Incidence of full-time and part-time employment based on OECD-harmonized definition

Data - OECD

Author

François Geerolf

Info

source dataset .html .RData
oecd FTPT_COMMON_INC 2025-08-25 2025-09-28

Données sur l’emploi

Title source dataset .html .RData
Chômage, taux de chômage par sexe et âge (sens BIT) (1975-) insee CHOMAGE-TRIM-NATIONAL 2025-09-26 2025-09-26
Emploi intérieur, durée effective travaillée et productivité horaire insee CNA-2014-EMPLOI 2025-09-26 2025-09-26
Demandeurs d'emploi inscrits à Pôle Emploi insee DEMANDES-EMPLOIS-NATIONALES 2025-09-26 2025-09-26
Emploi, activité, sous-emploi par secteur d’activité (sens BIT) insee EMPLOI-BIT-TRIM 2025-09-26 2025-09-26
Estimations d'emploi salarié par secteur d'activité insee EMPLOI-SALARIE-TRIM-NATIONAL 2025-09-26 2025-09-26
Taux de chômage localisé insee TAUX-CHOMAGE 2025-09-26 2025-09-26
Estimations d'emploi salarié par secteur d'activité et par département insee TCRED-EMPLOI-SALARIE-TRIM 2025-09-26 2025-09-26

REF_AREA

Code 
FTPT_COMMON_INC %>%
  left_join(REF_AREA, by = "REF_AREA") %>%
  group_by(REF_AREA, Ref_area) %>%
  summarise(Nobs = n()) %>%
  arrange(-Nobs) %>%
  print_table_conditional()

SEX

Code 
FTPT_COMMON_INC %>%
  left_join(SEX, by = "SEX") %>%
  group_by(SEX, Sex) %>%
  summarise(Nobs = n()) %>%
  arrange(-Nobs) %>%
  print_table_conditional()
SEX Sex Nobs
M Male 160096
F Female 159768
_T Total 106982

AGE

Code 
FTPT_COMMON_INC %>%
  left_join(AGE, by = "AGE") %>%
  group_by(AGE, Age) %>%
  summarise(Nobs = n()) %>%
  arrange(-Nobs) %>%
  print_table_conditional()
AGE Age Nobs
_T Total 28120
Y55T64 From 55 to 64 years 28006
Y25T54 From 25 to 54 years 28000
Y15T24 From 15 to 24 years 27994
Y15T64 From 15 to 64 years 27940
Y_GE65 65 years or over 26552
Y25T29 From 25 to 29 years 22808
Y40T44 From 40 to 44 years 22805
Y20T24 From 20 to 24 years 22802
Y45T49 From 45 to 49 years 22802
Y35T39 From 35 to 39 years 22796
Y50T54 From 50 to 54 years 22796
Y30T34 From 30 to 34 years 22793
Y55T59 From 55 to 59 years 22792
Y60T64 From 60 to 64 years 22772
Y15T19 From 15 to 19 years 22632
Y65T69 From 65 to 69 years 10633
Y70T74 From 70 to 74 years 9697
Y_GE75 75 years or over 7214
Y_GE55 55 years or over 2152
Y65T74 From 65 to 74 years 2000
Y_GE70 70 years or over 740

MEASURE

Code 
FTPT_COMMON_INC %>%
  left_join(MEASURE, by = "MEASURE") %>%
  group_by(MEASURE, Measure) %>%
  summarise(Nobs = n()) %>%
  arrange(-Nobs) %>%
  print_table_conditional()
MEASURE Measure Nobs
EMP Employment 320149
EMP_PT Part-time employment 106697

WORKER_STATUS

Code 
FTPT_COMMON_INC %>%
  left_join(WORKER_STATUS, by = "WORKER_STATUS") %>%
  group_by(WORKER_STATUS, Worker_status) %>%
  summarise(Nobs = n()) %>%
  arrange(-Nobs) %>%
  print_table_conditional()
WORKER_STATUS Worker_status Nobs
_T Total 210474
ICSE93_1 Employees 206872
ICSE93_2T5 Self-employed 9364
_U No data/unknown 136

Part-time employment rate

Table

Code 
FTPT_COMMON_INC %>%
  filter(MEASURE == "EMP_PT",
         AGE == "Y15T64",
         obsTime == "2023",
         WORKER_STATUS == "_T") %>%
  left_join(REF_AREA, by = "REF_AREA") %>%
  select_if(~ n_distinct(.) > 1) %>%
  arrange(-obsValue) %>%
  spread(SEX, obsValue) %>%
  arrange(-F) %>%
  print_table_conditional()

France, Netherlands, Germany, Eurozone

Code 
FTPT_COMMON_INC %>%
  filter(MEASURE == "EMP_PT",
         REF_AREA %in% c("FRA", "NLD", "DEU", "EA20"),
         AGE == "Y15T64",
         WORKER_STATUS == "_T") %>%
  year_to_date() %>%
  arrange(desc(date)) %>%
  left_join(REF_AREA, by = "REF_AREA") %>%
  left_join(SEX, by = "SEX") %>%
  mutate(Ref_area = ifelse(REF_AREA == "EA20", "Europe", Ref_area)) %>%
  group_by(Ref_area) %>%
  arrange(date) %>%
  left_join(colors, by = c("Ref_area" = "country")) %>%
  mutate(obsValue = obsValue/100) %>%
  ggplot + geom_line(aes(x = date, y = obsValue, color = color, linetype = Sex)) +
  scale_color_identity() + add_6flags + theme_minimal() + xlab("") + ylab("") +
  scale_x_date(breaks = seq(1960, 2100, 5) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  theme(legend.position = c(0.2, 0.5)) +
  scale_y_continuous(labels = scales::percent_format(),
                     breaks = 0.01*seq(0, 100, 10))