PIB et ses composantes - Equilibre emplois-ressources - volumes aux prix de l’année précédente chaînés - t_pib_vol

Data - Insee

Author

François Geerolf

Info

source dataset .html .RData
insee t_pib_val 2025-05-24 2025-02-28
insee t_pib_vol 2025-05-18 2025-02-28

Info

  • Comptes trimestriels. html

  • PIB et ses composantes. html

  • Secteurs et ses composantes. html

png

Code 
ig_b("insee", "t_pib_vol")

variable

Code 
t_pib_vol %>%
  left_join(variable, by = "variable") %>%
  group_by(variable, Variable1, Variable2) %>%
  summarise(Nobs = n()) %>%
  arrange(-Nobs) %>%
  print_table_conditional
variable Variable1 Variable2 Nobs
P3 Dépenses de consommation des : Total 304
P31G Dépenses de consommation des : APU (individual.) 304
P32G Dépenses de consommation des : APU (collectives) 304
P3M Dépenses de consommation des : ménages 304
P3P Dépenses de consommation des : ISBLSM 304
P51 FBCF des : Total 304
P51B FBCF des : entreprises financières 304
P51G FBCF des : APU 304
P51M FBCF des : ménages 304
P51P FBCF des : ISBLSM 304
P51S FBCF des : entreprises non financières 304
P6 Exportations NA 304
P7 Importations NA 304
PIB Produit intérieur brut NA 304

PIB

Code 
t_pib_vol %>%
  filter(variable == "PIB") %>%
  select(-variable) %>%
  print_table_conditional()

PIB

propension à importer = M(2016) - M(2014)/ (PIB+M)(2016)- (PIB+M)(2014)

Code 
t_pib_vol %>%
  filter(variable %in% c("PIB", "P7")) %>%
  spread(variable, value) %>%
  mutate(PIB_P7 = PIB+P7) %>%
  mutate(import_propensity = (P7 - lag(P7, 10))/(PIB_P7 - lag(PIB_P7, 10))) %>%
  ggplot() + geom_line(aes(x = date, y = import_propensity)) +
  xlab("") + ylab("Propension à importeer (%)") + theme_minimal() +
  theme(legend.title = element_blank(),
        legend.position = c(0.8, 0.2)) +
  scale_x_date(breaks = seq(1920, 2025, 5) %>% paste0("-01-01") %>% as.Date(),
               labels = date_format("%Y")) +
  scale_y_continuous(breaks = 0.01*seq(-200, 200, 10),
                     labels = scales::percent_format(accuracy = 1))

Code 
t_pib_vol %>%
  filter(variable %in% c("PIB", "P7", "P3M")) %>%
  spread(variable, value) %>%
  mutate(PIB_P7 = PIB+P7) %>%
  mutate(import_propensity = (P3M - lag(P3M, 10))/(PIB_P7 - lag(PIB_P7, 10))) %>%
  ggplot() + geom_line(aes(x = date, y = import_propensity)) +
  xlab("") + ylab("Propension à consommer (%)") + theme_minimal() +
  theme(legend.title = element_blank(),
        legend.position = c(0.8, 0.2)) +
  scale_x_date(breaks = seq(1920, 2025, 5) %>% paste0("-01-01") %>% as.Date(),
               labels = date_format("%Y")) +
  scale_y_continuous(breaks = 0.01*seq(-200, 200, 10),
                     labels = scales::percent_format(accuracy = 1),
                     limits = c(-2, 1.5))

Déflateur

P3M, P51M

Tous

Code 
t_pib_vol %>%
  filter(variable %in% c("P3M", "P51M")) %>%
  left_join(variable, by = "variable") %>%
  rename(vol = value) %>%
  left_join(t_pib_val, by = c("date", "variable")) %>%
  rename(val = value) %>%
  mutate(deflateur = val/vol) %>%
  #filter(date >= as.Date("1990-01-01")) %>%
  group_by(variable) %>%
  arrange(date) %>%
  mutate(deflateur = 100*deflateur/deflateur[1]) %>%
  ggplot + theme_minimal() + ylab("") + xlab("") +
  geom_line(aes(x = date, y = deflateur, color = paste(Variable1, Variable2))) +
  theme(legend.title = element_blank(),
        legend.position = c(0.3, 0.9)) +
  scale_x_date(breaks = seq(1950, 2100, 5) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  scale_y_log10(breaks = seq(0, 10000, 100))

1975-

Code 
t_pib_vol %>%
  filter(variable %in% c("P3M", "P51M")) %>%
  left_join(variable, by = "variable") %>%
  rename(vol = value) %>%
  left_join(t_pib_val, by = c("date", "variable")) %>%
  rename(val = value) %>%
  mutate(deflateur = val/vol) %>%
  filter(date >= as.Date("1975-01-01")) %>%
  group_by(variable) %>%
  arrange(date) %>%
  mutate(deflateur = 100*deflateur/deflateur[1]) %>%
  ggplot + theme_minimal() + ylab("") + xlab("") +
  geom_line(aes(x = date, y = deflateur, color = paste(Variable1, Variable2))) +
  theme(legend.title = element_blank(),
        legend.position = c(0.3, 0.9)) +
  scale_x_date(breaks = seq(1950, 2100, 5) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  scale_y_log10(breaks = seq(0, 1000, 10))

1990-

Code 
t_pib_vol %>%
  filter(variable %in% c("P3M", "P51M")) %>%
  left_join(variable, by = "variable") %>%
  rename(vol = value) %>%
  left_join(t_pib_val, by = c("date", "variable")) %>%
  rename(val = value) %>%
  mutate(deflateur = val/vol) %>%
  filter(date >= as.Date("1990-01-01")) %>%
  group_by(variable) %>%
  arrange(date) %>%
  mutate(deflateur = 100*deflateur/deflateur[1]) %>%
  ggplot + theme_minimal() + ylab("") + xlab("") +
  geom_line(aes(x = date, y = deflateur, color = paste(Variable1, Variable2))) +
  theme(legend.title = element_blank(),
        legend.position = c(0.3, 0.9)) +
  scale_x_date(breaks = seq(1950, 2100, 5) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  scale_y_log10(breaks = seq(0, 1000, 10))

1999-

Code 
t_pib_vol %>%
  filter(variable %in% c("P3M", "P51M")) %>%
  left_join(variable, by = "variable") %>%
  rename(vol = value) %>%
  left_join(t_pib_val, by = c("date", "variable")) %>%
  rename(val = value) %>%
  mutate(deflateur = val/vol) %>%
  filter(date >= as.Date("1999-01-01")) %>%
  group_by(variable) %>%
  arrange(date) %>%
  mutate(deflateur = 100*deflateur/deflateur[1]) %>%
  mutate(Variable = case_when(variable == "P3M" ~ "Déflateur de la consommation des ménages",
                              variable == "P51M" ~ "Déflateur de l'investissement des ménages")) %>%
  ggplot + theme_minimal() + ylab("") + xlab("") +
  geom_line(aes(x = date, y = deflateur, color =Variable)) +
  theme(legend.title = element_blank(),
        legend.position = c(0.3, 0.9)) +
  scale_x_date(breaks = seq(1999, 2100, 5) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  scale_y_log10(breaks = seq(0, 1000, 10)) +
  geom_label(data = . %>% filter(date == max(date)),
             aes(x = date, y = deflateur, color = Variable, label = round(deflateur, 1)))

1999-2024T1

Code 
t_pib_vol %>%
  filter(variable %in% c("P3M", "P51M")) %>%
  left_join(variable, by = "variable") %>%
  rename(vol = value) %>%
  left_join(t_pib_val, by = c("date", "variable")) %>%
  rename(val = value) %>%
  mutate(deflateur = val/vol) %>%
  filter(date >= as.Date("1999-01-01"),
         date <= as.Date("2024-01-01")) %>%
  group_by(variable) %>%
  arrange(date) %>%
  mutate(deflateur = 100*deflateur/deflateur[1]) %>%
  mutate(Variable = case_when(variable == "P3M" ~ "Déflateur de la consommation des ménages",
                              variable == "P51M" ~ "Déflateur de l'investissement des ménages")) %>%
  ggplot + theme_minimal() + ylab("") + xlab("") +
  geom_line(aes(x = date, y = deflateur, color =Variable)) +
  theme(legend.title = element_blank(),
        legend.position = c(0.3, 0.9)) +
  scale_x_date(breaks = seq(1999, 2100, 5) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  scale_y_log10(breaks = seq(0, 1000, 10)) +
  geom_label(data = . %>% filter(date == max(date)),
             aes(x = date, y = deflateur, color = Variable, label = round(deflateur, 1)))

P3M, P51M, P3M, P51M

1975-

Code 
t_pib_vol %>%
  filter(variable %in% c("P3M", "P51M")) %>%
  spread(variable, value) %>%
  mutate(`P3M + P51M` = P3M + P51M) %>%
  gather(variable, value, -date) %>%
  rename(vol = value) %>%
  left_join(t_pib_val %>%
              filter(variable %in% c("P3M", "P51M")) %>%
              spread(variable, value) %>%
              mutate(`P3M + P51M` = P3M + P51M) %>%
              gather(variable, value, -date),
            by = c("date", "variable")) %>%
  rename(val = value) %>%
  mutate(deflateur = val/vol) %>%
  filter(date >= as.Date("1975-01-01")) %>%
  group_by(variable) %>%
  arrange(date) %>%
  mutate(deflateur = 100*deflateur/deflateur[1]) %>%
  ggplot + theme_minimal() + ylab("") + xlab("") +
  geom_line(aes(x = date, y = deflateur, color = variable)) +
  theme(legend.title = element_blank(),
        legend.position = c(0.3, 0.9)) +
  scale_x_date(breaks = seq(1950, 2100, 5) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  scale_y_log10(breaks = seq(0, 1000, 10))

1990-

Code 
t_pib_vol %>%
  filter(variable %in% c("P3M", "P51M")) %>%
  spread(variable, value) %>%
  mutate(`P3M + P51M` = P3M + P51M) %>%
  gather(variable, value, -date) %>%
  rename(vol = value) %>%
  left_join(t_pib_val %>%
              filter(variable %in% c("P3M", "P51M")) %>%
              spread(variable, value) %>%
              mutate(`P3M + P51M` = P3M + P51M) %>%
              gather(variable, value, -date),
            by = c("date", "variable")) %>%
  rename(val = value) %>%
  mutate(deflateur = val/vol) %>%
  filter(date >= as.Date("1990-01-01")) %>%
  group_by(variable) %>%
  arrange(date) %>%
  mutate(deflateur = 100*deflateur/deflateur[1]) %>%
  ggplot + theme_minimal() + ylab("") + xlab("") +
  geom_line(aes(x = date, y = deflateur, color = variable)) +
  theme(legend.title = element_blank(),
        legend.position = c(0.3, 0.9)) +
  scale_x_date(breaks = seq(1950, 2100, 5) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  scale_y_log10(breaks = seq(0, 1000, 10))

1999-

All

Code 
t_pib_vol %>%
  filter(variable %in% c("P3M", "P51M")) %>%
  spread(variable, value) %>%
  mutate(`P3M + P51M` = P3M + P51M) %>%
  gather(variable, value, -date) %>%
  rename(vol = value) %>%
  left_join(t_pib_val %>%
              filter(variable %in% c("P3M", "P51M")) %>%
              spread(variable, value) %>%
              mutate(`P3M + P51M` = P3M + P51M) %>%
              gather(variable, value, -date),
            by = c("date", "variable")) %>%
  rename(val = value) %>%
  mutate(deflateur = val/vol) %>%
  filter(date >= as.Date("1999-01-01")) %>%
  group_by(variable) %>%
  arrange(date) %>%
  mutate(deflateur = 100*deflateur/deflateur[1]) %>%
  ggplot + theme_minimal() + ylab("") + xlab("") +
  geom_line(aes(x = date, y = deflateur, color = variable)) +
  theme(legend.title = element_blank(),
        legend.position = c(0.3, 0.9)) +
  scale_x_date(breaks = seq(1999, 2100, 5) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  scale_y_log10(breaks = seq(0, 1000, 10)) +
  geom_label_repel(data = . %>% filter(date == max(date)),
             aes(x = date, y = deflateur, color = variable, label = round(deflateur, 1)))

Consommation

All

Linear

Code 
t_pib_vol %>%
  filter(variable %in% c("P3M", "PIB", "P3")) %>%
  ggplot(.) + theme_minimal() + ylab("") + xlab("") +
  geom_line(aes(x = date, y = value, color = variable)) +
  theme(legend.title = element_blank(),
        legend.position = c(0.3, 0.9)) +
  scale_x_date(breaks = seq(1950, 2100, 10) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  scale_y_continuous(breaks = seq(0, 1000, 50))

Log

Code 
t_pib_vol %>%
  filter(variable %in% c("P3M", "PIB", "P3")) %>%
  ggplot(.) + theme_minimal() + ylab("") + xlab("") +
  geom_line(aes(x = date, y = value, color = variable)) +
  theme(legend.title = element_blank(),
        legend.position = c(0.3, 0.9)) +
  scale_x_date(breaks = seq(1950, 2100, 10) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  scale_y_log10(breaks = seq(0, 1000, 50))

2017-T2

Log

Code 
t_pib_vol %>%
  filter(variable %in% c("P3M", "PIB", "P3"),
         date >= as.Date("2017-04-01")) %>%
  group_by(variable) %>%
  arrange(date) %>%
  mutate(value = 100*value/value[1]) %>%
  ggplot(.) + theme_minimal() + ylab("") + xlab("") +
  geom_line(aes(x = date, y = value, color = variable)) +
  theme(legend.title = element_blank(),
        legend.position = c(0.3, 0.9)) +
  scale_x_date(breaks = seq(1950, 2100, 1) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  scale_y_log10(breaks = seq(0, 1000, 2))