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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
2023
Unemployment in average per month	857	712	18	98	12	18
Total population	37.072	20.027	2.208	8.263	1.409	5.165
Persons not in the labour force in average per month	8.023	5.922	575	1.042	176	308
Labour force in average per month	29.049	14.105	1.633	7.221	1.233	4.857
Employment in average per month	28.192	13.393	1.615	7.123	1.222	4.840

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

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
2025Q1
Total	4.616	100,0
1-3 months	2.597	56,3
4-6 months	1.112	24,1
7-9 months	475	10,3
10-12 months	432	9,4

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, 2023
All industries	29.339	25.874	3.465
Public administration and service	12.730	11.487	1.243
Social protection (COFOG 10)	5.089	4.651	437
Fishing and other related industries	4.517	3.281	1.237
Wholesale and retail trade	3.055	2.701	354
Family and children (COFOG 10.40)	2.519	2.290	229
Education (COFOG 9)	2.479	2.173	307
Construction	2.239	2.185	54
Transportation and storage	2.087	1.859	228
Primary education (COFOG 9.10)	1.559	1.262	297
Social protection (COFOG 10.x)	1.317	1.259	58
Health (COFOG 7)	1.302	1.222	80
General public services (COFOG 1)	1.270	1.135	135
Old age (COFOG 10.20)	1.253	1.102	150
Accommodation and food service activities	903	847	56
Unknown	822	748	74
Other (COFOG x)	750	701	49
Youth-level education (COFOG 9.20)	629	622	7
Economic affairs (COFOG 4)	619	501	118
Information and communication	555	550	5
Public order and safety (COFOG 3)	521	497	24
Administrative and support service activities	449	385	65
Energy and watersupply	424	327	97
Recreation, culture and religion (COFOG 8)	359	308	51
Other service industries	330	325	4
Real estate activities	325	320	5
Professional, scientific and technical activities	298	296	2
Education other (COFOG 9.x)	291	289	3
Environmental protection (COFOG 5)	264	226	39
Manufacturing	231	226	5
Financial and insurance activities	208	207	0
Mining and quarrying	85	79	6
Agriculture, forestry and related industries	83	53	31
Defence (COFOG 2)	48	45	3
Housing and community amenities (COFOG 6)	29	29	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")

	2019	2020	2021	2022	2023
Total	4,3	4,5	3,7	3,2	2,9
Primary	7,1	7,5	6,2	5,5	5,0
Upper secondary education - General	1,4	1,4	0,9	0,9	1,1
Upper secondary education - Vocational: Total	2,1	2,3	1,8	1,5	1,4
Upper secondary education - Vocational: Arts and humanities	2,6	3,4	2,8	2,0	0,7
Upper secondary education - Vocational: Business, administration and law	0,9	1,5	1,3	0,9	0,5
Upper secondary education - Vocational: Engineering, manufacturing and construction	1,8	1,9	1,6	1,6	1,4
Upper secondary education - Vocational: Agriculture, forestry, fisheries and veterinary	6,1	6,5	5,2	4,0	3,4
Upper secondary education - Vocational: Health and welfare	1,6	1,7	1,4	1,1	1,1
Upper secondary education - Vocational: Services	3,0	3,2	2,1	1,8	1,9
Upper secondary education - Vocational: Other	1,2	1,5	0,7	0,3	0,5
Post-secondary non-tertiary education	1,7	1,9	1,3	1,2	0,9
Bachelors, Masters, Doctoral or equivalent level	0,4	0,4	0,3	0,4	0,4
* Unemployment rate (pct.)

See the table in our Statbank: ARXLED7

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Last updated: 22. juni 2025