Employment rates by sex, age, educational attainment level, country of birth and NUTS 2 regions - lfst_r_lfe2emprc

Data - Eurostat

Author

François Geerolf

c_birth

Code 
lfst_r_lfe2emprc %>%
  left_join(c_birth, by = "c_birth") %>%
  group_by(c_birth, C_birth) %>%
  summarise(Nobs = n()) %>%
  arrange(-Nobs) %>%
  {if (is_html_output()) print_table(.) else .}
c_birth C_birth Nobs
TOTAL Total 592922
NAT Reporting country 551668
FOR Foreign country 527587
NEU27_2020_FOR Non-EU27 countries (from 2020) nor reporting country 461860
EU27_2020_FOR EU27 countries (from 2020) except reporting country 438875
NRP No response 147493

isced11

Code 
lfst_r_lfe2emprc %>%
  left_join(isced11, by = "isced11") %>%
  group_by(isced11, Isced11) %>%
  summarise(Nobs = n()) %>%
  arrange(-Nobs) %>%
  {if (is_html_output()) print_table(.) else .}
isced11 Isced11 Nobs
TOTAL All ISCED 2011 levels 647126
ED3_4 Upper secondary and post-secondary non-tertiary education (levels 3 and 4) 625735
ED5-8 Tertiary education (levels 5-8) 619418
ED0-2 Less than primary, primary and lower secondary education (levels 0-2) 610542
NRP No response 217524
UNK Unknown 60

age

Code 
lfst_r_lfe2emprc %>%
  left_join(age, by = "age") %>%
  group_by(age, Age) %>%
  summarise(Nobs = n()) %>%
  arrange(-Nobs) %>%
  {if (is_html_output()) print_table(.) else .}
age Age Nobs
Y15-64 From 15 to 64 years 710833
Y20-64 From 20 to 64 years 694910
Y25-54 From 25 to 54 years 681974
Y55-64 From 55 to 64 years 632688

sex

Code 
lfst_r_lfe2emprc %>%
  left_join(sex, by = "sex") %>%
  group_by(sex, Sex) %>%
  summarise(Nobs = n()) %>%
  arrange(-Nobs) %>%
  {if (is_html_output()) print_table(.) else .}
sex Sex Nobs
T Total 924084
F Females 899673
M Males 896648

unit

Code 
lfst_r_lfe2emprc %>%
  left_join(unit, by = "unit") %>%
  group_by(unit, Unit) %>%
  summarise(Nobs = n()) %>%
  arrange(-Nobs) %>%
  {if (is_html_output()) print_table(.) else .}
unit Unit Nobs
PC Percentage 2720405

geo

Code 
lfst_r_lfe2emprc %>%
  left_join(geo, by = "geo") %>%
  group_by(geo, Geo) %>%
  summarise(Nobs = n()) %>%
  arrange(-Nobs) %>%
  {if (is_html_output()) datatable(., filter = 'top', rownames = F) else .}

time

Code 
lfst_r_lfe2emprc %>%
  group_by(time) %>%
  summarise(Nobs = n()) %>%
  arrange(-Nobs) %>%
  {if (is_html_output()) datatable(., filter = 'top', rownames = F) else .}

Education Foreign: levels 0-2

Code 
lfst_r_lfe2emprc %>%
  filter(unit == "PC",
         sex == "T",
         isced11 == "ED0-2",
         c_birth == "FOR",
         nchar(geo) == 4,
         time == "2018") %>%
  right_join(europe_NUTS2, by = "geo") %>%
  filter(long >= -13.5, lat >= 33) %>%
  ggplot(., aes(x = long, y = lat, group = group, fill = values/100)) +
  geom_polygon() + coord_map() +
  scale_fill_viridis_c(na.value = "white",
                       labels = scales::percent_format(accuracy = 1),
                       breaks = 0.01*seq(20, 100, 10),
                       values = c(0, 0.1, 0.3, 0.4, 0.5, 0.6, 1)) +
  theme_void() + theme(legend.position = c(0.15, 0.85)) +
  labs(fill = "Employment \nPrimary Education (%)")

Upper secondary and post-secondary non-tertiary education (levels 3 and 4)

Education Foreign: levels 3-4

Code 
lfst_r_lfe2emprc %>%
  filter(unit == "PC",
         sex == "T",
         isced11 == "ED3_4",
         c_birth == "FOR",
         nchar(geo) == 4,
         time == "2018") %>%
  right_join(europe_NUTS2, by = "geo") %>%
  filter(long >= -13.5, lat >= 33) %>%
  ggplot(., aes(x = long, y = lat, group = group, fill = values/100)) +
  geom_polygon() + coord_map() +
  scale_fill_viridis_c(na.value = "white",
                       labels = scales::percent_format(accuracy = 1),
                       breaks = 0.01*seq(20, 100, 10),
                       values = c(0, 0.1, 0.3, 0.4, 0.5, 0.6, 1)) +
  theme_void() + theme(legend.position = c(0.15, 0.85)) +
  labs(fill = "Employment \nSecondary Education (%)")

Education Foreign: levels 5-8

Code 
lfst_r_lfe2emprc %>%
  filter(unit == "PC",
         sex == "T",
         isced11 == "ED5-8",
         c_birth == "FOR",
         nchar(geo) == 4,
         time == "2018") %>%
  right_join(europe_NUTS2, by = "geo") %>%
  filter(long >= -13.5, lat >= 33) %>%
  ggplot(., aes(x = long, y = lat, group = group, fill = values/100)) +
  geom_polygon() + coord_map() +
  scale_fill_viridis_c(na.value = "white",
                       labels = scales::percent_format(accuracy = 1),
                       breaks = 0.01*seq(20, 100, 10),
                       values = c(0, 0.1, 0.3, 0.4, 0.5, 0.6, 1)) +
  theme_void() + theme(legend.position = c(0.15, 0.85)) +
  labs(fill = "Employment \nTertiary Education (%)")