Employment in industry (% of total employment)
Data - WDI
François Geerolf
Info
| source | dataset | .html | .RData | .Rmd |
|---|---|---|---|---|
| wdi | SL.IND.EMPL.ZS | 2023-12-24 | 2023-07-22 | 2023-07-22 |
Data on industry
| source | dataset | Title | Download | Compile |
|---|---|---|---|---|
| wdi | SL.IND.EMPL.ZS | Employment in industry (% of total employment) | 2023-07-22 | 2023-12-24 |
| ec | INDUSTRY | Industry (sector data) | 2023-10-01 | 2023-10-01 |
| eurostat | ei_isin_m | Industry - monthly data - index (2015 = 100) (NACE Rev. 2) - ei_isin_m | 2023-12-24 | 2023-12-31 |
| eurostat | htec_trd_group4 | High-tech trade by high-tech group of products in million euro (from 2007, SITC Rev. 4) | 2023-12-24 | 2023-12-31 |
| eurostat | nama_10_a64 | National accounts aggregates by industry (up to NACE A*64) | 2023-12-24 | 2023-12-31 |
| eurostat | nama_10_a64_e | National accounts employment data by industry (up to NACE A*64) | 2023-12-31 | 2023-12-24 |
| eurostat | namq_10_a10_e | Employment A*10 industry breakdowns | 2023-12-31 | 2023-12-27 |
| eurostat | road_eqr_carmot | New registrations of passenger cars by type of motor energy and engine size - road_eqr_carmot | 2023-12-24 | 2023-12-31 |
| eurostat | sts_inpp_m | Producer prices in industry, total - monthly data | 2023-12-24 | 2024-01-02 |
| eurostat | sts_inppd_m | Producer prices in industry, domestic market - monthly data | 2023-12-31 | 2023-12-24 |
| eurostat | sts_inpr_m | Production in industry - monthly data | 2023-12-24 | 2023-12-31 |
| eurostat | sts_intvnd_m | Turnover in industry, non domestic market - monthly data - sts_intvnd_m | 2023-12-24 | 2023-12-31 |
| fred | industry | Manufacturing, Industry | 2024-01-06 | 2024-01-06 |
| oecd | ALFS_EMP | Employment by activities and status (ALFS) | 2023-10-30 | 2024-01-05 |
| oecd | BERD_MA_SOF | Business enterprise R&D expenditure by main activity (focussed) and source of funds | 2023-09-09 | 2024-01-05 |
| oecd | GBARD_NABS2007 | Government budget allocations for R and D | 2023-11-22 | 2024-01-05 |
| oecd | MEI_REAL | Production and Sales (MEI) | 2023-12-27 | 2024-01-05 |
| oecd | MSTI_PUB | Main Science and Technology Indicators | 2023-10-04 | 2024-01-05 |
| oecd | SNA_TABLE4 | PPPs and exchange rates | 2024-01-06 | 2024-01-06 |
| wdi | NV.IND.EMPL.KD | Industry, value added per worker (constant 2010 USD) | 2023-06-18 | 2024-01-06 |
| wdi | NV.IND.MANF.CD | Manufacturing, value added (current USD) | 2023-12-17 | 2024-01-06 |
| wdi | NV.IND.MANF.ZS | Manufacturing, value added (% of GDP) | 2023-09-20 | 2024-01-06 |
| wdi | NV.IND.TOTL.KD | Industry (including construction), value added (constant 2015 USD) - NV.IND.TOTL.KD | 2022-09-27 | 2024-01-06 |
| wdi | NV.IND.TOTL.ZS | Industry, value added (including construction) (% of GDP) | 2023-09-20 | 2024-01-06 |
| wdi | TX.VAL.MRCH.CD.WT | Merchandise exports (current USD) | 2023-04-12 | 2023-12-24 |
LAST_COMPILE
| LAST_COMPILE |
|---|
| 2024-01-06 |
Last
| year | Nobs |
|---|---|
| 2021 | 233 |
| 2020 | 235 |
| 2019 | 235 |
Nobs - Javascript
SL.IND.EMPL.ZS %>%
left_join(iso2c, by = "iso2c") %>%
group_by(iso2c, Iso2c) %>%
rename(value = `SL.IND.EMPL.ZS`) %>%
mutate(value = round(value, 1)) %>%
summarise(Nobs = n(),
`Year 1` = first(year),
`Industry Share 1 (%)` = first(value),
`Year 2` = last(year),
`Industry Share 2 (%)` = last(value),
growth = 100*(`Industry Share 2 (%)`/ `Industry Share 1 (%)`-1),
growth_pp = `Industry Share 2 (%)`- `Industry Share 1 (%)`) %>%
arrange(growth) %>%
mutate(Flag = gsub(" ", "-", str_to_lower(Iso2c)),
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 .}Southern Cone
1994: Mexico
SL.IND.EMPL.ZS %>%
year_to_date %>%
filter(iso2c %in% c("MX"),
date <= as.Date("1999-01-01"),
date >= as.Date("1989-01-01")) %>%
left_join(iso2c, by = "iso2c") %>%
ggplot(.) +
geom_line(aes(x = date, y = SL.IND.EMPL.ZS/100)) +
theme_minimal() + scale_color_manual(values = viridis(5)[1:4]) +
theme(legend.title = element_blank(),
legend.position = c(0.2, 0.8)) +
scale_x_date(breaks = seq(1950, 2020, 1) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
scale_y_continuous(breaks = 0.01*seq(-60, 60, 1),
labels = scales::percent_format(accuracy = 1)) +
xlab("") + ylab("Employment in industry (% of total employment) ")
1997: East Asia - Indonesia, Korea, Malaysia, Thailand
SL.IND.EMPL.ZS %>%
year_to_date %>%
filter(iso2c %in% c("ID", "KR", "MY", "TH"),
date <= as.Date("2002-01-01"),
date >= as.Date("1992-01-01")) %>%
left_join(iso2c, by = "iso2c") %>%
mutate(Iso2c = ifelse(iso2c == "KR", "South Korea", Iso2c)) %>%
left_join(colors, by = c("Iso2c" = "country")) %>%
mutate(value = SL.IND.EMPL.ZS/100) %>%
ggplot(.) + geom_line(aes(x = date, y = value, color = color)) +
xlab("") + ylab("Employment in industry (%)") +
theme_minimal() + scale_color_identity() + add_4flags +
scale_x_date(breaks = seq(1950, 2020, 1) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
scale_y_continuous(breaks = 0.01*seq(-60, 60, 2),
labels = scales::percent_format(accuracy = 1))
2001: Turkey
SL.IND.EMPL.ZS %>%
year_to_date %>%
filter(iso2c %in% c("TR"),
date <= as.Date("2006-01-01"),
date >= as.Date("1996-01-01")) %>%
left_join(iso2c, by = "iso2c") %>%
ggplot(.) +
geom_line(aes(x = date, y = SL.IND.EMPL.ZS/100)) +
theme_minimal() + scale_color_manual(values = viridis(5)[1:4]) +
theme(legend.title = element_blank(),
legend.position = c(0.2, 0.8)) +
scale_x_date(breaks = seq(1950, 2020, 1) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
scale_y_continuous(breaks = 0.01*seq(-60, 60, 1),
labels = scales::percent_format(accuracy = 1)) +
xlab("") + ylab("Employment in industry (% of total employment) ")
Iceland
SL.IND.EMPL.ZS %>%
filter(iso2c %in% c("IS")) %>%
left_join(iso2c, by = "iso2c") %>%
year_to_date %>%
ggplot(.) + geom_line(aes(x = date, y = SL.IND.EMPL.ZS/100)) +
xlab("") + ylab("Employment in industry (% of total employment) ") + theme_minimal() +
theme(legend.title = element_blank(),
legend.position = c(0.2, 0.2)) +
scale_x_date(breaks = seq(1950, 2020, 2) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
scale_y_continuous(breaks = 0.01*seq(-60, 60, 1),
labels = scales::percent_format(accuracy = 1))
Japan
SL.IND.EMPL.ZS %>%
filter(iso2c %in% c("JP")) %>%
left_join(iso2c, by = "iso2c") %>%
year_to_date %>%
ggplot(.) + geom_line(aes(x = date, y = SL.IND.EMPL.ZS/100)) +
xlab("") + ylab("Employment in industry (% of total employment) ") + theme_minimal() +
theme(legend.title = element_blank(),
legend.position = c(0.2, 0.2)) +
scale_x_date(breaks = seq(1950, 2020, 2) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
scale_y_continuous(breaks = 0.01*seq(-60, 60, 1),
labels = scales::percent_format(accuracy = 1))
Finland, Sweden, France
SL.IND.EMPL.ZS %>%
filter(iso2c %in% c("FI", "SE", "FR")) %>%
left_join(iso2c, by = "iso2c") %>%
left_join(colors, by = c("Iso2c" = "country")) %>%
year_to_date %>%
mutate(value = SL.IND.EMPL.ZS/100) %>%
ggplot(.) + geom_line(aes(x = date, y = value, color = color)) +
xlab("") + ylab("Employment in industry (%)") +
theme_minimal() + scale_color_identity() + add_3flags +
scale_x_date(breaks = seq(1950, 2020, 5) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
scale_y_continuous(breaks = 0.01*seq(-60, 60, 2),
labels = scales::percent_format(accuracy = 1))
France, Germany, OECD
SL.IND.EMPL.ZS %>%
filter(iso2c %in% c("OE", "FR", "DE", "DK")) %>%
left_join(iso2c, by = "iso2c") %>%
left_join(colors, by = c("Iso2c" = "country")) %>%
year_to_date %>%
mutate(value = SL.IND.EMPL.ZS/100) %>%
ggplot(.) + geom_line(aes(x = date, y = value, color = color)) +
xlab("") + ylab("Employment in industry (%)") +
theme_minimal() + scale_color_identity() + add_4flags +
scale_x_date(breaks = seq(1950, 2020, 5) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
scale_y_continuous(breaks = 0.01*seq(-60, 60, 2),
labels = scales::percent_format(accuracy = 1))
France, Germany, OECD
SL.IND.EMPL.ZS %>%
filter(iso2c %in% c("OE", "FR", "DE")) %>%
left_join(iso2c, by = "iso2c") %>%
left_join(colors, by = c("Iso2c" = "country")) %>%
year_to_date %>%
mutate(value = SL.IND.EMPL.ZS/100) %>%
ggplot(.) + geom_line(aes(x = date, y = value, color = color)) +
xlab("") + ylab("Employment in industry (%)") +
theme_minimal() + scale_color_identity() + add_3flags +
scale_x_date(breaks = seq(1950, 2020, 5) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
scale_y_continuous(breaks = 0.01*seq(-60, 60, 2),
labels = scales::percent_format(accuracy = 1))
China, France, Germany
SL.IND.EMPL.ZS %>%
filter(iso2c %in% c("CN", "FR", "DE")) %>%
left_join(iso2c, by = "iso2c") %>%
left_join(colors, by = c("Iso2c" = "country")) %>%
year_to_date %>%
mutate(value = SL.IND.EMPL.ZS/100) %>%
ggplot(.) + geom_line(aes(x = date, y = value, color = color)) +
xlab("") + ylab("Employment in industry (%)") +
theme_minimal() + scale_color_identity() + add_3flags +
scale_x_date(breaks = seq(1950, 2020, 5) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
scale_y_continuous(breaks = 0.01*seq(-60, 60, 2),
labels = scales::percent_format(accuracy = 1))
China, India, France, Germany, Japan, US, UK
SL.IND.EMPL.ZS %>%
filter(iso2c %in% c("CN", "FR", "DE", "IN", "GB", "US", "JP")) %>%
left_join(iso2c, by = "iso2c") %>%
left_join(colors, by = c("Iso2c" = "country")) %>%
year_to_date %>%
mutate(value = SL.IND.EMPL.ZS/100) %>%
ggplot(.) + geom_line(aes(x = date, y = value, color = color)) +
xlab("") + ylab("Employment in industry (%)") +
theme_minimal() + scale_color_identity() + add_7flags +
scale_x_date(breaks = seq(1950, 2020, 5) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
scale_y_continuous(breaks = 0.01*seq(-60, 60, 2),
labels = scales::percent_format(accuracy = 1))
Italy, Portugal, Spain
SL.IND.EMPL.ZS %>%
filter(iso2c %in% c("ES", "IT", "PT")) %>%
left_join(iso2c, by = "iso2c") %>%
left_join(colors, by = c("Iso2c" = "country")) %>%
year_to_date %>%
mutate(value = SL.IND.EMPL.ZS/100) %>%
ggplot(.) + geom_line(aes(x = date, y = value, color = color)) +
xlab("") + ylab("Employment in industry (%)") +
theme_minimal() + scale_color_identity() + add_3flags +
scale_x_date(breaks = seq(1950, 2020, 5) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
scale_y_continuous(breaks = 0.01*seq(-60, 60, 2),
labels = scales::percent_format(accuracy = 1))
Spain, United Kingdom, United States
SL.IND.EMPL.ZS %>%
filter(iso2c %in% c("US", "GB", "ES")) %>%
left_join(iso2c, by = "iso2c") %>%
left_join(colors, by = c("Iso2c" = "country")) %>%
year_to_date %>%
mutate(value = SL.IND.EMPL.ZS/100) %>%
ggplot(.) + geom_line(aes(x = date, y = value, color = color)) +
xlab("") + ylab("Employment in industry (%)") +
theme_minimal() + scale_color_identity() + add_3flags +
scale_x_date(breaks = seq(1950, 2020, 5) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
scale_y_continuous(breaks = 0.01*seq(-60, 60, 2),
labels = scales::percent_format(accuracy = 1))
Argentina, Chile, Venezuela
SL.IND.EMPL.ZS %>%
filter(iso2c %in% c("AR", "CL", "VE")) %>%
left_join(iso2c, by = "iso2c") %>%
mutate(Iso2c = ifelse(iso2c == "VE", "Venezuela", Iso2c)) %>%
left_join(colors, by = c("Iso2c" = "country")) %>%
year_to_date %>%
mutate(value = SL.IND.EMPL.ZS/100) %>%
ggplot(.) + geom_line(aes(x = date, y = value, color = color)) +
xlab("") + ylab("Employment in industry (%)") +
theme_minimal() + scale_color_identity() + add_3flags +
scale_x_date(breaks = seq(1950, 2020, 5) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
scale_y_continuous(breaks = 0.01*seq(-60, 60, 2),
labels = scales::percent_format(accuracy = 1))