Structure of earnings survey - monthly earnings - earn_ses_monthly

Data - Eurostat

Author

nace_r2

Code

earn_ses_monthly %>%
  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
B-N	Business economy	2606504
G-N	Services of the business economy	2553357
B-F	Industry and construction	2492938
B-S_X_O	Industry, construction and services (except public administration, defense, compulsory social security)	2343997
P-S	Education; human health and social work activities; arts, entertainment and recreation; other service activities	2217860

isco08

Code

earn_ses_monthly %>%
  left_join(isco08, by = "isco08") %>%
  group_by(isco08, Isco08) %>%
  summarise(Nobs = n()) %>%
  arrange(-Nobs) %>%
  {if (is_html_output()) datatable(., filter = 'top', rownames = F) else .}

worktime

Code

earn_ses_monthly %>%
  left_join(worktime, by = "worktime") %>%
  group_by(worktime, Worktime) %>%
  summarise(Nobs = n()) %>%
  arrange(-Nobs) %>%
  {if (is_html_output()) print_table(.) else .}

worktime	Worktime	Nobs
FT	Full-time	3113441
TOTAL	Total	3062116
PT	Part-time	2610946
TOT_FTE	Total in full-time equivalents	2150723
PT_FTE	Part-time in full-time equivalents	1277430

age

Code

earn_ses_monthly %>%
  left_join(age, by = "age") %>%
  group_by(age, Age) %>%
  summarise(Nobs = n()) %>%
  arrange(-Nobs) %>%
  {if (is_html_output()) print_table(.) else .}

age	Age	Nobs
TOTAL	Total	2275458
Y_LT30	Less than 30 years	2173273
Y40-49	From 40 to 49 years	1741306
Y30-39	From 30 to 39 years	1739364
Y50-59	From 50 to 59 years	1722459
Y_GE60	60 years or over	1594402
Y30-49	From 30 to 49 years	485511
Y_GE50	50 years or over	482883

sex

Code

earn_ses_monthly %>%
  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	4156774
M	Males	4095493
F	Females	3962389

indic_se

Code

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

geo

Code

earn_ses_monthly %>%
  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

earn_ses_monthly %>%
  group_by(time) %>%
  summarise(Nobs = n()) %>%
  {if (is_html_output()) print_table(.) else .}

time	Nobs
2002	1900421
2006	2631517
2010	3010992
2014	2879575
2018	1792151

Table

png

Code

include_graphics3b("bib/eurostat/earn_ses_monthly_ex1.png")

Javascript

Code

earn_ses_monthly %>%
  filter(nace_r2 == "B-N",
         isco08 == "TOTAL",
         worktime == "TOTAL", 
         age == "TOTAL",
         sex == "T",
         indic_se == "MEAN_E_EUR",
         time %in% c("2002", "2010", "2018")) %>%
  left_join(geo, by = "geo") %>%
  select(geo, Geo, time, values) %>%
  na.omit %>%
  mutate(Geo = ifelse(geo == "DE", "Germany", Geo),
         values = round(values)) %>%
  spread(time, values) %>%
  mutate_at(vars(-1, -2), funs(ifelse(is.na(.), "", paste0(., " €")))) %>%
  {if (is_html_output()) datatable(., filter = 'top', rownames = F) else .}

Maps

2018

Code

earn_ses_monthly %>%
  filter(nace_r2 == "B-N",
         isco08 == "TOTAL",
         worktime == "TOTAL", 
         age == "TOTAL",
         sex == "T",
         indic_se == "MEAN_E_EUR",
         time %in% c("2018")) %>%
  left_join(geo, by = "geo") %>%
  select(geo, Geo, values) %>%
  right_join(europe_NUTS0, by = "geo") %>%
  filter(long >= -15, lat >= 33) %>%
  ggplot(., aes(x = long, y = lat, group = group, fill = values)) +
  geom_polygon() + coord_map() +
  scale_fill_viridis_c(na.value = "white",
                       labels = scales::dollar_format(accuracy = 1, prefix = "", suffix = "€"),
                       breaks = seq(0, 20000, 1000),
                       values = c(0, 0.1, 0.2, 0.3, 0.4, 0.5, 1)) +
  theme_void() + theme(legend.position = c(0.25, 0.85)) + 
  labs(fill = "Avg Wage")

2014

Code

earn_ses_monthly %>%
  filter(nace_r2 == "B-N",
         isco08 == "TOTAL",
         worktime == "TOTAL", 
         age == "TOTAL",
         sex == "T",
         indic_se == "MEAN_E_EUR",
         time %in% c("2014")) %>%
  left_join(geo, by = "geo") %>%
  select(geo, Geo, values) %>%
  right_join(europe_NUTS0, by = "geo") %>%
  filter(long >= -15, lat >= 33) %>%
  ggplot(., aes(x = long, y = lat, group = group, fill = values)) +
  geom_polygon() + coord_map() +
  scale_fill_viridis_c(na.value = "white",
                       labels = scales::dollar_format(accuracy = 1, prefix = "", suffix = "€"),
                       breaks = seq(0, 20000, 1000),
                       values = c(0, 0.1, 0.2, 0.3, 0.4, 0.5, 1)) +
  theme_void() + theme(legend.position = c(0.25, 0.85)) + 
  labs(fill = "Avg Wage")