At-risk-of-poverty rate by poverty threshold, age and sex - EU-SILC and ECHP surveys - ilc_li02

Data - Eurostat

Author

François Geerolf

Info

LAST_DOWNLOAD

Code 
tibble(LAST_DOWNLOAD = as.Date(file.info("~/Library/Mobile\ Documents/com~apple~CloudDocs/website/data/eurostat/ilc_li02.RData")$mtime)) %>%
  print_table_conditional()
LAST_DOWNLOAD
2024-11-21

LAST_COMPILE

LAST_COMPILE
2024-11-22

Last

Code 
ilc_li02 %>%
  group_by(time) %>%
  summarise(Nobs = n()) %>%
  arrange(desc(time)) %>%
  head(1) %>%
  print_table_conditional()
time Nobs
2023 41664

age

Code 
ilc_li02 %>%
  left_join(age, by = "age") %>%
  group_by(age, Age) %>%
  summarise(Nobs = n()) %>%
  arrange(-Nobs) %>%
  print_table_conditional()

sex

Code 
ilc_li02 %>%
  left_join(sex, by = "sex") %>%
  group_by(sex, Sex) %>%
  summarise(Nobs = n()) %>%
  arrange(-Nobs) %>%
  print_table_conditional()
sex Sex Nobs
T Total 338864
F Females 338717
M Males 338717

indic_il

Code 
ilc_li02 %>%
  left_join(indic_il, by = "indic_il") %>%
  group_by(indic_il, Indic_il) %>%
  summarise(Nobs = n()) %>%
  arrange(-Nobs) %>%
  print_table_conditional()
indic_il Indic_il Nobs
LI_R_MD60 At risk of poverty rate (cut-off point: 60% of median equivalised income after social transfers) 146145
LI_R_MD50 At risk of poverty rate (cut-off point: 50% of median equivalised income) 145073
LI_R_M40 At risk of poverty rate (cut-off point: 40% of mean equivalised income) 145026
LI_R_M50 At risk of poverty rate (cut-off point: 50% of mean equivalised income) 145026
LI_R_M60 At risk of poverty rate (cut-off point: 60% of mean equivalised income) 145026
LI_R_MD40 At risk of poverty rate (cut-off point: 40% of median equivalised income) 145001
LI_R_MD70 At risk of poverty rate (cut-off point: 70% of median equivalised income) 145001

geo

Code 
ilc_li02 %>%
  left_join(geo, by = "geo") %>%
  group_by(geo, Geo) %>%
  summarise(Nobs = n()) %>%
  arrange(-Nobs) %>%
  mutate(Geo = ifelse(geo == "DE", "Germany", Geo)) %>%
  mutate(Flag = gsub(" ", "-", str_to_lower(Geo)),
         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 .}

Eurostat - poverty (60%)

France, Germany, Sweden

Code 
ilc_li02 %>%
  filter(sex == "T", 
         age == "TOTAL", 
         geo %in% c("FR", "DE", "SE"), 
         unit == "PC", 
         indic_il == "LI_R_M60") %>%
  time_to_date %>%
  left_join(geo, by = "geo") %>%
  mutate(values = values/100) %>%
  left_join(colors, by = c("Geo" = "country")) %>%
  mutate(color = ifelse(geo == "NL", color2, color)) %>%
  ggplot + geom_line(aes(x = date, y = values, color = color)) +
  theme_minimal()  + add_3flags + scale_color_identity() +
  scale_x_date(breaks = seq(1920, 2025, 2) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  theme(legend.position = c(0.78, 0.2),
        legend.title = element_blank()) +
  scale_y_continuous(breaks = 0.01*seq(-7, 50, 1),
                     labels = percent_format(accuracy = 1)) +
  ylab("Poverty Rate") + xlab("")

Germany, Europe

Code 
ilc_li02 %>%
  filter(sex == "T", 
         age == "TOTAL", 
         geo %in% c("DE", "EA19"), 
         unit == "PC", 
         indic_il == "LI_R_M60") %>%
  time_to_date %>%
  left_join(geo, by = "geo") %>%
  mutate(values = values/100) %>%
  mutate(Geo = ifelse(geo == "EA19", "Europe", Geo)) %>%
  left_join(colors, by = c("Geo" = "country")) %>%
  mutate(color = ifelse(geo == "NL", color2, color)) %>%
  ggplot + geom_line(aes(x = date, y = values, color = color)) +
  theme_minimal()  + add_2flags + scale_color_identity() +
  scale_x_date(breaks = seq(1920, 2025, 2) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  theme(legend.position = c(0.78, 0.2),
        legend.title = element_blank()) +
  scale_y_continuous(breaks = 0.01*seq(-7, 50, 1),
                     labels = percent_format(accuracy = 1)) +
  ylab("Poverty Rate") + xlab("")

At risk of poverty rate (cut-off point: 40% of mean equivalised income)

France, Germany, Italy, Spain, Netherlannds

Code 
ilc_li02 %>%
  filter(sex == "T", 
         age == "TOTAL", 
         geo %in% c("FR", "DE", "IT", "ES", "NL"), 
         unit == "PC", 
         indic_il == "LI_R_M40") %>%
  time_to_date %>%
  left_join(geo, by = "geo") %>%
  mutate(values = values/100) %>%
  left_join(colors, by = c("Geo" = "country")) %>%
  mutate(color = ifelse(geo == "NL", color2, color)) %>%
  ggplot + geom_line(aes(x = date, y = values, color = color)) +
  theme_minimal()  + add_5flags +
  scale_color_identity() + xlab("") + ylab("") +
  scale_x_date(breaks = seq(1920, 2025, 5) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  theme(legend.position = c(0.35, 0.9),
        legend.title = element_blank()) +
  scale_y_continuous(breaks = 0.01*seq(-7, 50, 1),
                     labels = percent_format(accuracy = 1))

France, Germany, Sweden

Code 
ilc_li02 %>%
  filter(sex == "T", 
         age == "TOTAL", 
         geo %in% c("FR", "DE", "SE"), 
         unit == "PC", 
         indic_il == "LI_R_M40") %>%
  time_to_date %>%
  left_join(geo, by = "geo") %>%
  mutate(values = values/100) %>%
  left_join(colors, by = c("Geo" = "country")) %>%
  ggplot + geom_line(aes(x = date, y = values, color = color)) +
  theme_minimal()  + add_3flags +
  scale_color_identity() + xlab("") + ylab("") +
  scale_x_date(breaks = seq(1920, 2025, 5) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  theme(legend.position = c(0.35, 0.9),
        legend.title = element_blank()) +
  scale_y_continuous(breaks = 0.01*seq(-7, 50, 1),
                     labels = percent_format(accuracy = 1))

France, Germany

Code 
ilc_li02 %>%
  filter(sex == "T", 
         age == "TOTAL", 
         geo %in% c("FR", "DE"), 
         unit == "PC", 
         indic_il == "LI_R_M40") %>%
  time_to_date %>%
  left_join(geo, by = "geo") %>%
  mutate(values = values/100) %>%
  left_join(colors, by = c("Geo" = "country")) %>%
  ggplot + geom_line(aes(x = date, y = values, color = color)) +
  theme_minimal()  + add_2flags +
  scale_color_identity() + xlab("") + ylab("") +
  scale_x_date(breaks = seq(1920, 2025, 5) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  theme(legend.position = c(0.35, 0.9),
        legend.title = element_blank()) +
  scale_y_continuous(breaks = 0.01*seq(-7, 50, 1),
                     labels = percent_format(accuracy = 1))