Population and employment

Data - Eurostat

Author

François Geerolf

Info

source dataset Title .html .rData
eurostat namq_10_pe Population and employment 2026-01-29 2026-01-28
eurostat nama_10_pe Population and employment - nama_10_pe 2026-01-29 2026-01-28

LAST_COMPILE

LAST_COMPILE
2026-01-29

Last

Code 
namq_10_pe %>%
  group_by(time) %>%
  summarise(Nobs = n()) %>%
  arrange(desc(time)) %>%
  head(1) %>%
  print_table_conditional()
time Nobs
2025Q3 1275

na_item

Code 
namq_10_pe %>%
  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 26090
SAL_DC Employees domestic concept 24916
SELF_DC Self-employed domestic concept 24916
POP_NC Total population national concept 23856
EMP_NC Total employment national concept 22324
SAL_NC Employees national concept 21514
SELF_NC Self-employed national concept 21514

s_adj

Code 
namq_10_pe %>%
  left_join(s_adj, by = "s_adj") %>%
  group_by(s_adj, S_adj) %>%
  summarise(Nobs = n()) %>%
  arrange(-Nobs) %>%
  {if (is_html_output()) print_table(.) else .}
s_adj S_adj Nobs
SCA Seasonally and calendar adjusted data 73367
NSA Unadjusted data (i.e. neither seasonally adjusted nor calendar adjusted data) 61752
SA Seasonally adjusted data, not calendar adjusted data 23875
CA Calendar adjusted data, not seasonally adjusted data 6136

freq

Code 
namq_10_pe %>%
  left_join(freq, by = "freq") %>%
  group_by(freq, Freq) %>%
  summarise(Nobs = n()) %>%
  arrange(-Nobs) %>%
  {if (is_html_output()) print_table(.) else .}
freq Freq Nobs
Q Quarterly 165130

unit

Code 
namq_10_pe %>%
  left_join(unit, by = "unit") %>%
  group_by(unit, Unit) %>%
  summarise(Nobs = n()) %>%
  arrange(-Nobs) %>%
  {if (is_html_output()) print_table(.) else .}
unit Unit Nobs
THS_PER Thousand persons 67348
PCH_SM_PER Percentage change compared to same period in previous year (based on persons) 65192
PCH_PRE_PER Percentage change on previous period (based on persons) 32590

geo

Code 
namq_10_pe %>%
  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 
namq_10_pe %>%
  group_by(time) %>%
  summarise(Nobs = n()) %>%
  {if (is_html_output()) datatable(., filter = 'top', rownames = F) else .}

Population Table

Code 
namq_10_pe %>%
  filter(time %in% c("2019Q1", "2009Q1", "1999Q1", "1989Q1"),
         na_item == "POP_NC",
         s_adj %in% c("SCA", "SA"),
         unit == "THS_PER") %>%
  select(geo, s_adj, time, values) %>%
  mutate(values = round(values/1000, 1)) %>%
  left_join(geo, by = "geo") %>%
  spread(time, values) %>%
  arrange(- `2009Q1`) %>%
  {if (is_html_output()) datatable(., filter = 'top', rownames = F) else .}

Employment Table

Code 
namq_10_pe %>%
  filter(time %in% c("2019Q1", "2009Q1", "1999Q1", "1989Q1"),
         na_item == "EMP_DC",
         s_adj %in% c("SCA", "SA"),
         unit == "THS_PER") %>%
  select(geo, s_adj, time, values) %>%
  mutate(values = round(values/1000, 1)) %>%
  left_join(geo, by = "geo") %>%
  spread(time, values) %>%
  arrange(- `2019Q1`) %>%
  {if (is_html_output()) datatable(., filter = 'top', rownames = F) else .}

Eurozone

Last observation

Code 
namq_10_pe %>%
  filter(time == max(time),
         na_item == "POP_NC") %>%
  spread(unit, values) %>%
  select_if(~ n_distinct(.) > 1) %>%
  left_join(geo, by = "geo") %>%
  select(geo, Geo, everything()) %>%
  print_table_conditional

Previous observation

Code 
namq_10_pe %>%
  filter(time %in% c("2023Q4", "2023Q3"),
         na_item == "POP_NC",
         geo == "EA20") %>%
  spread(time, values) %>%
  select_if(~ n_distinct(.) > 1) %>%
  print_table_conditional
unit 2023Q3 2023Q4
PCH_SM_PER 0.5 0.5
THS_PER 349664.7 350194.9

France, Germany, Italy, Europe

Population

All

Code 
namq_10_pe %>%
  filter(geo %in% c("FR", "DE", "IT", "EA20"),
         unit == "THS_PER",
         s_adj == "NSA",
         na_item == "POP_NC") %>%
  left_join(geo, by = "geo") %>%
  quarter_to_date %>%
  group_by(geo) %>%
  mutate(values = 100*values/values[1]) %>%
  select_if(~ n_distinct(.) > 1) %>%
  mutate(Geo = ifelse(geo == "EA20", "Europe", Geo)) %>%
  left_join(colors, by = c( "Geo" = "country")) %>%
  ggplot() + ylab("Population") + xlab("") + theme_minimal() +
  geom_line(aes(x = date, y = values, color = color)) +
  scale_color_identity() + theme_minimal() + add_4flags +
  scale_x_date(breaks = seq(1909, 2100, 5) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  theme(legend.position = c(0.3, 0.7),
        legend.title = element_blank()) +
  scale_y_log10(breaks = seq(10, 300, 2),
                     labels = dollar_format(accuracy = 1, prefix = "")) +
  geom_label_repel(data = . %>% group_by(Geo) %>% filter(date == max(date)), aes(x = date, y = values, label = round(values, 1), color = color), show.legend = F)

1999-

Code 
namq_10_pe %>%
  filter(geo %in% c("FR", "DE", "IT", "EA20"),
         unit == "THS_PER",
         s_adj == "NSA",
         na_item == "POP_NC") %>%
  left_join(geo, by = "geo") %>%
  quarter_to_date %>%
  filter(date >= as.Date("1999-01-01")) %>%
  group_by(geo) %>%
  mutate(values = 100*values/values[1]) %>%
  select_if(~ n_distinct(.) > 1) %>%
  mutate(Geo = ifelse(geo == "EA20", "Europe", Geo)) %>%
  left_join(colors, by = c( "Geo" = "country")) %>%
  ggplot() + ylab("Population") + xlab("") + theme_minimal() +
  geom_line(aes(x = date, y = values, color = color)) +
  scale_color_identity() + theme_minimal() + add_4flags +
  scale_x_date(breaks = seq(1909, 2100, 5) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  theme(legend.position = c(0.3, 0.7),
        legend.title = element_blank()) +
  scale_y_log10(breaks = seq(10, 300, 2),
                     labels = dollar_format(accuracy = 1, prefix = "")) +
  geom_label_repel(data = . %>% group_by(Geo) %>% filter(date == max(date)), aes(x = date, y = values, label = round(values, 1), color = color), show.legend = F)

2001-2021

Code 
namq_10_pe %>%
  filter(geo %in% c("FR", "DE", "IT", "EA20"),
         unit == "THS_PER",
         s_adj == "NSA",
         na_item == "POP_NC") %>%
  left_join(geo, by = "geo") %>%
  quarter_to_date %>%
  filter(date >= as.Date("2001-01-01"),
         date <= as.Date("2021-01-01")) %>%
  group_by(geo) %>%
  mutate(values = 100*values/values[1]) %>%
  select_if(~ n_distinct(.) > 1) %>%
  mutate(Geo = ifelse(geo == "EA20", "Europe", Geo)) %>%
  left_join(colors, by = c( "Geo" = "country")) %>%
  ggplot() + ylab("Population") + xlab("") + theme_minimal() +
  geom_line(aes(x = date, y = values, color = color)) +
  scale_color_identity() + theme_minimal() + add_4flags +
  scale_x_date(breaks = seq(1909, 2100, 2) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  theme(legend.position = c(0.3, 0.7),
        legend.title = element_blank()) +
  scale_y_log10(breaks = seq(10, 300, 2),
                     labels = dollar_format(accuracy = 1, prefix = "")) +
  geom_label_repel(data = . %>% group_by(Geo) %>% filter(date == max(date)), aes(x = date, y = values, label = round(values, 1), color = color), show.legend = F)

France Evolution

Max -

NSA

Code 
namq_10_pe %>%
  filter(geo == "FR",
         unit == "THS_PER",
         s_adj == "NSA") %>%
  left_join(na_item, by = "na_item") %>%
  quarter_to_date %>%
  group_by(na_item) %>%
  mutate(values = 100*values/values[1]) %>%
  select_if(~ n_distinct(.) > 1) %>%
  ggplot() + ylab("Indice des prix, Ensemble") + xlab("") + theme_minimal() +
  geom_line(aes(x = date, y = values, color = Na_item)) +
  theme_minimal() +
  scale_x_date(breaks = seq(1909, 2100, 5) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  theme(legend.position = c(0.3, 0.7),
        legend.title = element_blank()) +
  scale_y_log10(breaks = seq(10, 300, 2),
                     labels = dollar_format(accuracy = 1, prefix = "")) +
  geom_label_repel(data = . %>% group_by(Na_item) %>% filter(date == max(date)), aes(x = date, y = values, label = round(values, 1), color = Na_item), show.legend = F)

SA

Code 
namq_10_pe %>%
  filter(geo == "FR",
         unit == "THS_PER",
         s_adj == "SA") %>%
  left_join(na_item, by = "na_item") %>%
  quarter_to_date %>%
  #filter(date >= as.Date("1999-01-01")) %>%
  group_by(na_item) %>%
  mutate(values = 100*values/values[1]) %>%
  select_if(~ n_distinct(.) > 1) %>%
  ggplot() + ylab("") + xlab("") + theme_minimal() +
  geom_line(aes(x = date, y = values, color = Na_item)) +
  theme_minimal() +
  scale_x_date(breaks = seq(1979, 2100, 5) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  theme(legend.position = c(0.3, 0.7),
        legend.title = element_blank()) +
  scale_y_log10(breaks = seq(10, 300, 2),
                     labels = dollar_format(accuracy = 1, prefix = "")) +
  geom_label_repel(data = . %>% group_by(Na_item) %>% filter(date == max(date)), aes(x = date, y = values, label = round(values, 1), color = Na_item))

1999 -

NSA

Code 
namq_10_pe %>%
  filter(geo == "FR",
         unit == "THS_PER",
         s_adj == "NSA") %>%
  left_join(na_item, by = "na_item") %>%
  quarter_to_date %>%
  filter(date >= as.Date("1999-01-01")) %>%
  group_by(na_item) %>%
  mutate(values = 100*values/values[1]) %>%
  select_if(~ n_distinct(.) > 1) %>%
  ggplot() + ylab("Indice des prix, Ensemble") + xlab("") + theme_minimal() +
  geom_line(aes(x = date, y = values, color = Na_item)) +
  theme_minimal() +
  scale_x_date(breaks = seq(1999, 2100, 5) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  theme(legend.position = c(0.3, 0.8),
        legend.title = element_blank()) +
  scale_y_log10(breaks = seq(10, 300, 2),
                     labels = dollar_format(accuracy = 1, prefix = "")) +
  geom_label_repel(data = . %>% group_by(Na_item) %>% filter(date == max(date)), aes(x = date, y = values, label = round(values, 1), color = Na_item))

SA

Code 
namq_10_pe %>%
  filter(geo == "FR",
         unit == "THS_PER",
         s_adj == "SA") %>%
  left_join(na_item, by = "na_item") %>%
  quarter_to_date %>%
  filter(date >= as.Date("1999-01-01")) %>%
  group_by(na_item) %>%
  mutate(values = 100*values/values[1]) %>%
  select_if(~ n_distinct(.) > 1) %>%
  ggplot() + ylab("") + xlab("") + theme_minimal() +
  geom_line(aes(x = date, y = values, color = Na_item)) +
  theme_minimal() +
  scale_x_date(breaks = seq(1999, 2100, 5) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  theme(legend.position = c(0.3, 0.7),
        legend.title = element_blank()) +
  scale_y_log10(breaks = seq(10, 300, 2),
                     labels = dollar_format(accuracy = 1, prefix = "")) +
  geom_label_repel(data = . %>% group_by(Na_item) %>% filter(date == max(date)), aes(x = date, y = values, label = round(values, 1), color = Na_item))

2001-2021

NSA

Code 
namq_10_pe %>%
  filter(geo == "FR",
         unit == "THS_PER",
         s_adj == "NSA") %>%
  left_join(na_item, by = "na_item") %>%
  quarter_to_date %>%
  filter(date >= as.Date("2001-01-01"),
         date <= as.Date("2021-01-01")) %>%
  group_by(na_item) %>%
  mutate(values = 100*values/values[1]) %>%
  select_if(~ n_distinct(.) > 1) %>%
  ggplot() + ylab("Indice des prix, Ensemble") + xlab("") + theme_minimal() +
  geom_line(aes(x = date, y = values, color = Na_item)) +
  theme_minimal() +
  scale_x_date(breaks = seq(1999, 2100, 5) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  theme(legend.position = c(0.3, 0.7),
        legend.title = element_blank()) +
  scale_y_log10(breaks = seq(10, 300, 2),
                     labels = dollar_format(accuracy = 1, prefix = "")) +
  geom_label_repel(data = . %>% group_by(Na_item) %>% filter(date == max(date)), aes(x = date, y = values, label = round(values, 1), color = Na_item), show.legend = F)

SA

Code 
namq_10_pe %>%
  filter(geo == "FR",
         unit == "THS_PER",
         s_adj == "SA") %>%
  left_join(na_item, by = "na_item") %>%
  quarter_to_date %>%
  filter(date >= as.Date("2001-01-01"),
         date <= as.Date("2021-01-01")) %>%
  group_by(na_item) %>%
  mutate(values = 100*values/values[1]) %>%
  select_if(~ n_distinct(.) > 1) %>%
  ggplot() + ylab("") + xlab("") + theme_minimal() +
  geom_line(aes(x = date, y = values, color = Na_item)) +
  theme_minimal() +
  scale_x_date(breaks = seq(2001, 2100, 2) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  theme(legend.position = c(0.3, 0.7),
        legend.title = element_blank()) +
  scale_y_log10(breaks = seq(10, 300, 2),
                     labels = dollar_format(accuracy = 1, prefix = "")) +
  geom_label_repel(data = . %>% group_by(Na_item) %>% filter(date == max(date)), aes(x = date, y = values, label = round(values, 1), color = Na_item))