Industry, value added per worker (constant 2010 USD)
Data - WDI
François Geerolf
Info
Data on industry
| source | dataset | Title | Download | Compile |
|---|---|---|---|---|
| wdi | NV.IND.EMPL.KD | Industry, value added per worker (constant 2010 USD) | 2023-06-18 | 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.MANF.CD | Manufacturing, value added (current USD) | 2023-12-17 | 2023-12-24 |
| wdi | NV.IND.MANF.ZS | Manufacturing, value added (% of GDP) | 2023-09-20 | 2023-12-24 |
| wdi | NV.IND.TOTL.KD | Industry (including construction), value added (constant 2015 USD) - NV.IND.TOTL.KD | 2022-09-27 | 2023-12-24 |
| wdi | NV.IND.TOTL.ZS | Industry, value added (including construction) (% of GDP) | 2023-09-20 | 2023-12-24 |
| wdi | SL.IND.EMPL.ZS | Employment in industry (% of total employment) | 2023-07-22 | 2023-12-24 |
| 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 |
|---|---|
| 2019 | 207 |
| 2018 | 211 |
| 2017 | 211 |
Nobs - Javascript
NV.IND.EMPL.KD %>%
left_join(iso2c, by = "iso2c") %>%
group_by(iso2c, Iso2c) %>%
rename(value = `NV.IND.EMPL.KD`) %>%
mutate(value = round(value, 1)) %>%
summarise(Nobs = n(),
`Year 1` = first(year),
`Value added in Industry 1 (%)` = first(value),
`Year 2` = last(year),
`Value added in Industry 2 (%)` = last(value)) %>%
arrange(-`Value added in Industry 2 (%)`) %>%
{if (is_html_output()) datatable(., filter = 'top', rownames = F) else .}Japan
NV.IND.EMPL.KD %>%
filter(iso2c %in% c("JP")) %>%
left_join(iso2c, by = "iso2c") %>%
year_to_date %>%
ggplot(.) + geom_line(aes(x = date, y = NV.IND.EMPL.KD)) +
xlab("") + ylab("Industry, value added per worker") + theme_minimal() +
theme(legend.title = element_blank(),
legend.position = c(0.2, 0.2)) +
scale_x_date(breaks = seq(1950, 2025, 5) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
scale_y_continuous(breaks = seq(0, 200000, 10000),
labels = comma)
China, France, Germany
NV.IND.EMPL.KD %>%
filter(iso2c %in% c("CN", "FR", "DE")) %>%
left_join(iso2c, by = "iso2c") %>%
year_to_date %>%
left_join(colors, by = c("Iso2c" = "country")) %>%
mutate(value = NV.IND.EMPL.KD) %>%
ggplot(.) + geom_line(aes(x = date, y = value, color = color)) +
theme_minimal() + scale_color_identity() + add_3flags + theme_minimal() +
theme(legend.title = element_blank(),
legend.position = c(0.15, 0.9)) +
scale_x_date(breaks = seq(1950, 2025, 5) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
scale_y_continuous(breaks = seq(0, 200000, 10000),
labels = dollar_format()) +
xlab("") + ylab("Industry, value added per worker")
Italy, Portugal, Spain
NV.IND.EMPL.KD %>%
filter(iso2c %in% c("ES", "IT", "PT")) %>%
left_join(iso2c, by = "iso2c") %>%
year_to_date %>%
left_join(colors, by = c("Iso2c" = "country")) %>%
mutate(value = NV.IND.EMPL.KD) %>%
ggplot(.) + geom_line(aes(x = date, y = value, color = color)) +
theme_minimal() + scale_color_identity() + add_3flags + theme_minimal() +
theme(legend.title = element_blank(),
legend.position = c(0.15, 0.9)) +
scale_x_date(breaks = seq(1950, 2025, 5) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
scale_y_continuous(breaks = seq(0, 200000, 10000),
labels = dollar_format()) +
xlab("") + ylab("Industry, value added per worker")
Spain, United Kingdom, United States
Linear
NV.IND.EMPL.KD %>%
filter(iso2c %in% c("US", "GB", "ES")) %>%
left_join(iso2c, by = "iso2c") %>%
year_to_date %>%
left_join(colors, by = c("Iso2c" = "country")) %>%
mutate(value = NV.IND.EMPL.KD) %>%
ggplot(.) + geom_line(aes(x = date, y = value, color = color)) +
theme_minimal() + scale_color_identity() + add_3flags + theme_minimal() +
theme(legend.title = element_blank(),
legend.position = c(0.15, 0.9)) +
scale_x_date(breaks = seq(1950, 2025, 5) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
scale_y_continuous(breaks = seq(0, 200000, 10000),
labels = dollar_format()) +
xlab("") + ylab("Industry, value added per worker")
Log
NV.IND.EMPL.KD %>%
filter(iso2c %in% c("US", "GB", "ES")) %>%
left_join(iso2c, by = "iso2c") %>%
year_to_date %>%
left_join(colors, by = c("Iso2c" = "country")) %>%
mutate(value = NV.IND.EMPL.KD) %>%
ggplot(.) + geom_line(aes(x = date, y = value, color = color)) +
theme_minimal() + scale_color_identity() + add_3flags + theme_minimal() +
theme(legend.title = element_blank(),
legend.position = c(0.15, 0.9)) +
scale_x_date(breaks = seq(1950, 2025, 5) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
scale_y_log10(breaks = seq(0, 200000, 10000),
labels = dollar_format()) +
xlab("") + ylab("Industry, value added per worker")
Argentina, Chile, Venezuela
NV.IND.EMPL.KD %>%
filter(iso2c %in% c("AR", "CL", "VE")) %>%
left_join(iso2c, by = "iso2c") %>%
year_to_date %>%
left_join(colors, by = c("Iso2c" = "country")) %>%
mutate(value = NV.IND.EMPL.KD) %>%
ggplot(.) + geom_line(aes(x = date, y = value, color = color)) +
theme_minimal() + scale_color_identity() + add_3flags + theme_minimal() +
theme(legend.title = element_blank(),
legend.position = c(0.15, 0.9)) +
scale_x_date(breaks = seq(1950, 2025, 5) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
scale_y_log10(breaks = seq(0, 200000, 10000),
labels = dollar_format()) +
xlab("") + ylab("Industry, value added per worker")
Greece, Hong Kong, Mexico
NV.IND.EMPL.KD %>%
filter(iso2c %in% c("GR", "HK", "MX")) %>%
left_join(iso2c, by = "iso2c") %>%
mutate(Iso2c = ifelse(iso2c == "HK", "Hong Kong", Iso2c)) %>%
year_to_date %>%
left_join(colors, by = c("Iso2c" = "country")) %>%
mutate(value = NV.IND.EMPL.KD) %>%
ggplot(.) + geom_line(aes(x = date, y = value, color = color)) +
theme_minimal() + scale_color_identity() + add_3flags + theme_minimal() +
theme(legend.title = element_blank(),
legend.position = c(0.15, 0.9)) +
scale_x_date(breaks = seq(1950, 2025, 5) %>% paste0("-01-01") %>% as.Date,
labels = date_format("%Y")) +
scale_y_log10(breaks = seq(0, 200000, 5000),
labels = dollar_format(a = 1)) +
xlab("") + ylab("Industry, value added per worker")