Consumer Expectations Survey (EA6)

Data - ECB

Author

François Geerolf

Info

source dataset Title .html .rData
ecb CES Consumer Expectations Survey 2026-06-03 2026-05-28

Data on inflation

source dataset Title .html .rData
ecb CES Consumer Expectations Survey 2026-06-03 2026-05-28
bis CPI Consumer Price Index 2026-06-04 2026-06-04
eurostat nama_10_co3_p3 NA NA NA
eurostat prc_hicp_cow HICP - country weights 2026-06-04 2026-04-26
eurostat prc_hicp_ctrb Contributions to euro area annual inflation (in percentage points) 2026-06-04 2026-04-26
eurostat prc_hicp_inw HICP - item weights 2026-05-30 2026-04-26
eurostat prc_hicp_manr HICP (2015 = 100) - monthly data (annual rate of change) 2026-06-04 2026-04-26
eurostat prc_hicp_midx HICP (2015 = 100) - monthly data (index) 2026-06-04 2026-04-26
eurostat prc_hicp_mmor HICP (2015 = 100) - monthly data (monthly rate of change) 2026-06-04 2026-04-26
eurostat prc_ppp_ind Purchasing power parities (PPPs), price level indices and real expenditures for ESA 2010 aggregates 2026-06-04 2026-04-26
eurostat sts_inpp_m Producer prices in industry, total - monthly data 2026-06-04 2026-04-26
eurostat sts_inppd_m Producer prices in industry, domestic market - monthly data 2026-06-04 2026-04-26
eurostat sts_inppnd_m Producer prices in industry, non domestic market - monthly data 2026-06-04 2026-05-28
fred cpi Consumer Price Index 2026-06-04 2026-05-29
fred inflation Inflation 2026-06-04 2026-05-29
imf CPI Consumer Price Index (CPI) 2026 February - CPI_2026_FEB_VINTAGE 2026-06-04 2026-04-13
oecd MEI_PRICES_PPI Producer Prices - MEI_PRICES_PPI 2026-06-04 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 NA NA NA
wdi NY.GDP.DEFL.KD.ZG NA NA NA

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 2026-06-04 2026-06-03
insee INDICES_LOYERS Indices des loyers d'habitation (ILH) 2026-06-04 2026-06-03
insee IPC-1970-1980 Indice des prix à la consommation - Base 1970, 1980 2026-06-04 2026-06-03
insee IPC-1990 Indices des prix à la consommation - Base 1990 2026-06-04 2026-06-03
insee IPC-2015 Indice des prix à la consommation - Base 2015 2026-06-04 2026-06-04
insee IPC-PM-2015 Prix moyens de vente de détail 2026-02-24 2026-06-03
insee IPCH-2015 Indices des prix à la consommation harmonisés 2026-06-04 2026-06-03
insee IPCH-IPC-2015-ensemble Indices des prix à la consommation harmonisés 2026-06-04 2026-06-04
insee IPGD-2015 Indice des prix dans la grande distribution 2026-06-04 2025-12-20
insee IPLA-IPLNA-2015 Indices des prix des logements neufs et Indices Notaires-Insee des prix des logements anciens 2026-06-04 2026-06-03
insee IPPI-2015 Indices de prix de production et d'importation dans l'industrie 2026-06-04 2026-06-04
insee IRL Indice pour la révision d’un loyer d’habitation 2026-01-08 2026-06-04
insee SERIES_LOYERS Variation des loyers 2026-06-04 2026-06-04
insee T_CONSO_EFF_FONCTION Consommation effective des ménages par fonction 2026-06-04 2025-12-22
insee bdf2017 Budget de famille 2017 2026-02-03 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 2026-06-04 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 2026-06-04 2026-01-27
insee tranches_unitesurbaines Poids de chaque tranche d’unités urbaines dans la consommation 2026-06-04 2026-01-27

LAST_COMPILE

LAST_COMPILE
2026-07-04

Last

Code 
CES %>%
  wave_to_date %>%
  group_by(date) %>%
  summarise(Nobs = n()) %>%
  arrange(desc(date)) %>%
  head(1) %>%
  print_table_conditional()
date Nobs
2023-12-01 105

Var_label

Code 
CES %>%
  group_by(Var, Var_label) %>%
  summarise(Nobs = n()) %>%
  arrange(-Nobs) %>%
  print_table_conditional()
Var Var_label Nobs
c1010 Inflation perceptions over the previous 12 months (qualitative) 675
c1020 Inflation perceptions over the previous 12 months (% change) 675
c1110 Inflation expectations over the next 12 months (qualitative) 675
c1120 Inflation expectations over the next 12 months (% change) 675
c1150 Inflation expectations/uncertainty 12 months ahead (probabilistic bins) 675
c1210 Inflation expectations 3 years ahead (qualitative) 675
c1220 Inflation expectations 3 years ahead (% change) 675

Breakdown_label

All

Code 
CES %>%
  group_by(Breakdown, Breakdown_label) %>%
  summarise(Nobs = n()) %>%
  arrange(-Nobs) %>%
  print_table_conditional()
Breakdown Breakdown_label Nobs
Age 18-34 years 315
Age 35-54 years 315
Age 55-70 years 315
Country BE 315
Country DE 315
Country EA6 315
Country ES 315
Country FR 315
Country IT 315
Country NL 315
Income 1 315
Income 2 315
Income 3 315
Income 4 315
Income 5 315

Country

Code 
CES %>%
  filter(Breakdown == "Country") %>%
  rename(geo = Breakdown_label) %>%
  left_join(geo, by = "geo") %>%
  group_by(geo, Geo) %>%
  summarise(Nobs = n()) %>%
  arrange(-Nobs) %>%
  print_table_conditional()
geo Geo Nobs
BE Belgium 315
DE Germany 315
EA6 NA 315
ES Spain 315
FR France 315
IT Italy 315
NL Netherlands 315

wave

Code 
CES %>%
  wave_to_date %>%
  group_by(date) %>%
  summarise(Nobs = n()) %>%
  arrange(desc(date)) %>%
  print_table_conditional()

All Europe (Wave)

% change

Mean

Code 
CES %>%
  wave_to_date %>%
  filter(Breakdown == "Wave",
         Var %in% c("c1020", "c1120", "c1220")) %>%
  transmute(date, Var_label, OBS_VALUE = Mean/100) %>%
  ggplot(.) + geom_line(aes(x = date, y = OBS_VALUE, color = Var_label)) + 
  theme_minimal() + xlab("") + ylab("Mean (%)") +
  scale_x_date(breaks = seq.Date(as.Date("2019-12-01"), Sys.Date(), "2 months"),
               labels = date_format("%b %y")) +
  scale_y_continuous(breaks = 0.01*seq(-20, 20, 1),
                     labels = percent_format(a = 1)) + 
  theme(legend.position = c(0.33, 0.90),
        axis.text.x = element_text(angle = 45, vjust = 1, hjust = 1),
        legend.title = element_blank())

Median

Code 
CES %>%
  wave_to_date %>%
  filter(Breakdown == "Wave",
         Var %in% c("c1020", "c1120", "c1220")) %>%
  transmute(date, Var_label, OBS_VALUE = Median/100) %>%
  ggplot(.) + geom_line(aes(x = date, y = OBS_VALUE, color = Var_label)) + 
  theme_minimal() + xlab("") + ylab("Median (%)") +
  scale_x_date(breaks = seq.Date(as.Date("2019-12-01"), Sys.Date(), "2 months"),
               labels = date_format("%b %y")) +
  scale_y_continuous(breaks = 0.01*seq(-20, 20, 1),
                     labels = percent_format(a = 1)) + 
  theme(legend.position = c(0.33, 0.90),
        axis.text.x = element_text(angle = 45, vjust = 1, hjust = 1),
        legend.title = element_blank())

qualitative

Net percentage

Code 
CES %>%
  wave_to_date %>%
  filter(Breakdown == "Wave",
         Var %in% c("c1010", "c1110", "c1210")) %>%
  transmute(date, Var_label, OBS_VALUE = Net_perc/100) %>%
  ggplot(.) + geom_line(aes(x = date, y = OBS_VALUE, color = Var_label)) + 
  theme_minimal() + xlab("") + ylab("Net_perc (%)") +
  scale_x_date(breaks = seq.Date(as.Date("2019-12-01"), Sys.Date(), "2 months"),
               labels = date_format("%b %y")) +
  scale_y_continuous(breaks = 0.01*seq(-20, 100, 5),
                     labels = percent_format(a = 1)) + 
  theme(legend.position = c(0.33, 0.90),
        axis.text.x = element_text(angle = 45, vjust = 1, hjust = 1),
        legend.title = element_blank())

Up

Code 
CES %>%
  wave_to_date %>%
  filter(Breakdown == "Wave",
         Var %in% c("c1010", "c1110", "c1210")) %>%
  transmute(date, Var_label, OBS_VALUE = Net_perc/100) %>%
  ggplot(.) + geom_line(aes(x = date, y = OBS_VALUE, color = Var_label)) + 
  theme_minimal() + xlab("") + ylab("Up (%)") +
  scale_x_date(breaks = seq.Date(as.Date("2019-12-01"), Sys.Date(), "2 months"),
               labels = date_format("%b %y")) +
  scale_y_continuous(breaks = 0.01*seq(-20, 100, 5),
                     labels = percent_format(a = 1)) + 
  theme(legend.position = c(0.33, 0.90),
        axis.text.x = element_text(angle = 45, vjust = 1, hjust = 1),
        legend.title = element_blank())

Down

Code 
CES %>%
  wave_to_date %>%
  filter(Breakdown == "Wave",
         Var %in% c("c1010", "c1110", "c1210")) %>%
  transmute(date, Var_label, OBS_VALUE = Down/100) %>%
  ggplot(.) + geom_line(aes(x = date, y = OBS_VALUE, color = Var_label)) + 
  theme_minimal() + xlab("") + ylab("Up (%)") +
  scale_x_date(breaks = seq.Date(as.Date("2019-12-01"), Sys.Date(), "2 months"),
               labels = date_format("%b %y")) +
  scale_y_continuous(breaks = 0.01*seq(-20, 100, 1),
                     labels = percent_format(a = 1)) + 
  theme(legend.position = c(0.33, 0.90),
        axis.text.x = element_text(angle = 45, vjust = 1, hjust = 1),
        legend.title = element_blank())

France, Germany, Italy, Spain

1-year

Mean

All

Code 
CES %>%
  wave_to_date %>%
  filter(Breakdown == "Country",
         Var == "c1020") %>%
  rename(geo = Breakdown_label) %>%
  left_join(geo, by = "geo") %>%
  left_join(colors, by = c("Geo" = "country")) %>%
  mutate(OBS_VALUE = Mean/100) %>%
  rename(Ref_area = Geo) %>%
  ggplot(.) + geom_line(aes(x = date, y = OBS_VALUE, color = color)) + 
  theme_minimal() + xlab("") + ylab("Inflation expectations over the next 12 months\n% change, Mean") +
  scale_x_date(breaks = seq.Date(as.Date("2019-12-01"), Sys.Date(), "2 months"),
               labels = date_format("%b %y")) +
  scale_y_continuous(breaks = 0.01*seq(-20, 20, 1),
                     labels = percent_format(a = 1)) + 
  scale_color_identity() + add_flags +
  theme(legend.position = c(0.75, 0.90),
        axis.text.x = element_text(angle = 45, vjust = 1, hjust = 1),
        legend.title = element_blank())

October 2021-

Code 
CES %>%
  wave_to_date %>%
  filter(Breakdown == "Country",
         Var == "c1020") %>%
  rename(geo = Breakdown_label) %>%
  left_join(geo, by = "geo") %>%
  left_join(colors, by = c("Geo" = "country")) %>%
  mutate(OBS_VALUE = Mean/100) %>%
  filter(date >= as.Date("2021-10-01")) %>%
  rename(Ref_area = Geo) %>%
  ggplot(.) + geom_line(aes(x = date, y = OBS_VALUE, color = color)) + 
  theme_minimal() + xlab("") + ylab("Inflation expectations over the next 12 months\n% change, Mean") +
  scale_x_date(breaks = seq.Date(as.Date("2019-12-01"), Sys.Date(), "1 month"),
               labels = date_format("%b %y")) +
  scale_y_continuous(breaks = 0.01*seq(-20, 20, 1),
                     labels = percent_format(a = 1)) + 
  scale_color_identity() + add_flags +
  theme(legend.position = c(0.75, 0.90),
        axis.text.x = element_text(angle = 45, vjust = 1, hjust = 1),
        legend.title = element_blank())

Median

All

Code 
CES %>%
  wave_to_date %>%
  filter(Breakdown == "Country",
         Var == "c1020") %>%
  rename(geo = Breakdown_label) %>%
  left_join(geo, by = "geo") %>%
  left_join(colors, by = c("Geo" = "country")) %>%
  mutate(OBS_VALUE = Median/100) %>%
  rename(Ref_area = Geo) %>%
  ggplot(.) + geom_line(aes(x = date, y = OBS_VALUE, color = color)) + 
  theme_minimal() + xlab("") + ylab("Inflation expectations over the next 12 months\n% change, Median") +
  scale_x_date(breaks = seq.Date(as.Date("2019-12-01"), Sys.Date(), "2 months"),
               labels = date_format("%b %y")) +
  scale_y_continuous(breaks = 0.01*seq(-20, 20, 1),
                     labels = percent_format(a = 1)) + 
  scale_color_identity() + add_flags +
  theme(legend.position = c(0.75, 0.90),
        axis.text.x = element_text(angle = 45, vjust = 1, hjust = 1),
        legend.title = element_blank())

October 2021-

Code 
CES %>%
  wave_to_date %>%
  filter(Breakdown == "Country",
         Var == "c1020") %>%
  rename(geo = Breakdown_label) %>%
  left_join(geo, by = "geo") %>%
  left_join(colors, by = c("Geo" = "country")) %>%
  mutate(OBS_VALUE = Median/100) %>%
  filter(date >= as.Date("2021-10-01")) %>%
  rename(Ref_area = Geo) %>%
  ggplot(.) + geom_line(aes(x = date, y = OBS_VALUE, color = color)) + 
  theme_minimal() + xlab("") + ylab("Inflation expectations over the next 12 months\n% change, Median") +
  scale_x_date(breaks = seq.Date(as.Date("2019-12-01"), Sys.Date(), "1 month"),
               labels = date_format("%b %y")) +
  scale_y_continuous(breaks = 0.01*seq(-20, 20, 1),
                     labels = percent_format(a = 1)) + 
  scale_color_identity() + add_flags +
  theme(legend.position = c(0.75, 0.90),
        axis.text.x = element_text(angle = 45, vjust = 1, hjust = 1),
        legend.title = element_blank())

3-year

Mean

All

Code 
CES %>%
  wave_to_date %>%
  filter(Breakdown == "Country",
         Var == "c1220") %>%
  rename(geo = Breakdown_label) %>%
  left_join(geo, by = "geo") %>%
  left_join(colors, by = c("Geo" = "country")) %>%
  mutate(OBS_VALUE = Mean/100) %>%
  rename(Ref_area = Geo) %>%
  ggplot(.) + geom_line(aes(x = date, y = OBS_VALUE, color = color)) + 
  theme_minimal() + xlab("") + ylab("Inflation expectations 3 years ahead\n% change, Mean") +
  scale_x_date(breaks = seq.Date(as.Date("2019-12-01"), Sys.Date(), "2 months"),
               labels = date_format("%b %y")) +
  scale_y_continuous(breaks = 0.01*seq(-20, 20, 1),
                     labels = percent_format(a = 1)) + 
  scale_color_identity() + add_flags +
  theme(legend.position = c(0.75, 0.90),
        axis.text.x = element_text(angle = 45, vjust = 1, hjust = 1),
        legend.title = element_blank())

October 2021-

Code 
CES %>%
  wave_to_date %>%
  filter(Breakdown == "Country",
         Var == "c1220",
         date >= as.Date("2021-10-01")) %>%
  rename(geo = Breakdown_label) %>%
  left_join(geo, by = "geo") %>%
  left_join(colors, by = c("Geo" = "country")) %>%
  mutate(OBS_VALUE = Mean/100) %>%
  rename(Ref_area = Geo) %>%
  ggplot(.) + geom_line(aes(x = date, y = OBS_VALUE, color = color)) + 
  theme_minimal() + xlab("") + ylab("Inflation expectations 3 years ahead\n% change, Mean") +
  scale_x_date(breaks = seq.Date(as.Date("2019-12-01"), Sys.Date(), "1 month"),
               labels = date_format("%b %y")) +
  scale_y_continuous(breaks = 0.01*seq(-20, 20, 1),
                     labels = percent_format(a = 1)) + 
  scale_color_identity() + add_flags +
  theme(legend.position = c(0.75, 0.90),
        axis.text.x = element_text(angle = 45, vjust = 1, hjust = 1),
        legend.title = element_blank())

Median

All

Code 
CES %>%
  wave_to_date %>%
  filter(Breakdown == "Country",
         Var == "c1220") %>%
  rename(geo = Breakdown_label) %>%
  left_join(geo, by = "geo") %>%
  left_join(colors, by = c("Geo" = "country")) %>%
  mutate(OBS_VALUE = Median/100) %>%
  rename(Ref_area = Geo) %>%
  ggplot(.) + geom_line(aes(x = date, y = OBS_VALUE, color = color)) + 
  theme_minimal() + xlab("") + ylab("Inflation expectations 3 years ahead\n% change, Median") +
  scale_x_date(breaks = seq.Date(as.Date("2019-12-01"), Sys.Date(), "2 months"),
               labels = date_format("%b %y")) +
  scale_y_continuous(breaks = 0.01*seq(-20, 20, .2),
                     labels = percent_format(a = .1)) + 
  scale_color_identity() + add_flags +
  theme(legend.position = c(0.75, 0.90),
        axis.text.x = element_text(angle = 45, vjust = 1, hjust = 1),
        legend.title = element_blank())

October 2021-

Code 
CES %>%
  wave_to_date %>%
  filter(Breakdown == "Country",
         Var == "c1220",
         date >= as.Date("2021-10-01")) %>%
  rename(geo = Breakdown_label) %>%
  left_join(geo, by = "geo") %>%
  left_join(colors, by = c("Geo" = "country")) %>%
  mutate(OBS_VALUE = Median/100) %>%
  rename(Ref_area = Geo) %>%
  ggplot(.) + geom_line(aes(x = date, y = OBS_VALUE, color = color)) + 
  theme_minimal() + xlab("") + ylab("Inflation expectations 3 years ahead\n% change, Median") +
  scale_x_date(breaks = seq.Date(as.Date("2019-12-01"), Sys.Date(), "1 month"),
               labels = date_format("%b %y")) +
  scale_y_continuous(breaks = 0.01*seq(-20, 20, .2),
                     labels = percent_format(a = .1)) + 
  scale_color_identity() + add_flags +
  theme(legend.position = c(0.75, 0.90),
        axis.text.x = element_text(angle = 45, vjust = 1, hjust = 1),
        legend.title = element_blank())