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Labour market

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Labour force

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

url <- paste0("https://bank.stat.gl/api/v1/", language, "/Greenland/AR/AR10/ARXSTK2.px")

ARXSTK2_raw <- 
  url |> 
  statgl_fetch(
    time                 = px_top(),
    education            = c("AA", "10", "20", "30", "40", "50"),
    "inventory variable" = px_all(),
    .col_code            = TRUE
  ) %>% 
  as_tibble()

ARXSTK2 <-
  ARXSTK2_raw %>% 
  mutate(
    education = education %>% factor(levels = unique(education)),
    `inventory variable` = `inventory variable` %>% fct_rev()
  ) %>% 
  spread(education, value)


ARXSTK2 %>% 
  select(-time) %>% 
  rename(" " = 1) %>% 
  statgl_table() %>% 
  pack_rows(index = ARXSTK2[["time"]] %>% 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
2022
Unemployment in average per month	931	776	14	108	15	17
Total population	37.038	20.503	2.036	8.068	1.449	4.982
Persons not in the labour force in average per month	8.231	6.232	481	1.017	187	314
Labour force in average per month	28.808	14.272	1.555	7.051	1.262	4.668
Employment in average per month	27.877	13.495	1.542	6.943	1.246	4.651

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
2023
18-19	68	60	68	53	49	53	42	32	29	34	47	60
20-24	183	134	172	145	118	124	117	96	87	105	133	141
25-29	209	168	159	139	117	111	116	100	85	100	124	141
30-34	242	200	214	185	151	146	139	121	109	122	141	173
35-39	194	168	154	136	124	119	115	103	101	102	121	148
40-44	163	156	138	115	120	103	109	96	97	100	120	128
45-49	128	109	120	97	88	75	70	68	63	74	84	97
50-54	174	147	156	134	111	112	111	97	85	94	102	113
55-59	240	193	203	203	207	176	156	155	152	149	193	224
60+	208	181	198	190	176	149	141	139	140	145	169	175

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(
    gender               = 0,
    age                  = "A",
    "inventory variable" = px_all(),
    time                 = px_top(1),
    "number of months"   = px_all(),
    .col_code = TRUE
  ) %>% 
  as_tibble()

ARXLEDVAR <- 
  ARXLEDVAR_raw %>% 
  unite(combi, age, gender, sep = ", ") %>% 
  mutate(
    `number of months` = `number of months` %>% fct_inorder(),
    `inventory variable` = `inventory variable` %>% fct_inorder()
  ) %>% 
  spread(`inventory variable`, value)

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

	Number of persons	Percentage
2023Q4
Total	4.479	100,0
1-3 months	2.948	65,8
4-6 months	844	18,8
7-9 months	345	7,7
10-12 months	342	7,6

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_fetch(
    time                 = px_top(),
    industry             = px_all(),
    gender               = "A",
    "inventory variable" = "G",
    "place of residence" = px_all(),
    .col_code            = TRUE
  ) %>% 
  as_tibble()

ARXBFB1 <- 
  ARXBFB1_raw %>% 
  arrange(-value) %>% 
  mutate(
    industry = industry %>% fct_inorder(),
    `place of residence` = `place of residence` %>% fct_inorder()
  ) %>% 
  spread(`place of residence`, value) %>% 
  unite(combi, `inventory variable`, time, sep = ", ")

ARXBFB1 %>% 
  select(-c(combi, gender)) %>% 
  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, 2022
All industries	28.992	25.493	3.499
Public administration and service	12.873	11.540	1.333
Fishing and other related industries	4.343	3.125	1.218
Wholesale and retail trade	3.075	2.702	373
Construction	2.308	2.258	50
Transportation and storage	2.043	1.807	236
Accommodation and food service activities	829	794	34
Unknown	594	558	36
Information and communication	563	555	8
Energy and watersupply	417	326	91
Administrative and support service activities	401	338	63
Other service industries	318	316	3
Real estate activities	298	292	5
Professional, scientific and technical activities	298	296	2
Manufacturing	228	225	2
Financial and insurance activities	201	201	NA
Mining and quarrying	106	98	8
Agriculture, forestry and related industries	98	62	36

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 <- 
  url |> 
  statgl_fetch(
    time      = px_top(5),
    education = px_all(),
    "inventory variable" = "P",
    .col_code = TRUE
  ) %>% 
  as_tibble()

ARXLED6_raw %>% 
  mutate(
    education = education %>% fct_inorder(),
    time = time %>% fct_inorder()
  ) %>% 
  spread(time, value) %>%
  select(-`inventory variable`) |> 
  rename(" " = 1) %>% 
  statgl_table() %>% 
  row_spec(1, bold = TRUE) |> 
  add_footnote(ARXLED6_raw[[3]][1], notation = "symbol")

	2018	2019	2020	2021	2022
Total	5,0	4,3	4,5	3,7	3,2
Primary	8,1	7,1	7,5	6,2	5,4
Upper secondary education - General	1,9	1,4	1,4	0,9	0,9
Upper secondary education - Vocational: Total	2,4	2,1	2,3	1,8	1,5
Upper secondary education - Vocational: Arts and humanities	4,2	2,6	3,4	2,8	2,0
Upper secondary education - Vocational: Business, administration and law	1,1	0,9	1,5	1,3	1,0
Upper secondary education - Vocational: Engineering, manufacturing and construction	2,1	1,8	1,9	1,6	1,6
Upper secondary education - Vocational: Agriculture, forestry, fisheries and veterinary	5,3	6,1	6,5	5,2	4,1
Upper secondary education - Vocational: Health and welfare	1,9	1,6	1,7	1,4	1,1
Upper secondary education - Vocational: Services	3,6	3,0	3,2	2,1	1,8
Upper secondary education - Vocational: Other	0,9	1,2	1,5	0,7	0,1
Post-secondary non-tertiary education	1,8	1,7	1,9	1,3	1,2
Bachelors, Masters, Doctoral or equivalent level	0,4	0,4	0,4	0,3	0,4
* Unemployment rate (pct.)

See the table in our Statbank: ARXLED7

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Last updated: 17. april 2024