Emploi par branche - Personnes physiques - t_emploi_pp

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 personnes physiques.

Structure

Principales branches

Code 
`t_emploi_pp` |>
  filter(variable %in% big) |>
  ggplot() + theme_minimal() + xlab("") + ylab("Emploi (milliers)") +
  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_pp` |>
  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_pp` |>
  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_pp` |>
  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
2026-04-01 362.0 303.0 2610.1 654.8 6.5 320.2 209.6 1419.0 1577.4 3265.2 1444.8 1319.7 1007.6 794.6 339.3 4199.6 8323.6 1519.1
2026-01-01 367.6 301.6 2614.5 653.9 6.5 321.1 209.3 1423.7 1584.5 3265.0 1445.9 1315.5 1011.0 793.5 339.7 4212.5 8317.0 1515.1
2025-10-01 362.7 300.1 2619.7 652.7 6.5 322.0 210.0 1428.5 1590.7 3267.3 1445.4 1316.4 1015.7 792.0 340.0 4224.9 8320.9 1518.9
2025-07-01 362.1 299.1 2624.2 652.2 6.4 322.6 210.1 1432.9 1594.6 3275.5 1442.5 1315.1 1021.7 791.1 340.9 4237.4 8322.5 1484.7
2025-04-01 367.2 297.9 2630.3 651.4 6.4 323.5 211.8 1437.2 1598.3 3279.9 1440.3 1303.6 1026.6 790.4 341.5 4245.9 8316.2 1517.5
2025-01-01 366.4 296.7 2635.3 649.6 6.4 323.5 213.4 1442.3 1604.0 3281.8 1438.9 1294.8 1031.4 789.1 341.3 4247.6 8317.1 1506.6
2024-10-01 369.1 295.6 2636.3 647.2 6.3 323.4 212.4 1446.9 1611.1 3284.3 1436.4 1295.0 1037.1 788.1 341.4 4259.9 8325.5 1504.6