Emploi par branche - Équivalents temps plein - t_emploi_etp

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, 01:47

Comptes trimestriels : emploi par branche, en équivalents temps plein.

Structure

Principales branches

Code 
`t_emploi_etp` |>
  filter(variable %in% big) |>
  ggplot() + theme_minimal() + xlab("") + ylab("Emploi (milliers d'ETP)") +
  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_emploi_etp` |>
  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_emploi_etp` |>
  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_emploi_etp` |>
  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
2026-04-01 353.1 296.9 2541.5 626.9 6.4 314.2 205.9 1388.1 1537.9 3046.2 1381.3 1150.8 984.0 770.9 322.6 3942.4 7650.2 1353.6
2026-01-01 358.0 295.4 2545.8 626.1 6.4 315.1 205.7 1392.5 1544.9 3045.8 1382.3 1145.9 987.2 769.8 323.0 3953.6 7643.9 1349.4
2025-10-01 352.5 294.0 2551.0 625.1 6.4 316.0 206.4 1397.2 1551.1 3048.4 1381.8 1147.1 991.9 768.2 323.4 3965.7 7647.1 1351.6
2025-07-01 351.4 293.0 2555.5 624.6 6.4 316.7 206.5 1401.3 1554.6 3056.7 1378.7 1147.6 997.9 767.2 324.4 3979.3 7648.5 1325.3
2025-04-01 355.9 291.8 2561.0 623.8 6.3 317.6 208.1 1405.2 1557.9 3060.6 1376.4 1138.2 1002.9 766.5 325.3 3987.5 7642.8 1349.3
2025-01-01 354.8 290.6 2565.7 622.1 6.3 317.6 209.7 1410.0 1563.0 3061.7 1375.0 1131.2 1007.7 765.4 325.6 3990.6 7644.4 1340.2
2024-10-01 357.1 289.6 2566.5 619.9 6.3 317.4 208.8 1414.1 1569.2 3064.6 1372.5 1134.1 1013.6 764.5 326.2 4005.9 7653.6 1338.7