Employment by A*10 industry breakdowns

Data - Eurostat

Author

François Geerolf

Info

Data on employment

source dataset Title Download Compile
eurostat nama_10_a64_e National accounts employment data by industry (up to NACE A*64) 2024-10-08 [2024-11-05]
bls jt Job Openings and Labor Turnover Survey - JT NA [2024-05-01]
bls la Local Area Unemployment Statistics - LA NA [2024-06-19]
bls ln Labor Force Statistics including the National Unemployment Rate - LN NA [2024-06-19]
eurostat nama_10_a10_e Employment by A*10 industry breakdowns 2024-11-08 [2024-11-05]
eurostat namq_10_a10_e Employment A*10 industry breakdowns 2024-10-08 [2024-11-05]
eurostat une_rt_m Unemployment by sex and age – monthly data 2024-10-24 [2024-11-05]
oecd ALFS_EMP Employment by activities and status (ALFS) 2024-05-12 [2024-04-16]
oecd EPL_T Strictness of employment protection – temporary contracts 2023-12-10 [2024-04-16]
oecd LFS_SEXAGE_I_R LFS by sex and age - indicators 2024-04-15 [2024-09-15]
oecd STLABOUR Short-Term Labour Market Statistics 2024-06-30 [2024-09-15]

Last

Code 
nama_10_a10_e %>%
  group_by(time) %>%
  summarise(Nobs = n()) %>%
  arrange(desc(time)) %>%
  head(2) %>%
  print_table_conditional()
time Nobs
2023 11352
2022 11389

unit

Code 
nama_10_a10_e %>%
  left_join(unit, by = "unit") %>%
  group_by(unit, Unit) %>%
  summarise(Nobs = n()) %>%
  arrange(-Nobs) %>%
  {if (is_html_output()) datatable(., filter = 'top', rownames = F) else .}

nace_r2

Code 
nama_10_a10_e %>%
  left_join(nace_r2, by = "nace_r2") %>%
  group_by(nace_r2, Nace_r2) %>%
  summarise(Nobs = n()) %>%
  arrange(-Nobs) %>%
  {if (is_html_output()) print_table(.) else .}
nace_r2 Nace_r2 Nobs
A Agriculture, forestry and fishing 30062
B-E Industry (except construction) 30062
C Manufacturing 30062
F Construction 30062
G-I Wholesale and retail trade, transport, accommodation and food service activities 30062
J Information and communication 30062
M_N Professional, scientific and technical activities; administrative and support service activities 30062
O-Q Public administration, defence, education, human health and social work activities 30062
R-U Arts, entertainment and recreation; other service activities; activities of household and extra-territorial organizations and bodies 30062
TOTAL Total - all NACE activities 30062
L Real estate activities 30003
K Financial and insurance activities 29601

na_item

Code 
nama_10_a10_e %>%
  left_join(na_item, by = "na_item") %>%
  group_by(na_item, Na_item) %>%
  summarise(Nobs = n()) %>%
  arrange(-Nobs) %>%
  {if (is_html_output()) print_table(.) else .}
na_item Na_item Nobs
EMP_DC Total employment domestic concept 121032
SAL_DC Employees domestic concept 119856
SELF_DC Self-employed domestic concept 119336

geo

Code 
nama_10_a10_e %>%
  left_join(geo, by = "geo") %>%
  group_by(geo, Geo) %>%
  summarise(Nobs = n()) %>%
  arrange(-Nobs) %>%
  {if (is_html_output()) datatable(., filter = 'top', rownames = F) else .}

Number of hours

Code 
nama_10_a10_e %>%
  left_join(geo, by = "geo") %>%
  filter(geo %in% c("FR", "DE", "IT"),
         nace_r2 == "C",
         unit == "THS_HW",
         na_item == "EMP_DC") %>%
  year_to_date() %>%
  arrange(date) %>%
  ggplot(.) + geom_line(aes(x = date, y = values/1000, color = geo, linetype = geo)) + 
  theme_minimal() + xlab("") + ylab("Number of Hours Worked") +
  scale_x_date(breaks = seq(1960, 2020, 5) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%y")) +
  scale_y_continuous(breaks = seq(0, 1000000, 1000),
                     labels = dollar_format(accuracy = 1, prefix = "", suffix = "M")) +
  scale_color_manual(values = viridis(5)[1:4]) +
  theme(legend.position = c(0.2, 0.80),
        legend.title = element_blank())

Number of hours worked

Code 
nama_10_a10_e %>%
  left_join(geo, by = "geo") %>%
  filter(geo %in% c("FR", "DE", "IT"),
         nace_r2 == "TOTAL",
         unit == "THS_PER",
         na_item == "EMP_DC") %>%
  year_to_date() %>%
  arrange(date) %>%
  ggplot(.) + geom_line(aes(x = date, y = values/1000, color = geo, linetype = geo)) + 
  theme_minimal() + xlab("") + ylab("Number of Hours Worked") +
  scale_x_date(breaks = seq(1960, 2025, 5) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  scale_y_continuous(breaks = seq(0, 1000000, 1000),
                     labels = dollar_format(accuracy = 1, prefix = "", suffix = "M")) +
  scale_color_manual(values = viridis(5)[1:4]) +
  theme(legend.position = c(0.2, 0.80),
        legend.title = element_blank())