source | dataset | Title | Download | Compile |
---|---|---|---|---|
eurostat | nama_10_a64_e | National accounts employment data by industry (up to NACE A*64) | 2024-11-22 | [2024-11-16] |
bls | jt | Job Openings and Labor Turnover Survey - JT | NA | [2024-11-12] |
bls | la | Local Area Unemployment Statistics - LA | NA | [2024-11-12] |
bls | ln | Labor Force Statistics including the National Unemployment Rate - LN | NA | [2024-11-12] |
eurostat | nama_10_a10_e | Employment by A*10 industry breakdowns | 2024-11-23 | [2024-11-22] |
eurostat | namq_10_a10_e | Employment A*10 industry breakdowns | 2024-11-22 | [2024-11-22] |
eurostat | une_rt_m | Unemployment by sex and age – monthly data | 2024-11-22 | [2024-11-22] |
oecd | ALFS_EMP | Employment by activities and status (ALFS) | 2024-11-22 | [2024-04-16] |
oecd | EPL_T | Strictness of employment protection – temporary contracts | 2023-12-10 | [2024-11-12] |
oecd | LFS_SEXAGE_I_R | LFS by sex and age - indicators | 2024-04-15 | [2024-09-15] |
oecd | STLABOUR | Short-Term Labour Market Statistics | 2024-11-22 | [2024-11-22] |
Employment by A*10 industry breakdowns
Data - Eurostat
Info
Data on employment
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"),
== "C",
nace_r2 == "THS_HW",
unit == "EMP_DC") %>%
na_item 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"),
== "TOTAL",
nace_r2 == "THS_PER",
unit == "EMP_DC") %>%
na_item 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())