Valeur ajoutée par branche - Volume (CVS-CJO) - t_va_vol

Données - INSEE

Auteur

François Geerolf

Last observation: Q2 2026 (N = 22)

First observation: Q1 1949 (N = 22)

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

Last compile: 24 sept. 2026, 01:50

Comptes trimestriels : valeur ajoutée par branche, en volume.

Structure

Principales branches

Code 
`t_va_vol` |>
  filter(variable %in% big) |>
  ggplot() + theme_minimal() + xlab("") + ylab("Valeur ajoutée (volume)") +
  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_vol` |>
  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_vol` |>
  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_vol` |>
  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 C2 C3 C4 C5 FZ GZ HZ IZ JZ KZ LZ MN OQ RU
2026-04-01 7.7 10.1 63.7 9.5 1.9 8.4 11.0 33.5 30.0 54.2 28.0 17.0 38.2 21.8 81.3 88.3 131.6 22.8
2026-01-01 7.9 10.1 63.5 9.4 1.9 8.4 10.9 33.4 30.1 54.3 28.1 17.0 38.3 22.0 81.1 88.0 131.1 22.6
2025-10-01 8.3 10.2 63.2 9.3 1.8 8.4 10.8 33.3 30.7 54.8 28.2 17.1 38.1 22.1 81.0 87.7 130.8 22.6
2025-07-01 8.4 10.1 63.4 9.3 1.8 8.4 10.9 33.4 30.7 54.8 27.9 17.1 38.1 22.1 80.8 86.9 130.6 22.6
2025-04-01 8.3 9.7 63.0 9.4 1.5 8.4 10.6 33.3 30.6 54.5 28.2 17.1 37.5 22.1 80.5 86.3 130.4 22.6
2025-01-01 8.0 10.0 62.9 9.7 1.5 8.4 10.0 33.4 30.5 54.2 28.1 16.9 37.3 22.2 80.2 86.1 130.3 22.8
2024-10-01 7.7 10.0 62.6 9.6 1.3 8.4 9.8 33.5 30.6 54.3 28.2 16.9 37.2 22.2 79.9 85.7 130.3 22.8