Employment A*10 industry breakdowns

Data - Eurostat

Info

source dataset .html .RData
eurostat namq_10_a10_e 2024-11-22 2024-11-22

Data on employment

source dataset .html .RData
bls jt 2024-11-12 NA
bls la 2024-11-12 NA
bls ln 2024-11-12 NA
eurostat nama_10_a10_e 2024-11-23 2024-11-23
eurostat nama_10_a64_e 2024-11-23 2024-11-22
eurostat namq_10_a10_e 2024-11-22 2024-11-22
eurostat une_rt_m 2024-11-22 2024-11-22
oecd ALFS_EMP 2024-04-16 2024-11-22
oecd EPL_T 2024-11-12 2023-12-10
oecd LFS_SEXAGE_I_R 2024-09-15 2024-04-15
oecd STLABOUR 2024-11-22 2024-11-22

Data on industry

source dataset .html .RData
ec INDUSTRY 2024-09-15 2023-10-01
eurostat ei_isin_m 2024-11-23 2024-10-09
eurostat htec_trd_group4 2024-11-23 2024-10-08
eurostat nama_10_a64 2024-11-23 2024-11-22
eurostat nama_10_a64_e 2024-11-23 2024-11-22
eurostat namq_10_a10_e 2024-11-22 2024-11-22
eurostat road_eqr_carmot 2024-11-22 2024-10-08
eurostat sts_inpp_m 2024-06-24 2024-11-21
eurostat sts_inppd_m 2024-11-22 2024-11-21
eurostat sts_inpr_m 2024-11-22 2024-10-08
eurostat sts_intvnd_m 2024-11-22 2024-11-21
fred industry 2024-11-21 2024-11-21
oecd ALFS_EMP 2024-04-16 2024-11-22
oecd BERD_MA_SOF 2024-04-16 2023-09-09
oecd GBARD_NABS2007 2024-04-16 2023-11-22
oecd MEI_REAL 2024-05-12 2024-11-22
oecd MSTI_PUB 2024-09-15 2024-11-22
oecd SNA_TABLE4 2024-09-15 2024-11-22
wdi NV.IND.EMPL.KD 2024-01-06 2024-09-18
wdi NV.IND.MANF.CD 2024-11-22 2024-11-22
wdi NV.IND.MANF.ZS 2024-11-22 2024-11-22
wdi NV.IND.TOTL.KD 2024-01-06 2024-09-18
wdi NV.IND.TOTL.ZS 2024-11-22 2024-11-22
wdi SL.IND.EMPL.ZS 2024-11-22 2024-11-22
wdi TX.VAL.MRCH.CD.WT 2024-01-06 2024-09-18

Last

Code
namq_10_a10_e %>%
  group_by(time) %>%
  summarise(Nobs = n()) %>%
  arrange(desc(time)) %>%
  head(2) %>%
  print_table_conditional()
time Nobs
2024Q3 4096
2024Q2 26802

na_item

Code
namq_10_a10_e %>%
  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 1120403
SAL_DC Employees domestic concept 1097457
SELF_DC Self-employed domestic concept 1088141

unit

namq_10_a10_e

Code
namq_10_a10_e %>%
  left_join(unit, by = "unit") %>%
  group_by(unit, Unit) %>%
  summarise(Nobs = n()) %>%
  arrange(-Nobs) %>%
  {if (is_html_output()) datatable(., filter = 'top', rownames = F) else .}

unit

Code
namq_10_a10_e %>%
  left_join(unit, by = "unit") %>%
  group_by(unit, Unit) %>%
  summarise(Nobs = n()) %>%
  arrange(-Nobs) %>%
  {if (is_html_output()) datatable(., filter = 'top', rownames = F) else .}

nace_r2

Code
namq_10_a10_e %>%
  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
TOTAL Total - all NACE activities 277873
A Agriculture, forestry and fishing 275783
G-I Wholesale and retail trade, transport, accommodation and food service activities 275783
B-E Industry (except construction) 275707
C Manufacturing 275707
F Construction 275707
M_N Professional, scientific and technical activities; administrative and support service activities 275707
R-U Arts, entertainment and recreation; other service activities; activities of household and extra-territorial organizations and bodies 275689
J Information and communication 275355
O-Q Public administration, defence, education, human health and social work activities 275341
L Real estate activities 274971
K Financial and insurance activities 272378

s_adj

Code
namq_10_a10_e %>%
  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 1577481
NSA Unadjusted data (i.e. neither seasonally adjusted nor calendar adjusted data) 1368439
SA Seasonally adjusted data, not calendar adjusted data 200409
CA Calendar adjusted data, not seasonally adjusted data 159672

geo

Code
namq_10_a10_e %>%
  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="../../icon/flag/vsmall/', Flag, '.png" alt="Flag">')) %>%
  select(Flag, everything()) %>%
  {if (is_html_output()) datatable(., filter = 'top', rownames = F, escape = F) else .}

Industry and Manufacturing Share

France, Germany, Italy, Romania

1995-

Code
load_data("eurostat/nace_r2_fr.RData")
namq_10_a10_e %>%
  filter(na_item == "EMP_DC",
         nace_r2 %in% c("C", "TOTAL", "B-E"),
         geo %in% c("FR", "DE", "IT", "RO"),
         s_adj == "SCA",
         unit== "THS_HW") %>%
  quarter_to_date() %>%
  left_join(geo, by = "geo") %>%
  left_join(nace_r2, by = "nace_r2") %>%
  group_by(date) %>%
  mutate(values = values/ values[nace_r2 == "TOTAL"]) %>%
  filter(nace_r2 != "TOTAL",
         date >= as.Date("1995-01-01")) %>%
  mutate(Geo = ifelse(geo == "DE", "Germany", Geo)) %>%
  left_join(colors, by = c("Geo" = "country")) %>%
  ggplot(.) + geom_line(aes(x = date, y = values, color = color, linetype = Nace_r2)) +
  scale_color_identity() +
  scale_linetype_manual(values = c("solid", "dashed")) +
  theme_minimal() +
  scale_x_date(breaks = seq(1920, 2100, 2) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  theme(legend.position = c(0.2, 0.1),
        legend.title = element_blank()) +
  add_8flags +
  scale_y_continuous(breaks = 0.01*seq(-60, 60, 1),
                     labels = scales::percent_format(accuracy = 1)) +
  ylab("Emploi Manufacturier, Industriel (% de l'Emploi)") + xlab("")

France, Germany, Italy

1995-

Code
load_data("eurostat/nace_r2_fr.RData")
namq_10_a10_e %>%
  filter(na_item == "EMP_DC",
         nace_r2 %in% c("C", "TOTAL", "B-E"),
         geo %in% c("FR", "DE", "IT"),
         s_adj == "SCA",
         unit== "THS_HW") %>%
  quarter_to_date() %>%
  left_join(geo, by = "geo") %>%
  left_join(nace_r2, by = "nace_r2") %>%
  group_by(date) %>%
  mutate(values = values/ values[nace_r2 == "TOTAL"]) %>%
  filter(nace_r2 != "TOTAL",
         date >= as.Date("1995-01-01")) %>%
  mutate(Geo = ifelse(geo == "DE", "Germany", Geo)) %>%
  left_join(colors, by = c("Geo" = "country")) %>%
  ggplot(.) + geom_line(aes(x = date, y = values, color = color, linetype = Nace_r2)) +
  scale_color_identity() +
  scale_linetype_manual(values = c("solid", "dashed")) +
  theme_minimal() +
  scale_x_date(breaks = seq(1920, 2100, 2) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  theme(legend.position = c(0.2, 0.1),
        legend.title = element_blank()) +
  add_6flags +
  scale_y_continuous(breaks = 0.01*seq(-60, 60, 1),
                     labels = scales::percent_format(accuracy = 1)) +
  ylab("Emploi Manufacturier, Industriel (% de l'Emploi)") + xlab("")

2008-

Code
namq_10_a10_e %>%
  filter(na_item == "EMP_DC",
         nace_r2 %in% c("C", "TOTAL", "B-E"),
         geo %in% c("FR", "DE", "IT"),
         s_adj == "SCA",
         unit== "THS_HW") %>%
  quarter_to_date() %>%
  left_join(geo, by = "geo") %>%
  left_join(nace_r2, by = "nace_r2") %>%
  group_by(date) %>%
  mutate(values = values/ values[nace_r2 == "TOTAL"]) %>%
  filter(nace_r2 != "TOTAL",
         date >= as.Date("2008-01-01")) %>%
  left_join(colors, by = c("Geo" = "country")) %>%
  mutate(Geo = ifelse(geo == "DE", "Germany", Geo)) %>%
  ggplot(.) + geom_line(aes(x = date, y = values, color = color, linetype = Nace_r2)) +
  scale_color_identity() +
  scale_linetype_manual(values = c("solid", "dashed")) +
  theme_minimal() +
  scale_x_date(breaks = seq(1920, 2100, 2) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  theme(legend.position = c(0.2, 0.5),
        legend.title = element_blank()) +
  add_6flags +
  scale_y_continuous(breaks = 0.01*seq(-60, 60, 1),
                     labels = scales::percent_format(accuracy = 1)) +
  ylab("Emploi Manufacturier, Industriel (% de l'Emploi)") + xlab("")

France, Germany, Italy, Spain, Netherlands, Euro area

1995-

Code
namq_10_a10_e %>%
  filter(na_item == "EMP_DC",
         nace_r2 %in% c("C", "TOTAL", "B-E"),
         geo %in% c("FR", "DE", "IT", "ES", "EA"),
         s_adj == "SCA",
         unit== "THS_HW") %>%
  quarter_to_date() %>%
  left_join(geo, by = "geo") %>%
  left_join(nace_r2, by = "nace_r2") %>%
  group_by(date) %>%
  mutate(values = values/ values[nace_r2 == "TOTAL"]) %>%
  filter(nace_r2 != "TOTAL",
         date >= as.Date("1995-01-01")) %>%
  mutate(Geo = ifelse(geo == "DE", "Germany", Geo)) %>%
  mutate(Geo = ifelse(geo == "EA", "Europe", Geo)) %>%
  left_join(colors, by = c("Geo" = "country")) %>%
  ggplot(.) + geom_line(aes(x = date, y = values, color = color, linetype = Nace_r2)) +
  scale_color_identity() +
  scale_linetype_manual(values = c("solid", "dashed")) +
  theme_minimal() +
  scale_x_date(breaks = seq(1920, 2100, 2) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  theme(legend.position = c(0.2, 0.1),
        legend.title = element_blank()) +
  add_flags(10) +
  scale_y_continuous(breaks = 0.01*seq(-60, 60, 1),
                     labels = scales::percent_format(accuracy = 1)) +
  ylab("Emploi Manufacturier, Industriel (% de l'Emploi)") + xlab("")

2008-

Code
namq_10_a10_e %>%
  filter(na_item == "EMP_DC",
         nace_r2 %in% c("C", "TOTAL", "B-E"),
         geo %in% c("FR", "DE", "IT", "ES", "EA"),
         s_adj == "SCA",
         unit== "THS_HW") %>%
  quarter_to_date() %>%
  left_join(geo, by = "geo") %>%
  left_join(nace_r2, by = "nace_r2") %>%
  group_by(date) %>%
  mutate(values = values/ values[nace_r2 == "TOTAL"]) %>%
  filter(nace_r2 != "TOTAL",
         date >= as.Date("2008-01-01")) %>%
  mutate(Geo = ifelse(geo == "DE", "Germany", Geo)) %>%
  mutate(Geo = ifelse(geo == "EA", "Europe", Geo)) %>%
  left_join(colors, by = c("Geo" = "country")) %>%
  ggplot(.) + geom_line(aes(x = date, y = values, color = color, linetype = Nace_r2)) +
  scale_color_identity() +
  scale_linetype_manual(values = c("solid", "dashed")) +
  theme_minimal() +
  scale_x_date(breaks = seq(1920, 2100, 2) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  theme(legend.position = c(0.3, 0.9),
        legend.title = element_blank()) +
  add_flags(10) +
  scale_y_continuous(breaks = 0.01*seq(-60, 60, 1),
                     labels = scales::percent_format(accuracy = 1)) +
  ylab("Emploi Manufacturier, Industriel (% de l'Emploi)") + xlab("")

2017-

Code
namq_10_a10_e %>%
  filter(na_item == "EMP_DC",
         nace_r2 %in% c("C", "TOTAL", "B-E"),
         geo %in% c("FR", "DE", "IT", "ES", "EA"),
         s_adj == "SCA",
         unit== "THS_HW") %>%
  quarter_to_date() %>%
  left_join(geo, by = "geo") %>%
  left_join(nace_r2, by = "nace_r2") %>%
  group_by(date) %>%
  mutate(values = values/ values[nace_r2 == "TOTAL"]) %>%
  filter(nace_r2 != "TOTAL",
         date >= as.Date("2017-01-01")) %>%
  mutate(Geo = ifelse(geo == "DE", "Germany", Geo)) %>%
  mutate(Geo = ifelse(geo == "EA", "Europe", Geo)) %>%
  left_join(colors, by = c("Geo" = "country")) %>%
  ggplot(.) + geom_line(aes(x = date, y = values, color = color, linetype = Nace_r2)) +
  scale_color_identity() +
  scale_linetype_manual(values = c("solid", "dashed")) +
  theme_minimal() +
  scale_x_date(breaks = seq(1920, 2100, 1) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  theme(legend.position = c(0.3, 0.9),
        legend.title = element_blank()) +
  add_flags(10) +
  scale_y_continuous(breaks = 0.01*seq(-60, 60, 1),
                     labels = scales::percent_format(accuracy = 1)) +
  ylab("Emploi Manufacturier, Industriel (% de l'Emploi)") + xlab("")

Spain, Netherlands, Portugal

1995-

Code
namq_10_a10_e %>%
  filter(na_item == "EMP_DC",
         nace_r2 %in% c("C", "TOTAL", "B-E"),
         geo %in% c("ES", "NL", "PT"),
         s_adj == "SCA",
         unit== "THS_HW") %>%
  quarter_to_date() %>%
  left_join(geo, by = "geo") %>%
  left_join(nace_r2, by = "nace_r2") %>%
  group_by(date) %>%
  mutate(values = values/ values[nace_r2 == "TOTAL"]) %>%
  filter(nace_r2 != "TOTAL",
         date >= as.Date("1995-01-01")) %>%
  left_join(colors, by = c("Geo" = "country")) %>%
  mutate(Geo = ifelse(geo == "DE", "Germany", Geo)) %>%
  ggplot(.) + geom_line(aes(x = date, y = values, color = color, linetype = Nace_r2)) +
  scale_color_identity() +
  scale_linetype_manual(values = c("solid", "dashed")) +
  theme_minimal() +
  scale_x_date(breaks = seq(1920, 2100, 5) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  theme(legend.position = c(0.7, 0.8),
        legend.title = element_blank()) +
  add_6flags +
  scale_y_continuous(breaks = 0.01*seq(-60, 60, 2),
                     labels = scales::percent_format(accuracy = 1)) +
  ylab("Emploi Manufacturier, Industriel (% de l'Emploi)") + xlab("")

2008-

Code
namq_10_a10_e %>%
  filter(na_item == "EMP_DC",
         nace_r2 %in% c("C", "TOTAL", "B-E"),
         geo %in% c("ES", "NL", "PT"),
         s_adj == "SCA",
         unit== "THS_HW") %>%
  quarter_to_date() %>%
  left_join(geo, by = "geo") %>%
  left_join(nace_r2, by = "nace_r2") %>%
  group_by(date) %>%
  mutate(values = values/ values[nace_r2 == "TOTAL"]) %>%
  filter(nace_r2 != "TOTAL",
         date >= as.Date("2008-01-01")) %>%
  left_join(colors, by = c("Geo" = "country")) %>%
  mutate(Geo = ifelse(geo == "DE", "Germany", Geo)) %>%
  ggplot(.) + geom_line(aes(x = date, y = values, color = color, linetype = Nace_r2)) +
  scale_color_identity() +
  scale_linetype_manual(values = c("solid", "dashed")) +
  theme_minimal() +
  scale_x_date(breaks = seq(1920, 2100, 2) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  theme(legend.position = c(0.2, 0.5),
        legend.title = element_blank()) +
  add_6flags +
  scale_y_continuous(breaks = 0.01*seq(-60, 60, 1),
                     labels = scales::percent_format(accuracy = 1)) +
  ylab("Emploi Manufacturier, Industriel (% de l'Emploi)") + xlab("")

% of Total Employment

France

Code
load_data("eurostat/nace_r2.RData")
namq_10_a10_e %>%
  filter(na_item == "EMP_DC",
         nace_r2 %in% c("C", "TOTAL", "L", "F"),
         geo %in% c("FR"),
         s_adj == "SCA",
         unit== "THS_HW") %>%
  quarter_to_date() %>%
  left_join(nace_r2, by = "nace_r2") %>%
  select(nace_r2, Nace_r2, date, values) %>%
  group_by(date) %>%
  mutate(values = values/ values[nace_r2 == "TOTAL"]) %>%
  filter(nace_r2 != "TOTAL")  %>%
  ggplot(.) + geom_line(aes(x = date, y = values, color = Nace_r2)) + 
  theme_minimal() + xlab("") + ylab("% of Employment") +
  scale_color_manual(values = viridis(4)[1:3]) +
  scale_x_date(breaks = seq(1960, 2020, 5) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  scale_y_continuous(breaks = 0.01*seq(-500, 200, 1),
                     labels = percent_format(accuracy = 1)) +
  theme(legend.position = c(0.75, 0.85),
        legend.title = element_blank())

Germany

Code
namq_10_a10_e %>%
  filter(na_item == "EMP_DC",
         nace_r2 %in% c("C", "TOTAL", "L", "F"),
         geo %in% c("DE"),
         s_adj == "SCA",
         unit== "THS_HW") %>%
  quarter_to_date() %>%
  left_join(nace_r2, by = "nace_r2") %>%
  select(nace_r2, Nace_r2, date, values) %>%
  group_by(date) %>%
  mutate(values = values/ values[nace_r2 == "TOTAL"]) %>%
  filter(nace_r2 != "TOTAL")  %>%
  ggplot(.) + geom_line(aes(x = date, y = values, color = Nace_r2)) + 
  theme_minimal() + xlab("") + ylab("% of Employment") +
  scale_color_manual(values = viridis(4)[1:3]) +
  scale_x_date(breaks = seq(1960, 2020, 5) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  scale_y_continuous(breaks = 0.01*seq(-500, 200, 1),
                     labels = percent_format(accuracy = 1)) +
  theme(legend.position = c(0.75, 0.85),
        legend.title = element_blank())

Italy

Code
namq_10_a10_e %>%
  filter(na_item == "EMP_DC",
         nace_r2 %in% c("C", "TOTAL", "L", "F"),
         geo %in% c("IT"),
         s_adj == "SCA",
         unit== "THS_HW") %>%
  quarter_to_date() %>%
  left_join(nace_r2, by = "nace_r2") %>%
  select(nace_r2, Nace_r2, date, values) %>%
  group_by(date) %>%
  mutate(values = values/ values[nace_r2 == "TOTAL"]) %>%
  filter(nace_r2 != "TOTAL")  %>%
  ggplot(.) + geom_line(aes(x = date, y = values, color = Nace_r2)) + 
  theme_minimal() + xlab("") + ylab("% of Employment") +
  scale_color_manual(values = viridis(4)[1:3]) +
  scale_x_date(breaks = seq(1960, 2020, 5) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  scale_y_continuous(breaks = 0.01*seq(-500, 200, 1),
                     labels = percent_format(accuracy = 1)) +
  theme(legend.position = c(0.75, 0.85),
        legend.title = element_blank())

Spain

Code
namq_10_a10_e %>%
  filter(na_item == "EMP_DC",
         nace_r2 %in% c("C", "TOTAL", "L", "F"),
         geo %in% c("ES"),
         s_adj == "SCA",
         unit== "THS_HW") %>%
  quarter_to_date() %>%
  left_join(nace_r2, by = "nace_r2") %>%
  select(nace_r2, Nace_r2, date, values) %>%
  group_by(date) %>%
  mutate(values = values/ values[nace_r2 == "TOTAL"]) %>%
  filter(nace_r2 != "TOTAL")  %>%
  ggplot(.) + geom_line(aes(x = date, y = values, color = Nace_r2)) + 
  theme_minimal() + xlab("") + ylab("% of Employment") +
  scale_color_manual(values = viridis(4)[1:3]) +
  scale_x_date(breaks = seq(1960, 2020, 5) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  scale_y_continuous(breaks = 0.01*seq(-500, 200, 1),
                     labels = percent_format(accuracy = 1)) +
  theme(legend.position = c(0.75, 0.85),
        legend.title = element_blank())

Greece

Code
namq_10_a10_e %>%
  filter(na_item == "EMP_DC",
         nace_r2 %in% c("C", "TOTAL", "L", "F"),
         geo %in% c("EL"),
         s_adj == "SCA",
         unit== "THS_HW") %>%
  quarter_to_date() %>%
  left_join(nace_r2, by = "nace_r2") %>%
  select(nace_r2, Nace_r2, date, values) %>%
  group_by(date) %>%
  mutate(values = values/ values[nace_r2 == "TOTAL"]) %>%
  filter(nace_r2 != "TOTAL")  %>%
  ggplot(.) + geom_line(aes(x = date, y = values, color = Nace_r2)) + 
  theme_minimal() + xlab("") + ylab("% of Employment") +
  scale_color_manual(values = viridis(4)[1:3]) +
  scale_x_date(breaks = seq(1960, 2020, 5) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  scale_y_continuous(breaks = 0.01*seq(-500, 200, 1),
                     labels = percent_format(accuracy = 1)) +
  theme(legend.position = c(0.8, 0.9),
        legend.title = element_blank())

Netherlands

Code
namq_10_a10_e %>%
  filter(na_item == "EMP_DC",
         nace_r2 %in% c("C", "TOTAL", "L", "F"),
         geo %in% c("NL"),
         s_adj == "SCA",
         unit== "THS_HW") %>%
  quarter_to_date() %>%
  left_join(nace_r2, by = "nace_r2") %>%
  select(nace_r2, Nace_r2, date, values) %>%
  group_by(date) %>%
  mutate(values = values/ values[nace_r2 == "TOTAL"]) %>%
  filter(nace_r2 != "TOTAL")  %>%
  ggplot(.) + geom_line(aes(x = date, y = values, color = Nace_r2)) + 
  theme_minimal() + xlab("") + ylab("% of Employment") +
  scale_color_manual(values = viridis(4)[1:3]) +
  scale_x_date(breaks = seq(1960, 2020, 5) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  scale_y_continuous(breaks = 0.01*seq(-500, 200, 1),
                     labels = percent_format(accuracy = 1)) +
  theme(legend.position = c(0.8, 0.9),
        legend.title = element_blank())

Denmark

Code
namq_10_a10_e %>%
  filter(na_item == "EMP_DC",
         nace_r2 %in% c("C", "TOTAL", "L", "F"),
         geo %in% c("DK"),
         s_adj == "SCA",
         unit== "THS_HW") %>%
  quarter_to_date() %>%
  left_join(nace_r2, by = "nace_r2") %>%
  select(nace_r2, Nace_r2, date, values) %>%
  group_by(date) %>%
  mutate(values = values/ values[nace_r2 == "TOTAL"]) %>%
  filter(nace_r2 != "TOTAL")  %>%
  ggplot(.) + geom_line(aes(x = date, y = values, color = Nace_r2)) + 
  theme_minimal() + xlab("") + ylab("% of Employment") +
  scale_color_manual(values = viridis(4)[1:3]) +
  scale_x_date(breaks = seq(1960, 2020, 5) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  scale_y_continuous(breaks = 0.01*seq(-500, 200, 1),
                     labels = percent_format(accuracy = 1)) +
  theme(legend.position = c(0.8, 0.9),
        legend.title = element_blank())

Finland

Code
namq_10_a10_e %>%
  filter(na_item == "EMP_DC",
         nace_r2 %in% c("C", "TOTAL", "L", "F"),
         geo %in% c("FI"),
         s_adj == "SCA",
         unit== "THS_HW") %>%
  quarter_to_date() %>%
  left_join(nace_r2, by = "nace_r2") %>%
  select(nace_r2, Nace_r2, date, values) %>%
  group_by(date) %>%
  mutate(values = values/ values[nace_r2 == "TOTAL"]) %>%
  filter(nace_r2 != "TOTAL")  %>%
  ggplot(.) + geom_line(aes(x = date, y = values, color = Nace_r2)) + 
  theme_minimal() + xlab("") + ylab("% of Employment") +
  scale_color_manual(values = viridis(4)[1:3]) +
  scale_x_date(breaks = seq(1960, 2020, 5) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  scale_y_continuous(breaks = 0.01*seq(-500, 200, 1),
                     labels = percent_format(accuracy = 1)) +
  theme(legend.position = c(0.8, 0.9),
        legend.title = element_blank())

Poland

Code
namq_10_a10_e %>%
  filter(na_item == "EMP_DC",
         nace_r2 %in% c("C", "TOTAL", "L", "F"),
         geo %in% c("PL"),
         s_adj == "SCA",
         unit== "THS_HW") %>%
  quarter_to_date() %>%
  left_join(nace_r2, by = "nace_r2") %>%
  select(nace_r2, Nace_r2, date, values) %>%
  group_by(date) %>%
  mutate(values = values/ values[nace_r2 == "TOTAL"]) %>%
  filter(nace_r2 != "TOTAL")  %>%
  ggplot(.) + geom_line(aes(x = date, y = values, color = Nace_r2)) + 
  theme_minimal() + xlab("") + ylab("% of Employment") +
  scale_color_manual(values = viridis(4)[1:3]) +
  scale_x_date(breaks = seq(1960, 2020, 5) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  scale_y_continuous(breaks = 0.01*seq(-500, 200, 1),
                     labels = percent_format(accuracy = 1)) +
  theme(legend.position = c(0.8, 0.9),
        legend.title = element_blank())

Hungary

Code
namq_10_a10_e %>%
  filter(na_item == "EMP_DC",
         nace_r2 %in% c("C", "TOTAL", "L", "F"),
         geo %in% c("HU"),
         s_adj == "SCA",
         unit== "THS_HW") %>%
  quarter_to_date() %>%
  left_join(nace_r2, by = "nace_r2") %>%
  select(nace_r2, Nace_r2, date, values) %>%
  group_by(date) %>%
  mutate(values = values/ values[nace_r2 == "TOTAL"]) %>%
  filter(nace_r2 != "TOTAL")  %>%
  ggplot(.) + geom_line(aes(x = date, y = values, color = Nace_r2)) + 
  theme_minimal() + xlab("") + ylab("% of Employment") +
  scale_color_manual(values = viridis(4)[1:3]) +
  scale_x_date(breaks = seq(1960, 2020, 5) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  scale_y_continuous(breaks = 0.01*seq(-500, 200, 1),
                     labels = percent_format(accuracy = 1)) +
  theme(legend.position = c(0.8, 0.9),
        legend.title = element_blank())

Portugal

Code
namq_10_a10_e %>%
  filter(na_item == "EMP_DC",
         nace_r2 %in% c("C", "TOTAL", "L", "F"),
         geo %in% c("PT"),
         s_adj == "SCA",
         unit== "THS_HW") %>%
  quarter_to_date() %>%
  left_join(nace_r2, by = "nace_r2") %>%
  select(nace_r2, Nace_r2, date, values) %>%
  group_by(date) %>%
  mutate(values = values/ values[nace_r2 == "TOTAL"]) %>%
  filter(nace_r2 != "TOTAL")  %>%
  ggplot(.) + geom_line(aes(x = date, y = values, color = Nace_r2)) + 
  theme_minimal() + xlab("") + ylab("% of Employment") +
  scale_color_manual(values = viridis(4)[1:3]) +
  scale_x_date(breaks = seq(1960, 2020, 5) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  scale_y_continuous(breaks = 0.01*seq(-500, 200, 1),
                     labels = percent_format(accuracy = 1)) +
  theme(legend.position = c(0.8, 0.9),
        legend.title = element_blank())

Austria

Code
namq_10_a10_e %>%
  filter(na_item == "EMP_DC",
         nace_r2 %in% c("C", "TOTAL", "L", "F"),
         geo %in% c("AT"),
         s_adj == "SCA",
         unit== "THS_HW") %>%
  quarter_to_date() %>%
  left_join(nace_r2, by = "nace_r2") %>%
  select(nace_r2, Nace_r2, date, values) %>%
  group_by(date) %>%
  mutate(values = values/ values[nace_r2 == "TOTAL"]) %>%
  filter(nace_r2 != "TOTAL")  %>%
  ggplot(.) + geom_line(aes(x = date, y = values, color = Nace_r2)) + 
  theme_minimal() + xlab("") + ylab("% of Employment") +
  scale_color_manual(values = viridis(4)[1:3]) +
  scale_x_date(breaks = seq(1960, 2020, 5) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  scale_y_continuous(breaks = 0.01*seq(-500, 200, 1),
                     labels = percent_format(accuracy = 1)) +
  theme(legend.position = c(0.8, 0.9),
        legend.title = element_blank())

Sweden

Code
namq_10_a10_e %>%
  filter(na_item == "EMP_DC",
         nace_r2 %in% c("C", "TOTAL", "L", "F"),
         geo %in% c("SE"),
         s_adj == "SCA",
         unit== "THS_HW") %>%
  quarter_to_date() %>%
  left_join(nace_r2, by = "nace_r2") %>%
  select(nace_r2, Nace_r2, date, values) %>%
  group_by(date) %>%
  mutate(values = values/ values[nace_r2 == "TOTAL"]) %>%
  filter(nace_r2 != "TOTAL")  %>%
  ggplot(.) + geom_line(aes(x = date, y = values, color = Nace_r2)) + 
  theme_minimal() + xlab("") + ylab("% of Employment") +
  scale_color_manual(values = viridis(4)[1:3]) +
  scale_x_date(breaks = seq(1960, 2020, 5) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  scale_y_continuous(breaks = 0.01*seq(-500, 200, 1),
                     labels = percent_format(accuracy = 1)) +
  theme(legend.position = c(0.8, 0.9),
        legend.title = element_blank())

United Kingdom

Code
namq_10_a10_e %>%
  filter(na_item == "EMP_DC",
         nace_r2 %in% c("C", "TOTAL", "L", "F"),
         geo %in% c("UK"),
         s_adj == "SCA",
         unit== "THS_HW") %>%
  quarter_to_date() %>%
  left_join(nace_r2, by = "nace_r2") %>%
  select(nace_r2, Nace_r2, date, values) %>%
  group_by(date) %>%
  mutate(values = values/ values[nace_r2 == "TOTAL"]) %>%
  filter(nace_r2 != "TOTAL")  %>%
  ggplot(.) + geom_line(aes(x = date, y = values, color = Nace_r2)) + 
  theme_minimal() + xlab("") + ylab("% of Employment") +
  scale_color_manual(values = viridis(4)[1:3]) +
  scale_x_date(breaks = seq(1960, 2020, 5) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  scale_y_continuous(breaks = 0.01*seq(-500, 200, 1),
                     labels = percent_format(accuracy = 1)) +
  theme(legend.position = c(0.8, 0.9),
        legend.title = element_blank())

France, Germany, Italy

Number of hours worked

  • La durée individuelle du travail. html

  • Durée du travail. pdf

All

Code
namq_10_a10_e %>%
  left_join(geo, by = "geo") %>%
  filter(geo %in% c("FR", "DE", "IT"),
         nace_r2 == "C",
         s_adj == "SCA",
         unit == "THS_HW",
         na_item == "EMP_DC") %>%
  quarter_to_date() %>%
  arrange(date) %>%
  mutate(values = values/1000) %>%
  left_join(colors, by = c("Geo" = "country")) %>%
  ggplot(.) + geom_line(aes(x = date, y = values, color = color)) + 
  theme_minimal() + xlab("") + ylab("Number of Hours Worked, Millions") +
  scale_x_date(breaks = seq(1960, 2020, 5) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  scale_y_continuous(breaks = seq(0, 1000000, 500),
                     labels = dollar_format(accuracy = 1, prefix = "", suffix = "M")) +
  scale_color_identity() + add_3flags +
  theme(legend.position = c(0.2, 0.80),
        legend.title = element_blank())

2000-

Code
namq_10_a10_e %>%
  left_join(geo, by = "geo") %>%
  filter(geo %in% c("FR", "DE", "IT"),
         nace_r2 == "C",
         s_adj == "SCA",
         unit == "THS_HW",
         na_item == "EMP_DC") %>%
  quarter_to_date() %>%
  filter(date >= as.Date("2000-01-01")) %>%
  arrange(date) %>%
  mutate(values = values/1000) %>%
  left_join(colors, by = c("Geo" = "country")) %>%
  ggplot(.) + geom_line(aes(x = date, y = values, color = color)) + 
  theme_minimal() + xlab("") + ylab("Number of Hours Worked, Millions") +
  scale_x_date(breaks = seq(1960, 2020, 5) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  scale_y_continuous(breaks = seq(0, 1000000, 500),
                     labels = dollar_format(accuracy = 1, prefix = "", suffix = "M")) +
  scale_color_identity() + add_3flags +
  theme(legend.position = c(0.2, 0.80),
        legend.title = element_blank())

Number of employees

All

Code
namq_10_a10_e %>%
  left_join(geo, by = "geo") %>%
  filter(geo %in% c("FR", "DE", "IT"),
         nace_r2 == "TOTAL",
         s_adj == "NSA",
         unit == "THS_PER",
         na_item == "EMP_DC") %>%
  quarter_to_date() %>%
  arrange(date) %>%
  left_join(colors, by = c("Geo" = "country")) %>%
  ggplot(.) + geom_line(aes(x = date, y = values, color = color)) + 
  theme_minimal() + xlab("") + ylab("Number of Employees, '000") +
  scale_x_date(breaks = seq(1960, 2020, 5) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  scale_y_continuous(breaks = seq(0, 1000000, 5000),
                     labels = dollar_format(accuracy = 1, prefix = "", suffix = "M")) +
  scale_color_identity() + add_3flags +
  theme(legend.position = c(0.2, 0.80),
        legend.title = element_blank())

2000-

Code
namq_10_a10_e %>%
  left_join(geo, by = "geo") %>%
  filter(geo %in% c("FR", "DE", "IT"),
         nace_r2 == "TOTAL",
         s_adj == "NSA",
         unit == "THS_PER",
         na_item == "EMP_DC") %>%
  quarter_to_date() %>%
  filter(date >= as.Date("2000-01-01")) %>%
  arrange(date) %>%
  left_join(colors, by = c("Geo" = "country")) %>%
  ggplot(.) + geom_line(aes(x = date, y = values, color = color)) + 
  theme_minimal() + xlab("") + ylab("Number of Employees, '000") +
  scale_x_date(breaks = seq(1960, 2020, 5) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  scale_y_continuous(breaks = seq(0, 1000000, 5000),
                     labels = dollar_format(accuracy = 1, prefix = "", suffix = "M")) +
  scale_color_identity() + add_3flags +
  theme(legend.position = c(0.2, 0.80),
        legend.title = element_blank())

2010-

Code
namq_10_a10_e %>%
  left_join(geo, by = "geo") %>%
  filter(geo %in% c("FR", "DE", "IT"),
         nace_r2 == "TOTAL",
         s_adj == "NSA",
         unit == "THS_PER",
         na_item == "EMP_DC") %>%
  quarter_to_date() %>%
  filter(date >= as.Date("2010-01-01")) %>%
  arrange(date) %>%
  left_join(colors, by = c("Geo" = "country")) %>%
  ggplot(.) + geom_line(aes(x = date, y = values, color = color)) + 
  theme_minimal() + xlab("") + ylab("Number of Employees, '000") +
  scale_x_date(breaks = seq(1960, 2026, 2) %>% paste0("-01-01") %>% as.Date,
               labels = date_format("%Y")) +
  scale_y_continuous(breaks = seq(0, 1000000, 5000),
                     labels = dollar_format(accuracy = 1, prefix = "", suffix = "M")) +
  scale_color_identity() + add_3flags +
  theme(legend.position = c(0.2, 0.80),
        legend.title = element_blank())