Valeur ajoutée par branche - Valeurs aux prix courants - t_va_val

Données - INSEE

Auteur

François Geerolf

Last observation: Q2 2026 (N = 21)

First observation: Q1 1949 (N = 21)

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

Last compile: 24 sept. 2026, 01:50

Comptes trimestriels : valeur ajoutée par branche, à prix courants.

Structure

Principales branches

Code 
`t_va_val` |>
  filter(variable %in% big) |>
  ggplot() + theme_minimal() + xlab("") + ylab("Valeur ajoutée (Mds €)") +
  geom_line(aes(x = date, y = value, color = a17_label(variable))) +
  scale_x_date(date_breaks = "10 years", date_labels = "%Y") +
  theme(legend.title = element_blank(), legend.position = "bottom") +
  guides(color = guide_legend(nrow = 2))

Industrie manufacturière — glissement annuel

Code 
`t_va_val` |>
  filter(variable == "C") |>
  arrange(date) |> mutate(g = value / lag(value, 4) - 1) |>
  filter(date >= as.Date("1980-01-01")) |>
  ggplot() + theme_minimal() + xlab("") + ylab("Glissement annuel") +
  geom_hline(yintercept = 0, color = "grey60") +
  geom_line(aes(x = date, y = g)) +
  scale_x_date(date_breaks = "10 years", date_labels = "%Y") +
  scale_y_continuous(labels = percent_format(accuracy = 1))

Répartition par branche

Code 
`t_va_val` |>
  filter(grepl("^[A-Z][A-Z0-9]?$", variable), variable != "C") |>
  group_by(date) |> mutate(part = value / sum(value)) |> ungroup() |>
  arrange(desc(variable)) |> mutate(variable = factor(a17_label(variable), levels = unique(a17_label(variable)))) |>
  ggplot() + theme_minimal() + xlab("") + ylab("Part du total") +
  geom_area(aes(x = date, y = part, fill = variable)) +
  scale_x_date(date_breaks = "10 years", date_labels = "%Y") +
  scale_y_continuous(labels = percent_format(accuracy = 1)) +
  scale_fill_viridis_d() +
  theme(legend.title = element_blank(), legend.position = "bottom") +
  guides(fill = guide_legend(nrow = 3))

Derniers trimestres

Code 
`t_va_val` |>
  filter(grepl("^[A-Z][A-Z0-9]?$", variable), date >= last_d %m-% months(18)) |>
  mutate(value = round(value, 1)) |>
  tidyr::pivot_wider(names_from = variable, values_from = value) |>
  arrange(desc(date)) |>
  print_table_conditional()
date AZ DE C C1 C3 C4 C5 FZ GZ HZ IZ JZ KZ LZ MN OQ RU
2026-04-01 9.7 13.7 76.2 11.5 9.4 10.9 43.2 36.6 62.4 31.4 19.1 37.8 25.9 94.5 100.1 147.7 21.1
2026-01-01 10.6 14.1 75.5 11.5 9.5 10.8 43.1 37.2 61.8 30.5 18.7 37.5 25.9 94.3 99.6 146.9 21.1
2025-10-01 10.8 14.8 75.4 11.9 9.4 11.0 42.7 37.8 62.4 30.8 18.2 37.5 24.6 94.8 99.0 145.9 21.1
2025-07-01 10.7 14.8 74.7 11.8 9.4 11.0 42.0 37.4 62.7 30.5 17.8 37.2 24.5 94.1 97.5 145.4 21.0
2025-04-01 10.3 13.9 75.0 12.2 9.5 10.9 41.9 37.0 62.7 30.8 17.6 36.4 23.9 94.0 96.2 144.8 21.1
2025-01-01 9.6 15.7 75.1 12.4 9.5 10.8 41.9 36.7 62.8 31.6 17.5 36.3 23.3 93.9 95.8 144.1 20.8
2024-10-01 9.4 18.5 74.3 12.8 9.4 10.5 41.3 36.4 62.7 32.1 17.5 36.1 21.8 93.7 95.2 142.7 20.7