Employment by A*10 industry breakdowns

Data - Eurostat

Author

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	[2024-11-01]
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	2024-11-05	[2024-11-01]
eurostat	namq_10_a10_e	Employment A*10 industry breakdowns	2024-10-08	[2024-11-01]
eurostat	une_rt_m	Unemployment by sex and age – monthly data	2024-10-24	[2024-10-24]
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())