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Labour market

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Labour force

Tabel 1: The labour force by education, time and inventory variable

ARXSTK2_raw <- 
  statgl_url("ARXSTK2", lang = language) |> 
  statgl_fetch(
    aar       = px_top(),
    udd_grp   = c("AA", "10", "20", "30", "40", "50"),
    opg_var   = px_all(),
    .col_code = TRUE
  ) %>% 
  as_tibble()

ARXSTK2 <-
  ARXSTK2_raw %>% 
  mutate(
    udd_grp = udd_grp %>% factor(levels = unique(udd_grp)),
    opg_var = opg_var %>% fct_rev()
  ) %>% 
  spread(udd_grp, value)


ARXSTK2 %>% 
  select(-aar) %>% 
  rename(" " = 1) %>% 
  statgl_table() %>% 
  pack_rows(index = ARXSTK2[["aar"]] %>% table()) |> 
  row_spec(1, bold = T)

	Total	Primary	Upper secondary education - General	Upper secondary education - Vocational: Total	Post-secondary non-tertiary education	Bachelors, Masters, Doctoral or equivalent level
2024
Unemployment in average per month	963	803	23	121	3	13
Total population	37.054	19.864	2.266	9.172	519	5.233
Persons not in the labour force in average per month	8.075	5.990	573	1.114	84	314
Labour force in average per month	28.979	13.874	1.692	8.058	435	4.919
Employment in average per month	28.015	13.071	1.669	7.937	432	4.906

See the table in our Statbank: ARXSTK2

Jobseekers

Tabel 2: Registered jobseekers by time, month, gender, place of residence and age

ARXLED2_raw <- 
  statgl_url("ARXLED2", lang = language) %>%
  statgl_fetch(
    aar       = px_top(2),
    md        = px_all(),
    koen      = 3,
    type_k    = "A",
    alderskat = px_all(),
    .col_code = TRUE
  ) %>% 
  as_tibble()

ARXLED2 <- 
  ARXLED2_raw %>% 
  filter(aar <= Sys.time() %>% year() - 1) %>% 
  mutate(
    alderskat = alderskat %>% factor(levels = unique(alderskat)),
    md = md %>% factor(levels = unique(md))
  ) %>% 
  spread(md, value) %>% 
  unite(combi, type_k, koen, sep = ", ")

ARXLED2 %>% 
  select(-c(aar, combi)) %>% 
  rename(" " = 1) %>% 
  statgl_table() %>% 
  pack_rows(index = ARXLED2[["aar"]] %>% table())

	January	February	March	April	May	June	July	August	September	October	November	December
2024
18-19	86	78	73	67	68	68	69	54	43	60	70	96
20-24	200	193	184	200	173	150	137	126	115	142	178	207
25-29	200	181	182	185	169	157	158	145	126	146	179	220
30-34	219	189	184	203	187	170	157	160	126	153	176	217
35-39	193	160	171	175	147	132	115	131	109	131	147	187
40-44	173	148	139	147	146	121	114	110	114	137	153	160
45-49	123	104	95	115	101	95	84	85	77	96	107	120
50-54	160	113	123	131	116	99	98	92	90	107	125	147
55-59	261	209	189	214	192	164	154	134	138	166	210	240
60+	208	147	150	166	176	158	144	141	133	175	209	219
2025
18-19	130	109	108	105	102	88	89	58	48	51	65	77
20-24	263	246	231	230	220	208	188	139	107	122	165	209
25-29	260	238	234	230	219	189	168	133	117	134	170	190
30-34	256	240	235	221	198	176	167	152	130	159	182	192
35-39	224	208	223	218	181	164	138	117	131	137	164	185
40-44	207	189	193	188	163	135	133	105	98	127	143	158
45-49	147	129	123	135	127	129	122	104	107	118	130	142
50-54	166	144	131	146	129	121	103	88	83	86	104	114
55-59	264	226	237	253	238	222	200	168	176	176	187	193
60+	242	236	235	236	204	199	199	182	189	213	216	240

See the table in our Statbank: ARXLED2

Tabel 3: Number of month as a registered jobseeker by residence, gender, age, time, number of months and inventory variable

ARXLEDVAR_raw <- 
  statgl_url("ARXLEDVAR", lang = language) %>% 
  statgl_fetch(
    koen      = 0,
    Alderskat = "A",
    opg_var   = px_all(),
    taar_kvar = px_top(1),
    kvar_led  = px_all(),
    .col_code = TRUE
  ) %>% 
  as_tibble()

ARXLEDVAR <- 
  ARXLEDVAR_raw %>% 
  unite(combi, Alderskat, koen, sep = ", ") %>% 
  mutate(
    kvar_led = kvar_led %>% fct_inorder(),
    opg_var = opg_var %>% fct_inorder()
  ) %>% 
  spread(opg_var, value)

ARXLEDVAR %>% 
  select(-c(combi, taar_kvar)) %>% 
  rename(" " = 1) %>% 
  statgl_table() %>% 
  pack_rows(index = ARXLEDVAR[["taar_kvar"]] %>% table()) %>% 
  row_spec(1, bold = TRUE)

	Number of persons	Percentage
2025Q4
Total	4.525	100,0
1-3 months	2.352	52,0
4-6 months	1.035	22,9
7-9 months	606	13,4
10-12 months	532	11,8

See the table in our Statbank: ARXSTK1

Employment

Tabel 4: Main employment for permanent residents by industry, place of residence, place of birt, gender, age, time and inventory variable

url <- paste0("https://bank.stat.gl/api/v1/", language, "/Greenland/AR/AR30/ARXBFB01.px")

ARXBFB1_raw <- 
  #url |> 
  statgl_url("ARXBFB01", lang = language) |> 
  statgl_fetch(
    aar       = px_top(),
    beskbrch  = px_all(),
    sex       = "A",
    opg_var   = "G",
    bybygd    = px_all(),
    .col_code = TRUE
  ) %>% 
  as_tibble()

ARXBFB1 <- 
  ARXBFB1_raw %>% 
  arrange(-value) %>% 
  mutate(
    beskbrch = beskbrch %>% fct_inorder(),
    bybygd = bybygd %>% fct_inorder()
  ) %>% 
  spread(bybygd, value) %>% 
  unite(combi, opg_var, aar, sep = ", ")

ARXBFB1 %>% 
  select(-c(combi, sex)) %>% 
  rename(" " = 1) %>% 
  statgl_table() %>% 
  pack_rows(index = ARXBFB1[["combi"]] %>% table()) %>% 
  row_spec(1, bold = TRUE) 

	Total	Towns	Settlements etc.
Number of main employed persons in average per month, 2024
All industries	29.232	25.902	3.331
Public administration and service	12.836	11.603	1.233
Social protection (COFOG 10)	5.073	4.667	406
Fishing and other related industries	4.326	3.169	1.157
Wholesale and retail trade	3.114	2.743	371
Education (COFOG 9)	2.500	2.196	303
Family and children (COFOG 10.40)	2.466	2.246	220
Construction	2.185	2.131	55
Transportation and storage	2.068	1.892	176
Primary education (COFOG 9.10)	1.573	1.275	298
Social protection other (COFOG 10.x)	1.352	1.303	49
Health (COFOG 7)	1.304	1.226	78
Old age (COFOG 10.20)	1.256	1.118	138
General public services (COFOG 1)	1.234	1.110	125
Accommodation and food service activities	922	847	75
Other (COFOG x)	854	783	71
Unknown	787	746	41
Youth-level education (COFOG 9.20)	630	626	4
Economic affairs (COFOG 4)	616	500	116
Information and communication	532	527	6
Public order and safety (COFOG 3)	529	505	24
Administrative and support service activities	469	403	66
Energy and watersupply	431	335	96
Recreation, culture and religion (COFOG 8)	364	318	46
Other service industries	315	314	1
Financial and insurance activities	305	299	6
Education other (COFOG 9.x)	297	296	2
Professional, scientific and technical activities	282	281	1
Environmental protection (COFOG 5)	269	221	49
Real estate activities	256	253	2
Manufacturing	227	221	6
Mining and quarrying	93	84	9
Agriculture, forestry and related industries	85	54	31
Defence (COFOG 2)	63	47	15
Housing and community amenities (COFOG 6)	30	30	NA

See the table in our Statbank: ARXBFB01

Unemployment

Tabel 5: Unemployment among permanent residents by education, time and inventory variable

url <- paste0("https://bank.stat.gl/api/v1/", language, "/Greenland/AR/AR40/ARXLED6.px")

ARXLED6_raw <- 
  statgl_url("ARXLED6", lang = language) |> 
  statgl_fetch(
    aar       = px_top(5),
    udd_grp   = px_all(),
    opg_var   = "P",
    .col_code = TRUE
  ) %>% 
  as_tibble()

ARXLED6_raw %>% 
  mutate(
    udd_grp = udd_grp %>% fct_inorder(),
    aar = aar %>% fct_inorder()
  ) %>% 
  spread(aar, value) %>%
  select(-opg_var) |> 
  rename(" " = 1) %>% 
  statgl_table() %>% 
  row_spec(1, bold = TRUE) |> 
  add_footnote(ARXLED6_raw[[3]][1], notation = "symbol")

	2020	2021	2022	2023	2024
Total	4,5	3,7	3,2	3,0	3,3
Primary	7,5	6,2	5,5	5,1	5,8
Upper secondary education - General	1,4	0,9	0,9	1,1	1,4
Upper secondary education - Vocational: Total	2,3	1,8	1,5	1,4	1,5
Upper secondary education - Vocational: Arts and humanities	3,6	2,9	2,0	0,7	1,8
Upper secondary education - Vocational: Business, administration and law	1,9	1,5	1,3	0,9	1,1
Upper secondary education - Vocational: Engineering, manufacturing and construction	1,9	1,6	1,6	1,4	1,3
Upper secondary education - Vocational: Agriculture, forestry, fisheries and veterinary	6,5	5,2	4,0	3,4	3,6
Upper secondary education - Vocational: Health and welfare	1,7	1,4	1,1	1,0	1,2
Upper secondary education - Vocational: Services	3,2	2,1	1,8	1,9	2,3
Upper secondary education - Vocational: Other	1,6	0,7	0,3	0,5	0,8
Post-secondary non-tertiary education	0,9	0,6	0,4	0,2	0,7
Bachelors, Masters, Doctoral or equivalent level	0,4	0,3	0,4	0,4	0,3
* Unemployment rate (pct.)

See the table in our Statbank: ARXLED7

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Last updated: 15. januar 2026