source | dataset | .html | .RData |
---|---|---|---|
insee | CHOMAGE-TRIM-NATIONAL | 2024-10-29 | 2024-11-05 |
Chômage, taux de chômage par sexe et âge (sens BIT) (1975-)
Data - Insee
Info
Données sur l’emploi
source | dataset | .html | .RData |
---|---|---|---|
insee | CHOMAGE-TRIM-NATIONAL | 2024-10-29 | 2024-11-05 |
insee | CNA-2014-EMPLOI | 2024-06-07 | 2024-11-05 |
insee | DEMANDES-EMPLOIS-NATIONALES | 2024-10-29 | 2024-11-05 |
insee | EMPLOI-BIT-TRIM | 2024-06-07 | 2024-11-05 |
insee | EMPLOI-SALARIE-TRIM-NATIONAL | 2024-10-29 | 2024-11-05 |
insee | TAUX-CHOMAGE | 2024-10-29 | 2024-11-05 |
insee | TCRED-EMPLOI-SALARIE-TRIM | 2024-10-29 | 2024-11-05 |
Data on employment
source | dataset | .html | .RData |
---|---|---|---|
bls | jt | 2024-05-01 | NA |
bls | la | 2024-06-19 | NA |
bls | ln | 2024-06-19 | NA |
eurostat | nama_10_a10_e | 2024-11-01 | 2024-11-05 |
eurostat | nama_10_a64_e | 2024-11-01 | 2024-10-08 |
eurostat | namq_10_a10_e | 2024-11-01 | 2024-10-08 |
eurostat | une_rt_m | 2024-10-24 | 2024-10-24 |
oecd | ALFS_EMP | 2024-04-16 | 2024-05-12 |
oecd | EPL_T | 2024-04-16 | 2023-12-10 |
oecd | LFS_SEXAGE_I_R | 2024-09-15 | 2024-04-15 |
oecd | STLABOUR | 2024-09-15 | 2024-06-30 |
LAST_UPDATE
Code
`CHOMAGE-TRIM-NATIONAL` %>%
group_by(LAST_UPDATE) %>%
summarise(Nobs = n()) %>%
arrange(-Nobs) %>%
print_table_conditional()
LAST_UPDATE | Nobs |
---|---|
2024-08-09 | 12976 |
2019-08-14 | 5634 |
INDICATEUR
Code
`CHOMAGE-TRIM-NATIONAL` %>%
left_join(INDICATEUR, by = "INDICATEUR") %>%
group_by(INDICATEUR, Indicateur) %>%
summarise(Nobs = n()) %>%
arrange(-Nobs) %>%
print_table_conditional()
INDICATEUR | Indicateur | Nobs |
---|---|---|
CTTXC | Taux de chômage | 4752 |
CTCHC | Nombre de chômeurs | 4512 |
HALO | Personnes dans le halo autour du chômage | 3768 |
CTTC15 | Part de chômeurs dans la population 15 ans et plus | 2888 |
TXCHLODU | Taux de chômage de longue durée | 1824 |
CHLODU | Nombre de chômeurs de longue durée | 152 |
HALO_1 | Personnes dans le halo autour du chômage : inactifs faisant des démarches actives de recherche d'emploi mais non disponibles | 152 |
HALO_2 | Personnes dans le halo autour du chômage : inactifs disponibles mais ne faisant pas de démarche active de recherche d'emploi | 152 |
HALO_3 | Personnes dans le halo autour du chômage : inactifs souhaitant travailler mais non disponibles et ne faisant pas de démarche active de recherche d'emploi | 152 |
PCONTR1 | Part des chômeurs parmi les participants au marché du travail de 15 à 64 ans | 86 |
PCONTR2 | Part des personnes au chômage ou dans le halo parmi les participants au marché du travail de 15 à 64 ans | 86 |
PCONTR3 | Part des personnes contraintes sur leur offre de travail parmi les participants au marché du travail de 15 à 64 ans | 86 |
TIME_PERIOD
Code
`CHOMAGE-TRIM-NATIONAL` %>%
group_by(TIME_PERIOD) %>%
summarise(Nobs = n()) %>%
arrange(desc(TIME_PERIOD)) %>%
print_table_conditional()
AGE
Code
`CHOMAGE-TRIM-NATIONAL` %>%
left_join(AGE, by = "AGE") %>%
group_by(AGE, Age) %>%
summarise(Nobs = n()) %>%
arrange(-Nobs) %>%
print_table_conditional()
AGE | Age | Nobs |
---|---|---|
25-49 | De 25 à 49 ans | 3942 |
00- | Ensemble | 3380 |
50- | 50 ans et plus | 3380 |
00-24 | Moins de 25 ans | 2772 |
15-64 | De 15 à 64 ans | 1428 |
15-24 | De 15 à 24 ans | 1170 |
50-64 | De 50 à 64 ans | 1170 |
15- | 15 ans et plus | 456 |
55-64 | De 55 à 64 ans | 456 |
SO | Sans objet | 456 |
NATURE
Code
`CHOMAGE-TRIM-NATIONAL` %>%
left_join(NATURE, by = "NATURE") %>%
group_by(NATURE, Nature) %>%
summarise(Nobs = n()) %>%
arrange(-Nobs) %>%
print_table_conditional()
NATURE | Nature | Nobs |
---|---|---|
VALEUR_ABSOLUE | Valeur absolue | 7856 |
TAUX | Taux | 6576 |
PROPORTION | Proportion | 4178 |
SEXE
Code
`CHOMAGE-TRIM-NATIONAL` %>%
left_join(SEXE, by = "SEXE") %>%
group_by(SEXE, Sexe) %>%
summarise(Nobs = n()) %>%
arrange(-Nobs) %>%
print_table_conditional()
SEXE | Sexe | Nobs |
---|---|---|
0 | Ensemble | 6882 |
1 | Hommes | 5864 |
2 | Femmes | 5864 |
UNIT_MULT
Code
`CHOMAGE-TRIM-NATIONAL` %>%
group_by(UNIT_MULT) %>%
summarise(Nobs = n()) %>%
arrange(-Nobs) %>%
if (is_html_output()) print_table(.) else .} {
UNIT_MULT | Nobs |
---|---|
0 | 10754 |
3 | 7856 |
REF_AREA
Code
`CHOMAGE-TRIM-NATIONAL` %>%
group_by(REF_AREA) %>%
summarise(Nobs = n()) %>%
arrange(-Nobs) %>%
if (is_html_output()) print_table(.) else .} {
REF_AREA | Nobs |
---|---|
FR-D976 | 10600 |
FM | 8010 |
Personnes dans le Halo du chômage
Code
`CHOMAGE-TRIM-NATIONAL` %>%
filter(grepl("halo", TITLE_FR)) %>%
group_by(IDBANK, TITLE_FR) %>%
summarise(Nobs = n()) %>%
arrange(-Nobs) %>%
if (is_html_output()) datatable(., filter = 'top', rownames = F) else .} {
Halo du chomage
Ensemble
Code
`CHOMAGE-TRIM-NATIONAL` %>%
filter(IDBANK %in% c("010605056",
"010605048",
"010605049",
"010605050")) %>%
select(AGE, TITLE_FR, TIME_PERIOD, OBS_VALUE) %>%
mutate(OBS_VALUE = OBS_VALUE %>% as.numeric,
TITLE_FR = TITLE_FR %>% gsub("\\(en milliers\\) - France hors Mayotte - Données CVS", "", .),
TITLE_FR = TITLE_FR %>% gsub("Personnes dans le halo autour du chômage - ", "", .)) %>%
%>%
quarter_to_date ggplot() + geom_line(aes(x = date, y = OBS_VALUE, color = TITLE_FR, linetype = TITLE_FR)) +
theme_minimal() +
scale_x_date(breaks = seq(1920, 2025, 2) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
theme(legend.position = c(0.6, 0.85),
legend.title = element_blank()) +
scale_y_continuous(breaks = seq(0, 9000, 500),
labels = dollar_format(accuracy = 1, prefix = "", su = " K")) +
ylab("Halo du Chômage (en milliers)") + xlab("")
Inactifs
Code
`CHOMAGE-TRIM-NATIONAL` %>%
filter(INDICATEUR %in% c("CHLODU", "HALO_1", "HALO_2", "HALO_3"),
== "FR-D976") %>%
REF_AREA %>%
quarter_to_date left_join(INDICATEUR, by = "INDICATEUR") %>%
mutate(Indicateur = gsub("Personnes dans le halo autour du chômage : ", "", Indicateur)) %>%
arrange(date) %>%
select(date, OBS_VALUE, INDICATEUR, Indicateur) %>%
ggplot() + ylab("Halo du Chômage (en milliers)") + xlab("") + theme_minimal() +
geom_line(aes(x = date, y = OBS_VALUE, color = Indicateur, linetype = Indicateur)) +
scale_x_date(breaks = seq(1920, 2025, 2) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
theme(legend.position = c(0.55, 0.85),
legend.title = element_blank()) +
scale_y_continuous(breaks = seq(0, 9000, 200),
labels = dollar_format(accuracy = 1, prefix = ""),
limits = c(0, 2000))
Nombre de chômeurs
Tous
Code
`CHOMAGE-TRIM-NATIONAL` %>%
filter(INDICATEUR == "CTCHC",
== "00-",
AGE == "0") %>%
SEXE %>%
quarter_to_date left_join(REF_AREA, by = "REF_AREA") %>%
ggplot() + theme_minimal() + ylab("Nombre de chômeurs") + xlab("") +
geom_line(aes(x = date, y = OBS_VALUE, color = Ref_area)) +
scale_x_date(breaks = seq(1920, 2025, 5) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
theme(legend.position = c(0.8, 0.15),
legend.title = element_blank()) +
scale_y_continuous(breaks = seq(0, 3000, 500),
labels = dollar_format(accuracy = 1, pre = "", su = " K"))
Taux de chômage
Tous
Code
`CHOMAGE-TRIM-NATIONAL` %>%
filter(INDICATEUR == "CTTXC",
== "00-",
AGE == "0") %>%
SEXE %>%
quarter_to_date left_join(REF_AREA, by = "REF_AREA") %>%
mutate(OBS_VALUE = OBS_VALUE/100) %>%
ggplot() + theme_minimal() + ylab("Taux de chômage (%)") + xlab("") +
geom_line(aes(x = date, y = OBS_VALUE, color = Ref_area)) +
scale_x_date(breaks = seq(1920, 2025, 5) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
theme(legend.position = c(0.8, 0.15),
legend.title = element_blank()) +
scale_y_continuous(breaks = 0.01*seq(0, 20, 1),
labels = percent_format(accuracy = 1)) +
geom_hline(yintercept = 0.05, linetype = "dashed")
1990-
Code
`CHOMAGE-TRIM-NATIONAL` %>%
filter(INDICATEUR == "CTTXC",
== "00-",
AGE == "0") %>%
SEXE %>%
quarter_to_date filter(date >= as.Date("1990-01-01")) %>%
left_join(REF_AREA, by = "REF_AREA") %>%
mutate(OBS_VALUE = OBS_VALUE/100) %>%
ggplot() + theme_minimal() + ylab("Taux de chômage (%)") + xlab("") +
geom_line(aes(x = date, y = OBS_VALUE, color = Ref_area)) +
scale_x_date(breaks = seq(1920, 2025, 5) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
theme(legend.position = c(0.8, 0.15),
legend.title = element_blank()) +
scale_y_continuous(breaks = 0.01*seq(0, 20, 1),
labels = percent_format(accuracy = 1))
Genre
Tous
Code
`CHOMAGE-TRIM-NATIONAL` %>%
filter(INDICATEUR == "CTTXC",
== "FR-D976",
REF_AREA == "00-") %>%
AGE %>%
quarter_to_date left_join(SEXE, by = "SEXE") %>%
ggplot() + theme_minimal() + ylab("Taux de chômage (%)") + xlab("") +
geom_line(aes(x = date, y = OBS_VALUE/100, color = Sexe, linetype = Sexe)) +
scale_color_manual(values = c("black", "red", "blue")) +
scale_x_date(breaks = seq(1920, 2025, 5) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
theme(legend.position = c(0.8, 0.3),
legend.title = element_blank()) +
scale_y_continuous(breaks = 0.01*seq(0, 20, 1),
labels = percent_format(accuracy = 1))
2005-
Code
`CHOMAGE-TRIM-NATIONAL` %>%
filter(INDICATEUR == "CTTXC",
== "FR-D976",
REF_AREA == "00-") %>%
AGE %>%
quarter_to_date filter(date >= as.Date("2005-01-01")) %>%
left_join(SEXE, by = "SEXE") %>%
ggplot() + theme_minimal() + ylab("Taux de chômage (%)") + xlab("") +
geom_line(aes(x = date, y = OBS_VALUE/100, color = Sexe, linetype = Sexe)) +
scale_color_manual(values = c("black", "red", "blue")) +
scale_x_date(breaks = seq(1920, 2025, 1) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
theme(legend.position = c(0.85, 0.9),
legend.title = element_blank()) +
scale_y_continuous(breaks = 0.01*seq(0, 20, .5),
labels = percent_format(accuracy = .1))
2010-
Code
`CHOMAGE-TRIM-NATIONAL` %>%
filter(INDICATEUR == "CTTXC",
== "FR-D976",
REF_AREA == "00-") %>%
AGE %>%
quarter_to_date filter(date >= as.Date("2010-01-01")) %>%
left_join(SEXE, by = "SEXE") %>%
ggplot() + theme_minimal() + ylab("Taux de chômage (%)") + xlab("") +
geom_line(aes(x = date, y = OBS_VALUE/100, color = Sexe, linetype = Sexe)) +
scale_color_manual(values = c("black", "red", "blue")) +
scale_x_date(breaks = seq(1920, 2025, 1) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
theme(legend.position = c(0.8, 0.9),
legend.title = element_blank()) +
scale_y_continuous(breaks = 0.01*seq(0, 20, .5),
labels = percent_format(accuracy = .1))
Nombre de chômeurs
All
Code
`CHOMAGE-TRIM-NATIONAL` %>%
filter(INDICATEUR %in% c("CTCHC", "CHLODU"),
== "0",
SEXE == "00-24") %>%
AGE %>%
quarter_to_date left_join(REF_AREA, by = "REF_AREA") %>%
ggplot() + theme_minimal() + ylab("Nombre de chômeurs") + xlab("") +
geom_line(aes(x = date, y = OBS_VALUE*1000, color = Ref_area)) +
scale_x_date(breaks = seq(1920, 2025, 5) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
theme(legend.position = c(0.8, 0.88),
legend.title = element_blank()) +
scale_y_continuous(breaks = 1000*seq(100, 1100, 50),
labels = dollar_format(pre = ""))
1990-
Code
`CHOMAGE-TRIM-NATIONAL` %>%
filter(INDICATEUR == "CTCHC",
== "0",
SEXE == "00-24") %>%
AGE %>%
quarter_to_date filter(date >= as.Date("1990-01-01")) %>%
left_join(REF_AREA, by = "REF_AREA") %>%
ggplot() + theme_minimal() + ylab("Nombre de chômeurs") + xlab("") +
geom_line(aes(x = date, y = OBS_VALUE*1000, color = Ref_area)) +
scale_x_date(breaks = seq(1920, 2025, 5) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
theme(legend.position = c(0.4, 0.88),
legend.title = element_blank()) +
scale_y_continuous(breaks = 1000*seq(100, 1100, 50),
labels = dollar_format(pre = ""))
Age (Tous)
Tous
Code
`CHOMAGE-TRIM-NATIONAL` %>%
filter(INDICATEUR == "CTTXC",
== "FR-D976",
REF_AREA == "0") %>%
SEXE %>%
quarter_to_date left_join(AGE, by = "AGE") %>%
ggplot() + theme_minimal() + ylab("Taux de chômage (%)") + xlab("") +
geom_line(aes(x = date, y = OBS_VALUE/100, color = Age)) +
scale_x_date(breaks = seq(1920, 2025, 5) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
theme(legend.position = c(0.12, 0.88),
legend.title = element_blank()) +
scale_y_continuous(breaks = 0.01*seq(0, 40, 5),
labels = percent_format(accuracy = 1))
2005-
Code
`CHOMAGE-TRIM-NATIONAL` %>%
filter(INDICATEUR == "CTTXC",
== "FR-D976",
REF_AREA == "0") %>%
SEXE %>%
quarter_to_date filter(date >= as.Date("2005-01-01")) %>%
left_join(AGE, by = "AGE") %>%
ggplot() + theme_minimal() + ylab("Taux de chômage (%)") + xlab("") +
geom_line(aes(x = date, y = OBS_VALUE/100, color = Age)) +
scale_x_date(breaks = seq(1920, 2025, 1) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
theme(legend.position = c(0.9, 0.9),
legend.title = element_blank()) +
scale_y_continuous(breaks = 0.01*seq(0, 40, 2),
labels = percent_format(accuracy = 1))
2010-
Code
`CHOMAGE-TRIM-NATIONAL` %>%
filter(INDICATEUR == "CTTXC",
== "FR-D976",
REF_AREA == "0") %>%
SEXE %>%
quarter_to_date filter(date >= as.Date("2005-01-01")) %>%
left_join(AGE, by = "AGE") %>%
ggplot() + theme_minimal() + ylab("Taux de chômage (%)") + xlab("") +
geom_line(aes(x = date, y = OBS_VALUE/100, color = Age)) +
scale_x_date(breaks = seq(1920, 2025, 1) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
theme(legend.position = c(0.9, 0.9),
legend.title = element_blank()) +
scale_y_continuous(breaks = 0.01*seq(0, 40, 2),
labels = percent_format(accuracy = 1))
2017-
Code
`CHOMAGE-TRIM-NATIONAL` %>%
filter(INDICATEUR == "CTTXC",
== "FR-D976",
REF_AREA == "0") %>%
SEXE %>%
quarter_to_date filter(date >= as.Date("2017-01-01")) %>%
left_join(AGE, by = "AGE") %>%
ggplot() + theme_minimal() + ylab("Taux de chômage (%)") + xlab("") +
geom_line(aes(x = date, y = OBS_VALUE/100, color = Age)) +
scale_x_date(breaks = "6 months",
labels = date_format("%b %y")) +
theme(legend.position = c(0.9, 0.9),
legend.title = element_blank()) +
scale_y_continuous(breaks = 0.01*seq(0, 40, 2),
labels = percent_format(accuracy = 1))
Age (sauf - de 25 ans)
Tous
Code
`CHOMAGE-TRIM-NATIONAL` %>%
filter(INDICATEUR == "CTTXC",
== "FR-D976",
REF_AREA == "0",
SEXE != "00-24") %>%
AGE %>%
quarter_to_date left_join(AGE, by = "AGE") %>%
ggplot() + theme_minimal() + ylab("Taux de chômage (%)") + xlab("") +
geom_line(aes(x = date, y = OBS_VALUE/100, color = Age)) +
scale_x_date(breaks = seq(1920, 2025, 5) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
theme(legend.position = c(0.15, 0.9),
legend.title = element_blank()) +
scale_y_continuous(breaks = 0.01*seq(0, 40, 1),
labels = percent_format(accuracy = 1),
limits = c(0, 0.13))
2005-
Code
`CHOMAGE-TRIM-NATIONAL` %>%
filter(INDICATEUR == "CTTXC",
== "FR-D976",
REF_AREA == "0",
SEXE != "00-24") %>%
AGE %>%
quarter_to_date filter(date >= as.Date("2005-01-01")) %>%
left_join(AGE, by = "AGE") %>%
ggplot() + theme_minimal() + ylab("Taux de chômage (%)") + xlab("") +
geom_line(aes(x = date, y = OBS_VALUE/100, color = Age)) +
scale_x_date(breaks = seq(1920, 2025, 1) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
theme(legend.position = c(0.9, 0.9),
legend.title = element_blank()) +
scale_y_continuous(breaks = 0.01*seq(0, 40, 1),
labels = percent_format(accuracy = 1))
2010-
Code
`CHOMAGE-TRIM-NATIONAL` %>%
filter(INDICATEUR == "CTTXC",
== "FR-D976",
REF_AREA == "0",
SEXE != "00-24") %>%
AGE %>%
quarter_to_date filter(date >= as.Date("2005-01-01")) %>%
left_join(AGE, by = "AGE") %>%
ggplot() + theme_minimal() + ylab("Taux de chômage (%)") + xlab("") +
geom_line(aes(x = date, y = OBS_VALUE/100, color = Age)) +
scale_x_date(breaks = seq(1920, 2025, 1) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
theme(legend.position = c(0.9, 0.9),
legend.title = element_blank()) +
scale_y_continuous(breaks = 0.01*seq(0, 40, 1),
labels = percent_format(accuracy = 1))
2017-
Code
`CHOMAGE-TRIM-NATIONAL` %>%
filter(INDICATEUR == "CTTXC",
== "FR-D976",
REF_AREA == "0",
SEXE != "00-24") %>%
AGE %>%
quarter_to_date filter(date >= as.Date("2017-04-01")) %>%
left_join(AGE, by = "AGE") %>%
ggplot() + theme_minimal() + ylab("Taux de chômage (%)") + xlab("") +
geom_line(aes(x = date, y = OBS_VALUE/100, color = Age)) +
scale_x_date(breaks = seq(1920, 2025, 1) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
theme(legend.position = c(0.9, 0.9),
legend.title = element_blank()) +
scale_y_continuous(breaks = 0.01*seq(0, 40, 1),
labels = percent_format(accuracy = 1))