Consumer Expectations Survey (EA6) - CES_EA6

Data - ECB

Author

François Geerolf

Info

Last data update: 02 aoû 2026, 08:37. Last compile: 17 aoû 2026, 23:20

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())