Dépenses de consommation des ménages aux prix courants (données CVS-CJO) - t_conso_val

Données - INSEE

Auteur

François Geerolf

Last observation: Q2 2026 (N = 23)

First observation: Q1 1949 (N = 23)

Last data update: 03 sept. 2026, 05:31

Last compile: 16 sept. 2026, 20:50

variable

Code 
t_conso_val |>
  left_join(variable, by = "variable") |>
  group_by(variable, Variable) |>
  summarise(Nobs = n()) |>
  arrange(-Nobs) |>
  print_table_conditional()
variable Variable Nobs
(CHTR) Correction territoriale 310
(DE) à (C5) Industrie 310
(FZ) à (RU) Total services 310
(GZ) à (MN), (RU) Services marchands 310
AZ Produits agricoles 310
C Produits manufacturés 310
C1 Produits agro-alimentaires 310
C2 Cokéfaction et raffinage 310
C3 Biens d'équipement 310
C4 Matériels de transport 310
C5 Autres produits industriels 310
DE Energie, eau, déchets 310
FZ Construction 310
GZ Commerce 310
HZ Transport 310
IZ Hébergement-restauration 310
JZ Information-communication 310
KZ Services financiers 310
LZ Services immobiliers 310
MN Services aux entreprises 310
OQ Services non marchands 310
RU Services aux ménages 310
TOTAL Total 310

date

Code 
t_conso_val |>
  group_by(date) |>
  summarise(Nobs = n()) |>
  arrange(desc(date)) |>
  print_table_conditional()

Evolution

2017T2-

Code 
t_conso_val |>
  left_join(variable, by = "variable") |>
  filter(date %in% c(max(date), as.Date("2017-04-01"))) |>
  spread(date, value) %>%
  mutate(change = round(100*(.[[4]]/.[[3]]-1), 2)) |>
  arrange(-change) |>
  print_table_conditional()
variable Variable 2017-04-01 2026-04-01 change
(CHTR) Correction territoriale -2.451 -5.760 135.01
KZ Services financiers 17.975 31.546 75.50
IZ Hébergement-restauration 20.953 36.131 72.44
MN Services aux entreprises 7.387 11.855 60.48
GZ Commerce 1.146 1.717 49.83
RU Services aux ménages 11.285 16.758 48.50
HZ Transport 9.588 14.063 46.67
DE Energie, eau, déchets 13.182 19.147 45.25
(GZ) à (MN), (RU) Services marchands 141.932 204.590 44.15
(FZ) à (RU) Total services 163.155 232.495 42.50
FZ Construction 5.370 7.609 41.69
C2 Cokéfaction et raffinage 11.662 15.620 33.94
TOTAL Total 297.770 395.214 32.72
OQ Services non marchands 15.853 20.296 28.03
C1 Produits agro-alimentaires 42.302 53.543 26.57
LZ Services immobiliers 62.436 78.890 26.35
(DE) à (C5) Industrie 129.523 159.212 22.92
AZ Produits agricoles 7.542 9.266 22.86
JZ Information-communication 11.163 13.632 22.12
C4 Matériels de transport 16.396 19.989 21.91
C Produits manufacturés 116.341 140.065 20.39
C5 Autres produits industriels 37.037 41.070 10.89
C3 Biens d'équipement 8.944 9.842 10.04

2019T4-

Code 
t_conso_val |>
  left_join(variable, by = "variable") |>
  filter(date %in% c(max(date), as.Date("2019-10-01"))) |>
  spread(date, value) %>%
  mutate(change = round(100*(.[[4]]/.[[3]]-1), 2)) |>
  arrange(change) |>
  print_table_conditional()
variable Variable 2019-10-01 2026-04-01 change
C3 Biens d'équipement 9.168 9.842 7.35
C5 Autres produits industriels 37.746 41.070 8.81
C4 Matériels de transport 17.757 19.989 12.57
C Produits manufacturés 122.199 140.065 14.62
AZ Produits agricoles 8.062 9.266 14.93
JZ Information-communication 11.846 13.632 15.08
(DE) à (C5) Industrie 136.278 159.212 16.83
C2 Cokéfaction et raffinage 13.200 15.620 18.33
LZ Services immobiliers 66.101 78.890 19.35
C1 Produits agro-alimentaires 44.328 53.543 20.79
OQ Services non marchands 16.567 20.296 22.51
TOTAL Total 317.398 395.214 24.52
GZ Commerce 1.324 1.717 29.68
FZ Construction 5.839 7.609 30.31
(FZ) à (RU) Total services 176.521 232.495 31.71
(GZ) à (MN), (RU) Services marchands 154.115 204.590 32.75
RU Services aux ménages 12.520 16.758 33.85
DE Energie, eau, déchets 14.079 19.147 36.00
HZ Transport 10.204 14.063 37.82
MN Services aux entreprises 8.432 11.855 40.60
IZ Hébergement-restauration 24.622 36.131 46.74
KZ Services financiers 19.066 31.546 65.46
(CHTR) Correction territoriale -3.464 -5.760 66.28

2 years

Code 
t_conso_val |>
  left_join(variable, by = "variable") |>
  filter(date %in% c(max(date), max(date) - years(2))) |>
  spread(date, value) %>%
  mutate(change = round(100*(.[[4]]/.[[3]]-1), 2)) |>
  arrange(-change) |>
  print_table_conditional()
variable Variable 2024-04-01 2026-04-01 change
(CHTR) Correction territoriale -4.125 -5.760 39.64
IZ Hébergement-restauration 33.351 36.131 8.34
MN Services aux entreprises 10.995 11.855 7.82
C2 Cokéfaction et raffinage 14.564 15.620 7.25
HZ Transport 13.131 14.063 7.10
RU Services aux ménages 15.656 16.758 7.04
KZ Services financiers 29.672 31.546 6.32
(GZ) à (MN), (RU) Services marchands 192.716 204.590 6.16
OQ Services non marchands 19.137 20.296 6.06
AZ Produits agricoles 8.737 9.266 6.05
(FZ) à (RU) Total services 219.296 232.495 6.02
LZ Services immobiliers 74.632 78.890 5.71
C4 Matériels de transport 19.177 19.989 4.23
TOTAL Total 379.256 395.214 4.21
GZ Commerce 1.649 1.717 4.12
C Produits manufacturés 135.792 140.065 3.15
C1 Produits agro-alimentaires 51.993 53.543 2.98
C3 Biens d'équipement 9.563 9.842 2.92
(DE) à (C5) Industrie 155.347 159.212 2.49
FZ Construction 7.444 7.609 2.22
C5 Autres produits industriels 40.494 41.070 1.42
JZ Information-communication 13.629 13.632 0.02
DE Energie, eau, déchets 19.555 19.147 -2.09

Last year

Code 
t_conso_val |>
  left_join(variable, by = "variable") |>
  filter(date %in% c(max(date), max(date) - years(1))) |>
  spread(date, value) %>%
  mutate(change = round(100*(.[[4]]/.[[3]]-1), 2)) |>
  arrange(-change) |>
  print_table_conditional()
variable Variable 2025-04-01 2026-04-01 change
(CHTR) Correction territoriale -4.840 -5.760 19.01
C2 Cokéfaction et raffinage 13.444 15.620 16.19
C4 Matériels de transport 18.866 19.989 5.95
C3 Biens d'équipement 9.356 9.842 5.19
KZ Services financiers 30.050 31.546 4.98
MN Services aux entreprises 11.351 11.855 4.44
RU Services aux ménages 16.264 16.758 3.04
C Produits manufacturés 136.014 140.065 2.98
(DE) à (C5) Industrie 154.918 159.212 2.77
OQ Services non marchands 19.753 20.296 2.75
(GZ) à (MN), (RU) Services marchands 199.199 204.590 2.71
GZ Commerce 1.672 1.717 2.69
(FZ) à (RU) Total services 226.438 232.495 2.67
TOTAL Total 385.675 395.214 2.47
LZ Services immobiliers 77.067 78.890 2.37
IZ Hébergement-restauration 35.381 36.131 2.12
C5 Autres produits industriels 40.375 41.070 1.72
HZ Transport 13.832 14.063 1.67
FZ Construction 7.487 7.609 1.63
DE Energie, eau, déchets 18.904 19.147 1.29
AZ Produits agricoles 9.159 9.266 1.17
JZ Information-communication 13.581 13.632 0.38
C1 Produits agro-alimentaires 53.972 53.543 -0.79

Consommation, Services financiers

2017-

Code 
t_conso_val |>
  filter(variable %in% c("AZ", "C1", "KZ"),
         date >= as.Date("2017-01-01")) |>
  group_by(variable) |>
  arrange(date) |>
  mutate(value = 100*value/value[1]) |>
  left_join(variable, by = "variable") |>
  ggplot() + geom_line(aes(x = date, y = value, color = Variable)) +
  theme_minimal() + ylab("Consommation trimestrielle") + xlab("") +
  theme(legend.title = element_blank(),
        legend.position = c(0.2, 0.8)) +
  scale_x_date(breaks = seq(1950, 2100, 1) |> paste0("-01-01") |> as.Date(),
               labels = date_format("%Y")) +
  scale_y_log10(breaks = seq(70, 1000, 5))

Produits agricoles

1949-

Code 
t_conso_val |>
  filter(variable %in% c("AZ")) |>
  group_by(variable) |>
  arrange(date) |>
  mutate(value = 100*value/value[1]) |>
  left_join(variable, by = "variable") |>
  ggplot() + geom_line(aes(x = date, y = value)) +
  theme_minimal() + ylab("Consommation trimestrielle de produits agricoles") + xlab("") +
  theme(legend.title = element_blank(),
        legend.position = c(0.2, 0.9)) +
  scale_x_date(breaks = seq(1950, 2100, 5) |> paste0("-01-01") |> as.Date(),
               labels = date_format("%Y")) +
  scale_y_log10(breaks = c(100, 200, 500, 800, 1000, 2000, 5000, 10000))

Consommation alimentaire

1949-

Code 
t_conso_val |>
  filter(variable %in% c("AZ", "C1")) |>
  group_by(variable) |>
  arrange(date) |>
  mutate(value = 100*value/value[1]) |>
  left_join(variable, by = "variable") |>
  ggplot() + geom_line(aes(x = date, y = value, color = Variable)) +
  theme_minimal() + ylab("Consommation trimestrielle") + xlab("") +
  theme(legend.title = element_blank(),
        legend.position = c(0.2, 0.8)) +
  scale_x_date(breaks = seq(1950, 2100, 5) |> paste0("-01-01") |> as.Date(),
               labels = date_format("%Y")) +
  scale_y_log10(breaks = c(100, 200, 500, 800, 1000, 2000, 5000, 10000))

2010-

Code 
t_conso_val |>
  filter(variable %in% c("AZ", "C1"),
         date >= as.Date("2010-01-01")) |>
  group_by(variable) |>
  arrange(date) |>
  mutate(value = 100*value/value[1]) |>
  left_join(variable, by = "variable") |>
  ggplot() + geom_line(aes(x = date, y = value, color = Variable)) +
  theme_minimal() + ylab("Consommation trimestrielle") + xlab("") +
  theme(legend.title = element_blank(),
        legend.position = c(0.2, 0.8)) +
  scale_x_date(breaks = seq(1950, 2100, 1) |> paste0("-01-01") |> as.Date(),
               labels = date_format("%Y")) +
  scale_y_log10(breaks = seq(70, 1000, 5))

2017-

Code 
t_conso_val |>
  filter(variable %in% c("AZ", "C1"),
         date >= as.Date("2017-01-01")) |>
  group_by(variable) |>
  arrange(date) |>
  mutate(value = 100*value/value[1]) |>
  left_join(variable, by = "variable") |>
  ggplot() + geom_line(aes(x = date, y = value, color = Variable)) +
  theme_minimal() + ylab("Consommation trimestrielle") + xlab("") +
  theme(legend.title = element_blank(),
        legend.position = c(0.2, 0.8)) +
  scale_x_date(breaks = seq(1950, 2100, 1) |> paste0("-01-01") |> as.Date(),
               labels = date_format("%Y")) +
  scale_y_log10(breaks = seq(70, 1000, 2))