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National accounts

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National accounts

Tabel 1: Account 0: Goods and services by units, account name and time

NRX01_raw <- 
  statgl_url("NRX01", lang = language) %>%
  statgl_fetch(
    units          = "L",
    time           = px_top(5),
    "account name" = px_all(),
    .col_code      = TRUE
  ) %>% 
  as_tibble()
  
NRX01 <- 
  NRX01_raw %>% 
  arrange(desc(time)) %>% 
  mutate(
    time = time %>% fct_inorder(),
    `account name` = `account name` %>% str_remove_all("[:digit:]|[:punct:]|\\+") %>% trimws(),
    `account name` = `account name` %>% fct_inorder()
    ) %>% 
  drop_na() %>% 
  spread(time, value)
  
NRX01 %>% 
  select(-units) %>% 
  rename(" " = 1) %>% 
  statgl_table() %>% 
  pack_rows(index = NRX01[["units"]] %>% table()) %>% 
  row_spec(c(8, 19), bold = TRUE)

	2023	2022	2021	2020	2019
Current prices, million dkk
Output	38.457	37.069	34.183	32.824	32.633
Taxes less subsidies on products	919	875	754	748	769
Taxes on products	1.204	1.158	1.024	998	1.010
Subsidies on products	285	283	270	250	240
Imports of goods and services	11.710	11.728	10.277	9.438	10.225
Imports of goods	6.620	7.295	6.571	6.268	6.870
Imports of services	5.089	4.432	3.706	3.170	3.355
Total Resources	51.085	49.672	45.214	43.010	43.627
Intermediate consumption	16.457	15.638	14.230	13.359	13.412
Final consumption expenditure	17.028	16.494	16.248	15.708	15.760
Private consumption expenditure	7.469	7.286	7.083	6.892	7.005
Off Government consumption expenditure	9.560	9.209	9.164	8.816	8.754
Individual consumption expenditure	5.720	5.509	5.294	5.100	5.158
Collective consumption expenditure	3.840	3.699	3.871	3.716	3.596
Gross fixed capital formation	8.250	8.441	7.684	6.673	6.251
Exports of goods and services	9.351	9.099	7.052	7.270	8.205
Exports of goods	6.778	6.775	5.372	5.716	6.008
Exports of services	2.573	2.324	1.680	1.554	2.197
Total use	51.085	49.672	45.214	43.010	43.627

See the table in our Statbank: NRX01

Tabel 2: Production by units, industry and time

NRX0318_raw <- 
  statgl_url("NRX0318", lang = language) %>% 
  statgl_fetch(
    units     = "L",
    industry  = paste0("0100", LETTERS[1:11]),
    time      = px_top(5),
    .col_code = TRUE
  ) %>% 
  as_tibble()


NRX0318 <- 
  NRX0318_raw %>% 
  arrange(desc(time)) %>% 
  mutate(
    time = time %>% fct_inorder(),
    industry = industry %>% fct_reorder(value, .fun = sum, .desc = TRUE)
    ) %>% 
  spread(time, value) %>% 
  mutate(industry = industry %>% str_remove("A|B|C|D|E|F|G|H|I|J|K"))



NRX0318 %>% 
  select(-units) %>% 
  rename(" " = 1) %>%  
  statgl_table() %>% 
  pack_rows(index = NRX0318[["units"]] %>% table())

	2023	2022	2021	2020	2019
Current prices, million dkk
Public adm. and service	10.137	9.755	9.660	9.311	9.277
Fishery and hunting	5.721	5.374	4.743	4.797	5.039
Construction business	5.308	4.971	5.134	4.526	3.651
Finance and business	4.162	4.004	3.550	3.279	3.254
Transport	4.133	3.849	3.327	3.004	3.335
Trade	3.090	3.094	2.743	2.759	2.776
Industry	2.280	2.435	1.889	2.018	2.165
Post, tele, IT, radio, TV etc.	1.297	1.273	1.244	1.219	1.262
Supply and renovation	994	947	963	956	903
Hotels and restaurants	1.138	1.064	818	650	729
Extraction of raw materials	197	302	112	306	242

See the table in our Statbank: NRX0318

Tabel 3: Trends in GDP by units, account and Aar

NRX10_raw <- 
  statgl_url("NRX10", lang = language) %>% 
  statgl_fetch(
    units = "K",
    account = c("01", "02", "04"),
    time = px_top(5),
    .col_code = TRUE
  ) %>% 
  as_tibble()

NRX10 <- 
  NRX10_raw %>% 
  arrange(desc(time)) %>% 
  mutate(time = time %>% fct_inorder()) %>% 
  spread(time, value)

NRX10 %>% 
  select(-units) %>% 
  rename(" " = 1) %>% 
  statgl_table() %>% 
  pack_rows(index = NRX10[["units"]] %>% table())

	2023	2022	2021	2020	2019
2010-prices, chained values
Annual real growth in percent	0,9	2	1,6	0,3	2,8
GDP, million DKK	16.260,4	16.121	15.804,0	15.549,9	15.511,0
Per capita, 1.000 DKK	287,5	285	280,4	277,6	277,4

See the table in our Statbank: NRX10

Tabel 4: Growth contribution to GDP by Kode and Aar

NRX13_raw <- 
  statgl_url("NRX13", lang = language) %>% 
  statgl_fetch(
    time      = px_top(5),
    Kode      = paste0("VBVT", LETTERS[1:11]),
    .col_code = TRUE
  ) %>% 
  as_tibble()


NRX13 <-
  NRX13_raw %>% 
  arrange(desc(time)) %>% 
  mutate(
    time = time %>% fct_inorder(),
    Kode = Kode %>% str_remove("A|B|C|D|E|F|G|H|I|J|K") %>% trimws(),
    Kode = Kode %>% fct_reorder(value, .fun = mean, .desc = TRUE)
  ) %>% 
  spread(time, value)

NRX13 %>% 
  rename(" " = 1) %>% 
  statgl_table()

	2023	2022	2021	2020	2019
Fishery and hunting	0,18	0,36	1,55	-0,93	0,98
Trade	-0,31	0,72	-0,23	0,75	0,77
Construction business	0,82	-1,83	-0,48	1,97	1,13
Transport	0,80	-0,48	1,12	-0,48	0,09
Finance and business	0,65	0,56	0,56	-1,03	0,02
Public adm. and service	0,23	-0,28	-0,11	-0,68	1,38
Supply and renovation	-0,17	0,02	0,02	0,64	-0,02
Extraction of raw materials	-0,29	0,50	-0,57	0,25	0,27
Hotels and restaurants	-0,04	0,72	0,43	-0,73	-0,27
Post, tele, IT, radio, TV etc.	-0,02	0,16	-0,12	0,21	-0,30
Industry	-0,61	1,62	-1,39	0,73	-0,57

See the table in our Statbank: NRX13

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Last updated: 22. juni 2025