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

Données - INSEE

Auteur

François Geerolf

Info

Last observation: 2025-10-01 (N = 23)

First observation: 1949-01-01 (N = 23)

Last data update: 02 aoû 2026, 11:05. Last compile: 18 aoû 2026, 02:55

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 308
(DE) à (C5) Industrie 308
(FZ) à (RU) Total services 308
(GZ) à (MN), (RU) Services marchands 308
AZ Produits agricoles 308
C Produits manufacturés 308
C1 Produits agro-alimentaires 308
C2 Cokéfaction et raffinage 308
C3 Biens d'équipement 308
C4 Matériels de transport 308
C5 Autres produits industriels 308
DE Energie, eau, déchets 308
FZ Construction 308
GZ Commerce 308
HZ Transport 308
IZ Hébergement-restauration 308
JZ Information-communication 308
KZ Services financiers 308
LZ Services immobiliers 308
MN Services aux entreprises 308
OQ Services non marchands 308
RU Services aux ménages 308
TOTAL Total 308

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 2025-10-01 change
(CHTR) Correction territoriale -2.454 -5.057 106.07
KZ Services financiers 17.969 32.073 78.49
IZ Hébergement-restauration 20.957 34.400 64.15
MN Services aux entreprises 7.385 11.774 59.43
GZ Commerce 1.145 1.689 47.51
RU Services aux ménages 11.284 16.384 45.20
DE Energie, eau, déchets 13.198 19.077 44.54
(GZ) à (MN), (RU) Services marchands 141.924 201.631 42.07
HZ Transport 9.590 13.574 41.54
FZ Construction 5.370 7.568 40.93
(FZ) à (RU) Total services 163.144 228.645 40.15
TOTAL Total 297.736 388.447 30.47
LZ Services immobiliers 62.418 78.156 25.21
C1 Produits agro-alimentaires 42.304 52.673 24.51
AZ Produits agricoles 7.542 9.379 24.36
OQ Services non marchands 15.850 19.446 22.69
JZ Information-communication 11.177 13.582 21.52
(DE) à (C5) Industrie 129.503 155.479 20.06
C2 Cokéfaction et raffinage 11.658 13.788 18.27
C Produits manufacturés 116.305 136.403 17.28
C4 Matériels de transport 16.387 18.981 15.83
C5 Autres produits industriels 37.028 41.289 11.51
C3 Biens d'équipement 8.929 9.670 8.30

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 2025-10-01 change
C2 Cokéfaction et raffinage 13.193 13.788 4.51
C3 Biens d'équipement 9.182 9.670 5.31
C4 Matériels de transport 17.761 18.981 6.87
C5 Autres produits industriels 37.738 41.289 9.41
C Produits manufacturés 122.189 136.403 11.63
(DE) à (C5) Industrie 136.271 155.479 14.10
JZ Information-communication 11.855 13.582 14.57
AZ Produits agricoles 8.058 9.379 16.39
OQ Services non marchands 16.590 19.446 17.22
LZ Services immobiliers 66.061 78.156 18.31
C1 Produits agro-alimentaires 44.314 52.673 18.86
TOTAL Total 317.815 388.447 22.22
GZ Commerce 1.324 1.689 27.57
(FZ) à (RU) Total services 176.506 228.645 29.54
FZ Construction 5.836 7.568 29.68
(GZ) à (MN), (RU) Services marchands 154.080 201.631 30.86
RU Services aux ménages 12.520 16.384 30.86
HZ Transport 10.198 13.574 33.10
DE Energie, eau, déchets 14.082 19.077 35.47
MN Services aux entreprises 8.434 11.774 39.60
IZ Hébergement-restauration 24.626 34.400 39.69
(CHTR) Correction territoriale -3.020 -5.057 67.45
KZ Services financiers 19.061 32.073 68.27

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 2023-10-01 2025-10-01 change
(CHTR) Correction territoriale -3.385 -5.057 49.39
MN Services aux entreprises 10.748 11.774 9.55
IZ Hébergement-restauration 31.469 34.400 9.31
HZ Transport 12.432 13.574 9.19
RU Services aux ménages 15.182 16.384 7.92
DE Energie, eau, déchets 17.744 19.077 7.51
LZ Services immobiliers 73.277 78.156 6.66
(GZ) à (MN), (RU) Services marchands 190.236 201.631 5.99
(FZ) à (RU) Total services 216.175 228.645 5.77
OQ Services non marchands 18.573 19.446 4.70
GZ Commerce 1.637 1.689 3.18
TOTAL Total 376.461 388.447 3.18
FZ Construction 7.365 7.568 2.76
AZ Produits agricoles 9.132 9.379 2.70
C5 Autres produits industriels 40.754 41.289 1.31
JZ Information-communication 13.456 13.582 0.94
C3 Biens d'équipement 9.604 9.670 0.69
(DE) à (C5) Industrie 154.540 155.479 0.61
C1 Produits agro-alimentaires 52.403 52.673 0.52
KZ Services financiers 32.035 32.073 0.12
C Produits manufacturés 136.796 136.403 -0.29
C4 Matériels de transport 19.271 18.981 -1.50
C2 Cokéfaction et raffinage 14.765 13.788 -6.62

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 2024-10-01 2025-10-01 change
(CHTR) Correction territoriale -3.760 -5.057 34.49
KZ Services financiers 29.624 32.073 8.27
MN Services aux entreprises 11.368 11.774 3.57
(GZ) à (MN), (RU) Services marchands 195.055 201.631 3.37
(FZ) à (RU) Total services 221.553 228.645 3.20
GZ Commerce 1.637 1.689 3.18
LZ Services immobiliers 75.821 78.156 3.08
IZ Hébergement-restauration 33.428 34.400 2.91
AZ Produits agricoles 9.162 9.379 2.37
OQ Services non marchands 19.058 19.446 2.04
HZ Transport 13.304 13.574 2.03
RU Services aux ménages 16.085 16.384 1.86
FZ Construction 7.440 7.568 1.72
TOTAL Total 383.080 388.447 1.40
C3 Biens d'équipement 9.564 9.670 1.11
C5 Autres produits industriels 40.855 41.289 1.06
C Produits manufacturés 136.045 136.403 0.26
C1 Produits agro-alimentaires 52.579 52.673 0.18
(DE) à (C5) Industrie 156.125 155.479 -0.41
C4 Matériels de transport 19.108 18.981 -0.66
C2 Cokéfaction et raffinage 13.939 13.788 -1.08
JZ Information-communication 13.789 13.582 -1.50
DE Energie, eau, déchets 20.080 19.077 -5.00

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))