Indices des prix à la consommation - Base 1990

Data - INSEE

Author

François Geerolf

Info

source dataset Title .html .rData
insee IPC-1990 Indices des prix à la consommation - Base 1990 2025-10-10 2025-10-09

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 2025-10-10 2025-10-09
insee INDICES_LOYERS Indices des loyers - Base 2019 2025-10-10 2025-10-09
insee IPC-1970-1980 Indice des prix à la consommation - Base 1970, 1980 2025-10-10 2025-10-09
insee IPC-1990 Indices des prix à la consommation - Base 1990 2025-10-10 2025-10-09
insee IPC-2015 Indice des prix à la consommation - Base 2015 2025-10-10 2025-10-10
insee IPC-PM-2015 Prix moyens de vente de détail 2025-10-10 2025-10-09
insee IPCH-2015 Indices des prix à la consommation harmonisés 2025-10-10 2025-10-09
insee IPCH-IPC-2015-ensemble Indices des prix à la consommation harmonisés 2025-10-10 2025-10-10
insee IPGD-2015 Indice des prix dans la grande distribution 2025-10-10 2025-05-24
insee IPLA-IPLNA-2015 Indices des prix des logements neufs et Indices Notaires-Insee des prix des logements anciens 2025-10-10 2025-10-09
insee IPPI-2015 Indices de prix de production et d'importation dans l'industrie 2025-10-10 2025-10-10
insee IRL Indice pour la révision d’un loyer d’habitation 2025-10-10 2025-10-09
insee SERIES_LOYERS Variation des loyers 2025-10-10 2025-10-10
insee T_CONSO_EFF_FONCTION Consommation effective des ménages par fonction 2025-10-10 2024-07-18
insee bdf2017 Budget de famille 2017 2025-10-10 2023-11-21
insee echantillon-agglomerations-IPC-2024 Échantillon d’agglomérations enquêtées de l’IPC en 2024 2025-10-10 2025-04-02
insee liste-varietes-IPC-2024 Liste des variétés pour la mesure de l'IPC en 2024 2025-10-10 2025-04-02
insee ponderations-elementaires-IPC-2024 Pondérations élémentaires 2024 intervenant dans le calcul de l’IPC 2025-10-10 2025-04-02

Data on inflation

source dataset Title .html .rData
bis CPI Consumer Price Index 2025-10-10 2025-10-09
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) 2025-10-10 2025-09-26
eurostat prc_hicp_cow HICP - country weights 2025-10-10 2025-10-10
eurostat prc_hicp_ctrb Contributions to euro area annual inflation (in percentage points) 2025-10-10 2025-10-10
eurostat prc_hicp_inw HICP - item weights 2025-10-10 2025-10-09
eurostat prc_hicp_manr HICP (2015 = 100) - monthly data (annual rate of change) 2025-10-10 2025-10-10
eurostat prc_hicp_midx HICP (2015 = 100) - monthly data (index) 2025-10-10 2025-10-09
eurostat prc_hicp_mmor HICP (2015 = 100) - monthly data (monthly rate of change) 2025-10-10 2025-10-09
eurostat prc_ppp_ind Purchasing power parities (PPPs), price level indices and real expenditures for ESA 2010 aggregates 2025-10-10 2025-10-10
eurostat sts_inpp_m Producer prices in industry, total - monthly data 2025-10-10 2025-10-09
eurostat sts_inppd_m Producer prices in industry, domestic market - monthly data 2025-10-10 2025-10-10
eurostat sts_inppnd_m Producer prices in industry, non domestic market - monthly data 2024-06-24 2025-10-10
fred cpi Consumer Price Index 2025-10-09 2025-10-09
fred inflation Inflation 2025-10-09 2025-10-09
imf CPI Consumer Price Index - CPI 2025-08-28 2020-03-13
oecd MEI_PRICES_PPI Producer Prices - MEI_PRICES_PPI 2025-09-29 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 %) 2023-01-15 2025-09-27
wdi NY.GDP.DEFL.KD.ZG Inflation, GDP deflator (annual %) 2025-10-10 2025-09-27

LAST_COMPILE

LAST_COMPILE
2025-10-11

LAST_UPDATE

Code 
`IPC-1990` %>%
  group_by(LAST_UPDATE) %>%
  summarise(Nobs = n()) %>%
  arrange(desc(LAST_UPDATE)) %>%
  print_table_conditional()
LAST_UPDATE Nobs
2018-02-12 69239

Last TIME_PERIOD

Code 
`IPC-1990` %>%
  group_by(TIME_PERIOD) %>%
  summarise(Nobs = n()) %>%
  arrange(desc(TIME_PERIOD)) %>%
  head(1) %>%
  print_table_conditional()
TIME_PERIOD Nobs
1998-12 587

NATURE

Code 
`IPC-1990` %>%
  left_join(NATURE,  by = "NATURE") %>%
  group_by(NATURE, Nature) %>%
  summarise(Nobs = n()) %>%
  arrange(-Nobs) %>%
  print_table_conditional
NATURE Nature Nobs
INDICE Indice 60671
POND Pondérations d'indice 3168
VARIATIONS_M Variations mensuelles 2758
GLISSEMENT_ANNUEL Glissement annuel 2627
VARIATIONS_A Variations annuelles 15

MENAGES_IPC

Code 
`IPC-1990` %>%
  left_join(MENAGES_IPC,  by = "MENAGES_IPC") %>%
  group_by(MENAGES_IPC, Menages_ipc) %>%
  summarise(Nobs = n()) %>%
  arrange(-Nobs) %>%
  print_table_conditional
MENAGES_IPC Menages_ipc Nobs
POPULATION-TOTALE Population totale 62343
MENAGES-URBAINS Ménages urbains employés ou ouvriers 3918
MENAGES-PARISIENS Ménages parisiens employés ou ouvriers 2049
ENSEMBLE Ensemble des ménages 929

COICOP_1990

Code 
`IPC-1990` %>%
  left_join(COICOP_1990,  by = "COICOP_1990") %>%
  group_by(COICOP_1990, Coicop_1990) %>%
  summarise(Nobs = n()) %>%
  arrange(-Nobs) %>%
  print_table_conditional

PRODUITS_1990

Code 
`IPC-1990` %>%
  left_join(PRODUITS_1990,  by = "PRODUITS_1990") %>%
  group_by(PRODUITS_1990, Produits_1990) %>%
  summarise(Nobs = n()) %>%
  arrange(-Nobs) %>%
  print_table_conditional

TITLE_FR

Code 
`IPC-1990` %>%
  group_by(TITLE_FR, IDBANK) %>%
  summarise(Nobs = n()) %>%
  arrange(-Nobs) %>%
  print_table_conditional

REF_AREA

Code 
`IPC-1990` %>%
  group_by(REF_AREA) %>%
  summarise(Nobs = n()) %>%
  arrange(-Nobs) %>%
  print_table_conditional
REF_AREA Nobs
FE 62343
FM 4847
D75 2049

TIME_PERIOD

Code 
`IPC-1990` %>%
  group_by(TIME_PERIOD) %>%
  summarise(Nobs = n()) %>%
  arrange(desc(TIME_PERIOD)) %>%
  print_table_conditional

Pondérations d’indice

1992, 1994, 1996, 1998

Code 
`IPC-1990` %>%
  filter(NATURE == "POND",
         TIME_PERIOD %in% c("1998", "1996", "1994", "1992", "1990"),
         MENAGES_IPC == "POPULATION-TOTALE") %>%
  select_if(function(col) length(unique(col)) > 1) %>%
  select(-IDBANK, -TITLE_FR, -TITLE_EN, -OBS_STATUS, -OBS_TYPE) %>%
  left_join(PRODUITS_1990,  by = "PRODUITS_1990") %>%
  left_join(COICOP_1990,  by = "COICOP_1990") %>%
  spread(TIME_PERIOD, OBS_VALUE) %>%
  print_table_conditional

Tabac

Code 
`IPC-1990` %>%
  filter(INDICATEUR == "IPC",
         MENAGES_IPC == "POPULATION-TOTALE",
         COICOP_1990 %in% c("14"),
         NATURE == "POND") %>%
  year_to_date %>%
  mutate(OBS_VALUE = OBS_VALUE/10000) %>%
  ggplot() + ylab("Poids du tabac dans l'indice des prix") + xlab("") + theme_minimal() +
  geom_line(aes(x = date, y = OBS_VALUE)) +
  scale_color_manual(values = viridis(3)[1:2]) +
  scale_x_date(breaks = seq(1920, 2025, 1) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  scale_y_continuous(breaks = 0.01*seq(0, 10, 0.1),
                     labels = percent_format(accuracy = .1))

Santé

Code 
`IPC-1990` %>%
  filter(INDICATEUR == "IPC",
         MENAGES_IPC == "POPULATION-TOTALE",
         COICOP_1990 %in% c("5"),
         NATURE == "POND") %>%
  year_to_date %>%
  mutate(OBS_VALUE = OBS_VALUE/10000) %>%
  ggplot() + ylab("Poids de la santé dans l'indice des prix") + xlab("") + theme_minimal() +
  geom_line(aes(x = date, y = OBS_VALUE)) +
  scale_color_manual(values = viridis(3)[1:2]) +
  scale_x_date(breaks = seq(1920, 2025, 1) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  scale_y_continuous(breaks = 0.01*seq(0, 10, 0.1),
                     labels = percent_format(accuracy = .1))

2-digit

Boissons Alcoolisées, Logement, Restaurants et hôtels

Code 
`IPC-1990` %>%
  filter(COICOP_1990 %in% c("0", "2", "11", "4"),
         REF_AREA == "FE",
         NATURE == "INDICE",
         FREQ == "M") %>%
  month_to_date %>%
  left_join(COICOP_1990, by = "COICOP_1990") %>%
  group_by(COICOP_1990) %>%
  arrange(date) %>%
  mutate(OBS_VALUE = 100*OBS_VALUE/OBS_VALUE[1]) %>%
  ggplot() + ylab("Indice des prix") + xlab("") + theme_minimal() +
  geom_line(aes(x = date, y = OBS_VALUE, color = Coicop_1990)) +
  scale_color_manual(values = viridis(5)[1:4]) +
  scale_x_date(breaks = seq(1920, 2025, 1) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  theme(legend.position = c(0.3, 0.85),
        legend.title = element_blank()) +
  scale_y_log10(breaks = seq(100, 200, 1),
                     labels = dollar_format(accuracy = 1, prefix = ""))

Transports, Enseignement, B&S Divers

Code 
`IPC-1990` %>%
  filter(COICOP_1990 %in% c("00", "1", "7", "12"),
         REF_AREA == "FE",
         NATURE == "INDICE",
         FREQ == "M") %>%
  month_to_date %>%
  left_join(COICOP_1990, by = "COICOP_1990") %>%
  group_by(COICOP_1990) %>%
  arrange(date) %>%
  mutate(OBS_VALUE = 100*OBS_VALUE/OBS_VALUE[1]) %>%
  ggplot() + ylab("Indice des prix") + xlab("") + theme_minimal() +
  geom_line(aes(x = date, y = OBS_VALUE, color = Coicop_1990)) +
  scale_color_manual(values = viridis(5)[1:4]) +
  scale_x_date(breaks = seq(1920, 2025, 2) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  theme(legend.position = c(0.3, 0.85),
        legend.title = element_blank()) +
  scale_y_log10(breaks = seq(100, 300, 1),
                     labels = dollar_format(accuracy = 1, prefix = ""))

Alimentation, Habillement, Meubles

Code 
`IPC-1990` %>%
  filter(COICOP_1990 %in% c("00", "5", "1", "3"),
         REF_AREA == "FE",
         NATURE == "INDICE",
         FREQ == "M") %>%
  month_to_date %>%
  left_join(COICOP_1990, by = "COICOP_1990") %>%
  group_by(COICOP_1990) %>%
  arrange(date) %>%
  mutate(OBS_VALUE = 100*OBS_VALUE/OBS_VALUE[1]) %>%
  ggplot() + ylab("Indice des prix") + xlab("") + theme_minimal() +
  geom_line(aes(x = date, y = OBS_VALUE, color = Coicop_1990)) +
  scale_color_manual(values = viridis(5)[1:4]) +
  scale_x_date(breaks = seq(1920, 2025, 1) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  theme(legend.position = c(0.3, 0.85),
        legend.title = element_blank()) +
  scale_y_log10(breaks = seq(100, 300, 1),
                     labels = dollar_format(accuracy = 1, prefix = ""))

Santé, Communications, Loisirs

Code 
`IPC-1990` %>%
  filter(COICOP_1990 %in% c("00", "6", "9", "8"),
         REF_AREA == "FE",
         NATURE == "INDICE",
         FREQ == "M") %>%
  month_to_date %>%
  left_join(COICOP_1990, by = "COICOP_1990") %>%
  group_by(COICOP_1990) %>%
  arrange(date) %>%
  mutate(OBS_VALUE = 100*OBS_VALUE/OBS_VALUE[1]) %>%
  ggplot() + ylab("Indice des prix") + xlab("") + theme_minimal() +
  geom_line(aes(x = date, y = OBS_VALUE, color = Coicop_1990)) +
  scale_color_manual(values = viridis(5)[1:4]) +
  scale_x_date(breaks = seq(1920, 2025, 2) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  theme(legend.position = c(0.2, 0.85),
        legend.title = element_blank()) +
  scale_y_log10(breaks = seq(10, 300, 10),
                     labels = dollar_format(accuracy = 1, prefix = ""))