1960, 2100, 2) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  scale_y_continuous(breaks = 0.01*seq(-100, 100, 2),
                     labels = percent_format(a = 1)) + 
  scale_color_identity() + add_4flags +
  theme(legend.position = c(0.75, 0.90),
        legend.title = element_blank())

Netherlands, Belgium, Austria, Portugal

Code 
prc_hicp_cow %>%
  filter(statinfo == "COWEA20",
         geo %in% c("NL", "BE", "AT", "PT")) %>%
  year_to_date %>%
  left_join(geo, by = "geo") %>%
  left_join(colors, by = c("Geo" = "country")) %>%
  mutate(values = values/1000) %>%
  ggplot(.) + geom_line(aes(x = date, y = values, color = color)) + 
  theme_minimal() + xlab("") + ylab("") +
  scale_x_date(breaks = seq(1960, 2100, 2) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  scale_y_continuous(breaks = 0.01*seq(-100, 100, .5),
                     labels = percent_format(a = .1)) + 
  scale_color_identity() + add_4flags +
  theme(legend.position = c(0.75, 0.90),
        legend.title = element_blank())

Greece, Finland, Ireland, Slovakia

Code 
prc_hicp_cow %>%
  filter(statinfo == "COWEA20",
         geo %in% c("EL", "FI", "IE", "SK")) %>%
  year_to_date %>%
  left_join(geo, by = "geo") %>%
  left_join(colors, by = c("Geo" = "country")) %>%
  mutate(values = values/1000) %>%
  ggplot(.) + geom_line(aes(x = date, y = values, color = color)) + 
  theme_minimal() + xlab("") + ylab("") +
  scale_x_date(breaks = seq(1960, 2100, 2) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  scale_y_continuous(breaks = 0.01*seq(-100, 100, .5),
                     labels = percent_format(a = .1)) + 
  scale_color_identity() + add_4flags +
  theme(legend.position = c(0.75, 0.90),
        legend.title = element_blank())

Lithuania, Slovenia, Luxembourg, Latvia

Code 
prc_hicp_cow %>%
  filter(statinfo == "COWEA20",
         geo %in% c("LT", "SI", "LU", "LV")) %>%
  year_to_date %>%
  left_join(geo, by = "geo") %>%
  left_join(colors, by = c("Geo" = "country")) %>%
  mutate(values = values/1000) %>%
  ggplot(.) + geom_line(aes(x = date, y = values, color = color)) + 
  theme_minimal() + xlab("") + ylab("") +
  scale_x_date(breaks = seq(1960, 2100, 2) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  scale_y_continuous(breaks = 0.01*seq(-100, 100, .1),
                     labels = percent_format(a = .1)) + 
  scale_color_identity() + add_4flags +
  theme(legend.position = c(0.75, 0.90),
        legend.title = element_blank())

Estonia, Cyprus, Malta

Code 
prc_hicp_cow %>%
  filter(statinfo == "COWEA20",
         geo %in% c("EE", "CY", "MT")) %>%
  year_to_date %>%
  left_join(geo, by = "geo") %>%
  left_join(colors, by = c("Geo" = "country")) %>%
  mutate(values = values/1000) %>%
  ggplot(.) + geom_line(aes(x = date, y = values, color = color)) + 
  theme_minimal() + xlab("") + ylab("") +
  scale_x_date(breaks = seq(1960, 2100, 2) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  scale_y_continuous(breaks = 0.01*seq(-100, 100, .05),
                     labels = percent_format(a = .01)) + 
  scale_color_identity() + add_3flags +
  theme(legend.position = c(0.75, 0.90),
        legend.title = element_blank())

All

2022

Code 
prc_hicp_cow %>%
  filter(time == "2022") %>%
  left_join(geo, by = "geo") %>%
  select(-time) %>%
  spread(statinfo, values) %>%
  mutate(Flag = gsub(" ", "-", str_to_lower(gsub(" ", "-", Geo))),
         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 .}

2019

Code 
prc_hicp_cow %>%
  filter(time == "2019") %>%
  left_join(geo, by = "geo") %>%
  select(-time) %>%
  spread(statinfo, values) %>%
  mutate(Flag = gsub(" ", "-", str_to_lower(gsub(" ", "-", Geo))),
         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 .}

PECO

Bulgarie, Croatie, Estonie, Hongrie, Lettonie, Lituanie, Pologne, Roumanie, Slovénie, Slovaquie, République tchèque.

List

Code 
Geo_PECO <- c("Bulgaria", "Croatia", "Estonia", "Hungary", "Lithuania", "Poland",
              "Romania", "Slovenia", "Slovakia", "Czechia")

EA19

2022 Table

Code 
prc_hicp_cow %>%
  filter(time == "2022",
         statinfo == "COWEA20",
         geo != "EA19") %>%
  left_join(geo, by = "geo") %>%
  filter(Geo %in% Geo_PECO) %>%
  select(-time) %>%
  spread(statinfo, values) %>%
  arrange(-COWEA20) %>%
  mutate(cumsum = cumsum(COWEA20)) %>%
  mutate(Flag = gsub(" ", "-", str_to_lower(gsub(" ", "-", Geo))),
         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 .}