Industry as % of total employment – ILO modelled estimates, November 2019 - EMP_2IND_NOC_RT_A

Data - ILO

Author

source

Code

EMP_2IND_NOC_RT_A %>%
  left_join(source, by = "source") %>%
  group_by(source, Source) %>%
  summarise(Nobs = n()) %>%
  arrange(-Nobs) %>%
  {if (is_html_output()) datatable(., filter = 'top', rownames = F) else .}

ref_area

Code

EMP_2IND_NOC_RT_A %>%
  left_join(ref_area, by = "ref_area") %>%
  group_by(ref_area, Ref_area) %>%
  summarise(Nobs = n()) %>%
  arrange(-Nobs) %>%
  mutate(Flag = gsub(" ", "-", str_to_lower(Ref_area)),
         Flag = paste0('<img src="../../bib/flags/vsmall/', Flag, '.png" alt="Flag">')) %>%
  select(Flag, everything()) %>%
  {if (is_html_output()) datatable(., filter = 'top', rownames = F, escape = F) else .}

Industry

Germany, France, U.S., Switzerland

Code

EMP_2IND_NOC_RT_A %>%
  year_to_date %>%
  filter(ref_area %in% c("DEU", "FRA", "USA", "CHE")) %>%
  left_join(ref_area, by = "ref_area") %>%
  ggplot() + scale_color_manual(values = viridis(5)[1:4]) + theme_minimal() +
  geom_line(aes(x = date, y = obs_value/100, color = Ref_area, linetype = Ref_area)) +
  scale_x_date(breaks = seq(1920, 2025, 2) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%y")) +
  theme(legend.position = c(0.85, 0.8),
        legend.title = element_blank()) +
  scale_y_continuous(breaks = 0.01*seq(0, 90, 2),
                     labels = percent_format(accuracy = 1)) +
  ylab("Industry (% of total employment)") + xlab("")

Sweden, U.K., Spain, Italy

Code

EMP_2IND_NOC_RT_A %>%
  year_to_date %>%
  filter(ref_area %in% c("SWE", "GBR", "ESP", "ITA")) %>%
  left_join(ref_area, by = "ref_area") %>%
  ggplot() + scale_color_manual(values = viridis(5)[1:4]) + theme_minimal() +
  geom_line(aes(x = date, y = obs_value/100, color = Ref_area, linetype = Ref_area)) +
  scale_x_date(breaks = seq(1920, 2025, 2) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%y")) +
  theme(legend.position = c(0.85, 0.8),
        legend.title = element_blank()) +
  scale_y_continuous(breaks = 0.01*seq(0, 90, 2),
                     labels = percent_format(accuracy = 1)) +
  ylab("Industry (% of total employment)") + xlab("")