Dépenses de consommation des ménages - volumes aux prix de l’année précédente chaînés (données CVS-CJO), base 2014 - t_conso_vol

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: 24 sept. 2026, 01:46

variable

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

Evolution

2017T2-

Code 
t_conso_vol |>
  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 -2.571 -4.864 89.19
IZ Hébergement-restauration 22.109 29.922 35.34
MN Services aux entreprises 7.729 10.164 31.50
C3 Biens d'équipement 8.198 10.389 26.73
RU Services aux ménages 11.780 14.713 24.90
JZ Information-communication 11.002 13.288 20.78
(GZ) à (MN), (RU) Services marchands 146.035 173.326 18.69
(FZ) à (RU) Total services 168.013 197.136 17.33
LZ Services immobiliers 63.523 72.669 14.40
HZ Transport 9.865 11.086 12.38
FZ Construction 5.689 6.301 10.76
KZ Services financiers 18.780 20.221 7.67
OQ Services non marchands 16.289 17.538 7.67
TOTAL Total 308.625 330.454 7.07
GZ Commerce 1.252 1.328 6.07
C4 Matériels de transport 16.765 17.305 3.22
C5 Autres produits industriels 37.255 37.618 0.97
C Produits manufacturés 120.347 117.649 -2.24
(DE) à (C5) Industrie 134.520 131.189 -2.48
DE Energie, eau, déchets 14.171 13.558 -4.33
C1 Produits agro-alimentaires 45.963 42.762 -6.96
AZ Produits agricoles 8.699 7.417 -14.74
C2 Cokéfaction et raffinage 12.019 9.902 -17.61

2019T4-

Code 
t_conso_vol |>
  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
C2 Cokéfaction et raffinage 11.730 9.902 -15.58
AZ Produits agricoles 8.517 7.417 -12.92
C1 Produits agro-alimentaires 45.006 42.762 -4.99
DE Energie, eau, déchets 14.116 13.558 -3.95
(DE) à (C5) Industrie 135.897 131.189 -3.46
C Produits manufacturés 121.773 117.649 -3.39
C4 Matériels de transport 17.759 17.305 -2.56
GZ Commerce 1.344 1.328 -1.19
C5 Autres produits industriels 37.946 37.618 -0.86
TOTAL Total 318.063 330.454 3.90
KZ Services financiers 19.227 20.221 5.17
OQ Services non marchands 16.594 17.538 5.69
FZ Construction 5.880 6.301 7.16
HZ Transport 10.178 11.086 8.92
LZ Services immobiliers 66.251 72.669 9.69
(FZ) à (RU) Total services 177.125 197.136 11.30
(GZ) à (MN), (RU) Services marchands 154.649 173.326 12.08
JZ Information-communication 11.834 13.288 12.29
C3 Biens d'équipement 9.076 10.389 14.47
RU Services aux ménages 12.599 14.713 16.78
MN Services aux entreprises 8.441 10.164 20.41
IZ Hébergement-restauration 24.805 29.922 20.63
(CHTR) Correction territoriale -3.506 -4.864 38.73

2 years

Code 
t_conso_vol |>
  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 -3.527 -4.864 37.91
C3 Biens d'équipement 9.657 10.389 7.58
MN Services aux entreprises 9.646 10.164 5.37
RU Services aux ménages 14.088 14.713 4.44
AZ Produits agricoles 7.158 7.417 3.62
HZ Transport 10.760 11.086 3.03
IZ Hébergement-restauration 29.088 29.922 2.87
(GZ) à (MN), (RU) Services marchands 168.911 173.326 2.61
LZ Services immobiliers 70.835 72.669 2.59
(FZ) à (RU) Total services 192.671 197.136 2.32
JZ Information-communication 13.019 13.288 2.07
C4 Matériels de transport 16.960 17.305 2.03
TOTAL Total 326.613 330.454 1.18
OQ Services non marchands 17.351 17.538 1.08
C5 Autres produits industriels 37.274 37.618 0.92
KZ Services financiers 20.088 20.221 0.66
C Produits manufacturés 116.927 117.649 0.62
C1 Produits agro-alimentaires 42.533 42.762 0.54
(DE) à (C5) Industrie 130.680 131.189 0.39
GZ Commerce 1.337 1.328 -0.67
DE Energie, eau, déchets 13.735 13.558 -1.29
FZ Construction 6.443 6.301 -2.20
C2 Cokéfaction et raffinage 10.611 9.902 -6.68

Last year

Code 
t_conso_vol |>
  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 -4.115 -4.864 18.20
C3 Biens d'équipement 9.738 10.389 6.69
C4 Matériels de transport 16.454 17.305 5.17
MN Services aux entreprises 9.902 10.164 2.65
RU Services aux ménages 14.362 14.713 2.44
C5 Autres produits industriels 37.082 37.618 1.45
OQ Services non marchands 17.321 17.538 1.25
GZ Commerce 1.312 1.328 1.22
LZ Services immobiliers 71.818 72.669 1.18
DE Energie, eau, déchets 13.422 13.558 1.01
AZ Produits agricoles 7.348 7.417 0.94
(FZ) à (RU) Total services 195.766 197.136 0.70
(GZ) à (MN), (RU) Services marchands 172.154 173.326 0.68
KZ Services financiers 20.140 20.221 0.40
TOTAL Total 329.426 330.454 0.31
(DE) à (C5) Industrie 130.841 131.189 0.27
C Produits manufacturés 117.468 117.649 0.15
HZ Transport 11.070 11.086 0.14
FZ Construction 6.317 6.301 -0.25
JZ Information-communication 13.399 13.288 -0.83
IZ Hébergement-restauration 30.182 29.922 -0.86
C1 Produits agro-alimentaires 43.510 42.762 -1.72
C2 Cokéfaction et raffinage 10.718 9.902 -7.61

Consommation, Services financiers

2017-

Code 
t_conso_vol |>
  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, 0.3)) +
  scale_x_date(breaks = seq(1950, 2100, 1) |> paste0("-01-01") |> as.Date(),
               labels = date_format("%Y")) +
  scale_y_log10(breaks = seq(70, 1000, 1))

Produits agricoles

1949-

Code 
t_conso_vol |>
  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 = seq(100, 1000, 10))

Alimentation, C1, Totale

1949-

Code 
t_conso_vol |>
  filter(variable %in% c("AZ", "C1", "(DE) à (C5)")) |>
  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(0.7, 0.3)) +
  scale_x_date(breaks = seq(1950, 2100, 5) |> paste0("-01-01") |> as.Date(),
               labels = date_format("%Y")) +
  scale_y_log10(breaks = seq(100, 1000, 50))

Consommation alimentaire

1949-

Code 
t_conso_vol |>
  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(0.7, 0.3)) +
  scale_x_date(breaks = seq(1950, 2100, 5) |> paste0("-01-01") |> as.Date(),
               labels = date_format("%Y")) +
  scale_y_log10(breaks = seq(100, 1000, 50))

2010-

Code 
t_conso_vol |>
  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, 0.3)) +
  scale_x_date(breaks = seq(1950, 2100, 1) |> paste0("-01-01") |> as.Date(),
               labels = date_format("%Y")) +
  scale_y_log10(breaks = seq(70, 1000, 1))

2017-

Code 
t_conso_vol |>
  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, 0.3)) +
  scale_x_date(breaks = seq(1950, 2100, 1) |> paste0("-01-01") |> as.Date(),
               labels = date_format("%Y")) +
  scale_y_log10(breaks = seq(70, 1000, 1))