Economic data in high-tech sectors by NACE Rev.1.1 activity - htec_eco_sbs

Data - Eurostat

Info

Last observation: Annual: 2007 (N = 1,531)

First observation: Annual: 1995 (N = 533)

Last data update: 23 jul 2026, 22:20. Last compile: 24 jul 2026, 01:53

Structure

High-Technology Sectors: Value Added

France, Germany, Italy, Spain, United Kingdom

Code
htec_eco_sbs %>%
  filter(geo %in% c("FR", "DE", "IT", "ES", "UK"),
         nace_r1 == "HTC",
         indic_sb == "V12150") %>%
  year_to_date %>%
  mutate(values = values/1000) %>%
  left_join(colors, by = c("Geo" = "country")) %>%
  mutate(color = ifelse(geo == "ES", color2, color)) %>%
  ggplot + geom_line(aes(x = date, y = values, color = color)) +
  theme_minimal() + scale_color_identity() + add_5flags +
  scale_x_date(breaks = as.Date(paste0(seq(1995, 2100, 2), "-01-01")),
               labels = date_format("%Y")) +
  xlab("") + ylab("Value added at factor cost, high-technology sectors (Bn€)")

France: Manufacturing vs. Services

Code
htec_eco_sbs %>%
  filter(geo == "FR",
         nace_r1 %in% c("D_HTC", "KIS_HTC"),
         indic_sb == "V12150") %>%
  year_to_date %>%
  ggplot + geom_line(aes(x = date, y = values, color = Nace_r1)) +
  theme_minimal() +
  scale_x_date(breaks = as.Date(paste0(seq(1995, 2100, 2), "-01-01")),
               labels = date_format("%Y")) +
  theme(legend.position = "bottom",
        legend.title = element_blank()) +
  xlab("") + ylab("Value added at factor cost (Mio EUR)")

Latest Year by Country

Code
latest_t <- htec_eco_sbs %>%
  filter(nace_r1 == "HTC", indic_sb == "V12150", !is.na(values)) %>%
  summarise(m = max(time)) %>%
  pull(m)

htec_eco_sbs %>%
  filter(nace_r1 == "HTC",
         indic_sb %in% c("V12110", "V12150", "V15110"),
         time == latest_t) %>%
  select(Indic_sb, Geo, values) %>%
  spread(Geo, values) %>%
  print_table_conditional()
Indic_sb Austria Belgium Bulgaria Cyprus Czechia Denmark Estonia Finland France Germany Greece Hungary Ireland Italy Latvia Lithuania Luxembourg Netherlands Norway Poland Portugal Romania Slovakia Slovenia Spain Sweden United Kingdom
Gross investment in tangible goods - million euro 1915 1841 NA NA NA NA NA 1179 15444 16326 1280 1353 2426 9881 NA 227 NA NA 1517 NA NA 2738 718 NA 5891 NA NA
Turnover or gross premiums written - million euro 27337 48061 3364 NA NA NA NA 55055 265648 356362 13737 31161 67964 156725 NA 1945 NA NA 24247 31358 NA 10512 9602 NA 99249 NA 320246
Value added at factor cost - million euro 12007 20722 1495 NA NA NA NA 15661 106254 147214 6506 6647 17294 70659 NA 755 NA NA 10732 12437 NA 4504 2494 NA 41403 NA 156982

indic_sb

Code
htec_eco_sbs %>%
  
  group_by(indic_sb, Indic_sb) %>%
  summarise(Nobs = n()) %>%
  arrange(-Nobs) %>%
  print_table_conditional
indic_sb Indic_sb Nobs
V12110 Turnover or gross premiums written - million euro 2723
V12120 Production value - million euro 2694
V13110 Total purchases of goods and services - million euro 2646
V12150 Value added at factor cost - million euro 2641
V15110 Gross investment in tangible goods - million euro 2568
V12170 Gross operating surplus - million euro 2493
V15150 Gross investment in machinery and equipment - million euro 1406

nace_r1

Code
htec_eco_sbs %>%
  
  group_by(nace_r1, Nace_r1) %>%
  summarise(Nobs = n()) %>%
  arrange(-Nobs) %>%
  print_table_conditional
nace_r1 Nace_r1 Nobs
D Manufacturing 2219
D_LTC Low-technology manufacturing (DA to DE and DN) 2177
D_LTC_M Medium low-technology manufacturing (DF, DH to DJ and DM35.1) 2129
D_HTC High-technology manufacturing (DG24.4, DL30, DL32, DL33, DM35.3) 2096
HTC High-technology sectors (high-technology manufacturing and knowledge-intensive high-technology services) 1818
LKIS_MKT Less knowledge-intensive market services 1759
KIS_HTC Knowledge-intensive high-technology services (I64, K72 and K73) 1709
KIS_MKT_OTH Knowledge-intensive market services (except financial intermediation and high-technology services) 1703
D_HTC_M Medium high-technology manufacturing (DG (except DG24.4); DK, DL31, DM34, DM35.2, DM35.4, DM35.5) 1561

currency

Code
htec_eco_sbs %>%
  
  group_by(currency, Currency) %>%
  summarise(Nobs = n()) %>%
  arrange(-Nobs) %>%
  print_table_conditional
currency Currency Nobs
MIO_EUR Million euro 17171