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Greenlanders in Denmark

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Greenlanders in Denmark

Tabel 1: Persons born in Greenland living in Denmark by municipality, affiliation and time

GDX1GDO_raw <-
  statgl_url("GDX1GDO", lang = language) %>%
  statgl_fetch(
    municipality = px_all(),
    affiliation  = px_all(),
    time         = px_top(1),
    .col_code    = TRUE
  ) %>% 
  as_tibble()

regions <- 
  c("Danmark","Byen København", "Københavns omegn", "Nordsjælland", 
    "Bornholm i alt", "Østsjælland", "Vest- og Sydsjælland",
    "Fyn", "Sydjylland", "Østjylland", "Vestjylland", "Nordjylland")

GDX1GDO <-
  GDX1GDO_raw %>% 
  filter(municipality %in% regions) %>% 
  mutate(
    affiliation = affiliation %>% fct_inorder(),
    municipality = municipality %>% fct_reorder(value, .fun = sum, .desc = TRUE)
    ) %>% 
  spread(affiliation, value) %>% 
  mutate_if(is.integer, ~ replace(., is.na(.), 0))


GDX1GDO %>% 
  select(-time) %>% 
  rename(" " = 1) %>% 
  statgl_table(replace_0s = TRUE) %>% 
  pack_rows(index = GDX1GDO[["time"]] %>% table()) %>% 
  row_spec(1, bold = TRUE) 

	Total	Both parents born in Greenland	One parent born in Greenland and one outside	One parent born in Greenland and one unknown	Both parents born outside Greenland	One parent born outside Greenland and one unknown	Both parents birthplace unknown
2023
Danmark	17.079	5.308	3.618	1.104	4.091	616	2.342
Nordjylland	2.929	1.352	548	218	377	61	373
Østjylland	2.667	792	606	149	732	81	307
Sydjylland	2.366	854	507	161	452	68	324
Byen København	2.206	552	455	159	633	110	297
Fyn	1.787	612	387	112	384	50	242
Vest- og Sydsjælland	1.456	278	331	91	396	78	282
Vestjylland	1.273	504	247	86	238	38	160
Nordsjælland	885	102	180	46	355	50	152
Københavns omegn	860	166	199	39	299	45	112
Østsjælland	486	62	120	25	186	28	65
Bornholm i alt	164	34	38	18	39	7	28

See the table in our Statbank: GDX1GDO

Tabel 2: Persons born in Greenland living in Denmark by gender, age, socioeconomic status, affiliation and time

GDXRA_raw <-
  statgl_url("GDXRA", lang = language) %>% 
  statgl_fetch(
    "socioeconomic status" = px_all(),
    gender                 = px_all(),
    time                   = px_top(1),
    .col_code              = TRUE
  ) %>% 
  as_tibble()

GDXRA <- 
  GDXRA_raw %>% 
  mutate(
    gender = gender %>% fct_inorder(),
    `socioeconomic status` = `socioeconomic status` %>% fct_inorder()
  ) %>% 
  spread(gender, value) %>% 
  mutate_if(is.integer, ~ replace(., is.na(.), 0))

GDXRA %>% 
  select(-time) %>% 
  rename(" " = 1) %>% 
  statgl_table(replace_0s = TRUE) %>% 
  pack_rows(index = GDXRA[["time"]] %>% table()) %>% 
  row_spec(1, bold = TRUE)

	Total	Men	Women
2021
Total	16.814	7.213	9.601
Self-employed	337	165	172
Assisting spouses	9	0	0
Employees, managers	212	135	77
Employees - upper level	1.869	699	1.170
Employees - medium level	640	290	350
Employees - basic level	2.952	1.395	1.557
Other employees	677	330	347
Employees, not specified	556	278	278
Unemployed	345	147	198
Subsidized employment without salary	113	58	55
Persons receiving holiday benefits	0	0	0
Guidance and activities upgrading skills	140	77	63
Unemployment benefit	70	18	52
Maternity absence from unemployment	18	0	0
Sickness absence from unemployment	120	46	74
Cash benefit (passive)/cash benefit for foreigners	1.853	844	1.009
Rehabilitation	0	0	0
Specially arranged scheme	245	95	150
Job clarification program	75	10	65
Disability pension	1.734	672	1.062
Early retirement pay	80	19	61
Flex benefit	9	0	0
Old-age pension	1.554	420	1.134
Other pensions	152	60	92
Enrolled in education	2.007	942	1.065
Children and youth (not enrolled in education)	254	124	130
Others outside the labour force	754	358	396
Unknown	35	22	13

See the table in our Statbank: GDXRA

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Last updated: 22. juni 2025