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Social Services

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Unemployment and maternity benefits

Tabel 1: Unemployment and maternity benefits by municipality, unit, type and time

SOX006_raw <- 
  statgl_url("SOX006", lang = language) |> 
  statgl_fetch(
    municipality = px_all(),
    unit         = px_all(),
    type         = px_all(),
    time         = px_top(),
    .col_code    = T
  ) |> 
  as_tibble()

SOX006 <- 
  SOX006_raw |> 
  mutate(municipality = municipality |> fct_inorder(),
         type = type |> fct_inorder()) |>
  spread(type, value) |> 
  arrange(unit) 

SOX006 |> 
  select(-c(2, 3)) |> 
  rename(" " = 1) |> 
  statgl_table() |> 
  pack_rows(index = table(SOX006[[3]])) |> 
  pack_rows(index = table(SOX006[2])) |> 
  row_spec(c(1, 7), bold = T)

	Unemployment benefits, total	Unemployment benefit, basic	Unemployment benefit, termination	Unemployment benefit, disease	Maternity benefits
2022
Amount (1000 DKK)
Total	28.314	9.252	3.462	15.600	35.119
Kommune Kujalleq	5.329	3.168	14	2.147	4.317
Kommuneqarfik Sermersooq	7.453	3.080	18	4.355	10.739
Qeqqata Kommunia	5.355	1.783	431	3.142	7.735
Kommune Qeqertalik	5.183	930	1.409	2.844	4.222
Avannaata Kommunia	4.994	291	1.590	3.112	8.107
Number
Total	3.056	757	396	2.197	846
Kommune Kujalleq	472	276	1	250	101
Kommuneqarfik Sermersooq	662	152	2	533	272
Qeqqata Kommunia	736	210	101	520	171
Kommune Qeqertalik	572	80	113	436	103
Avannaata Kommunia	614	39	179	458	199

See the table in our Statbank: SOX006

Foster care

Tabel 2: Foster children by municipality and year

SOXPV004_raw <- 
  statgl_url("SOXPV004", lang = language) %>%
  statgl_fetch(
    municipality = px_all(),
    year = px_top(1),
    .col_code = TRUE
  ) %>% 
  as_tibble()



SOXPV004_raw %>% 
  mutate(municipality = municipality %>% fct_inorder()) %>% 
  spread(year, value) %>% 
  rename(" " = 1) %>% 
  statgl_table() %>% 
  row_spec(1, bold = TRUE)

	2023
Total	409
Kommuneqarfik Sermersooq	151
Avannaata Kommunia	60
Kommune Qeqertalik	51
Qeqqata Kommunia	71
Kommune Kujalleq	76

See the table in our Statbank: SOXPV004

Public benefits

Tabel 3: Public benefits by municipality, unit, type and time

SOX004_raw <- 
  statgl_url("SOX004", lang = language) %>% 
  statgl_fetch(
    municipality = px_all(),
    unit         = px_all(),
    type         = 10,
    time         = px_top(),
    .col_code    = TRUE
  ) %>% 
  as_tibble()


SOX004 <- 
  SOX004_raw %>% 
  mutate(municipality = municipality %>% fct_inorder()) %>% 
  spread(unit, value) %>% 
  unite(combi, type, time, sep = ", ")


SOX004 %>% 
  select(-combi) %>% 
  rename(" " = 1) %>% 
  statgl_table() %>% 
  pack_rows(index = SOX004[["combi"]] %>% table()) %>% 
  row_spec(1, bold = TRUE)

	Amount (1000 DKK)	Number
Public benefits total, 2022
Total	53.040	3.323
Kommune Kujalleq	11.531	721
Kommuneqarfik Sermersooq	19.953	1.045
Qeqqata Kommunia	10.504	609
Kommune Qeqertalik	5.857	462
Avannaata Kommunia	5.196	486

See the table in our Statbank: SOX004

Pensions

Tabel 4: Pensioners in December by district, gender, payment and time

SOXPEN1_raw <- 
  statgl_url("SOXPEN1", lang = language) %>% 
  statgl_fetch(
    gender    = px_all(),
    payment   = px_all(),
    time      = px_top(),
    .col_code = TRUE
  ) %>% 
  as_tibble()


SOXPEN1 <- 
  SOXPEN1_raw %>% 
  mutate(
    gender = gender %>% fct_inorder(),
    payment = payment %>% fct_inorder()
  ) %>% 
  spread(gender, value)

SOXPEN1 %>% 
  select(-time) %>% 
  rename(" " = 1) %>% 
  statgl_table() %>% 
  pack_rows(index = SOXPEN1[["time"]] %>% table()) %>% 
  row_spec(1, bold = TRUE)

	Total	Man	Woman
2022
Total	6.322	3.278	3.044
Age pension	3.859	1.994	1.865
Early retirement pension	2.463	1.284	1.179

See the table in our Statbank: SOXPEN1

Social Care

Tabel 5: Disabled citizens

SOXFO10_raw <- 
  statgl_url("SOXFO10", lang = language) %>%
  statgl_fetch(
    "age group" = px_all(),
    gender      = px_all(),
    time        = px_top(1),
    .col_code   = TRUE
  ) %>% 
  as_tibble()



SOXFO10 <- 
  SOXFO10_raw %>% 
  filter(time == max(time)) %>% 
  mutate(
    `age group` = `age group` %>% fct_inorder(),
    gender = gender %>% fct_inorder()
  ) %>% 
  spread(gender, value)


SOXFO10 %>% 
  select(-time) %>% 
  rename(" " = 1) %>% 
  statgl_table() %>% 
  pack_rows(index = SOXFO10[["time"]] %>% table()) %>% 
  row_spec(1, bold = TRUE)

	Total	Man	Woman
2018
Total	1.131	706	425
0-17 years	199	138	61
18-30 years	292	189	103
31-40 years	175	107	68
41-50 years	143	86	57
51-60 years	238	137	101
61+ years	84	49	35

See the table in our Statbank: SOXFO10

Additional social subsidies

Tabel 6: Rent subsidy by district, unit and time

SOXBS02_raw <- 
  statgl_url("SOXBS02", lang = language) |> 
  statgl_fetch(
    district  = px_all(),
    unit      = "Antal",
    time      = px_top(7),
    .col_code = T
  ) |> 
  as_tibble()

SOXBS02 <- 
  SOXBS02_raw |> 
  spread(time, value)

SOXBS02 |> 
  select(-unit) |> 
  rename(" " = 1) |> 
  statgl_table() |> 
  pack_rows(index = table(SOXBS02["unit"])) |> 
  row_spec(1, bold = T)

	2016	2017	2018	2019	2020	2021	2022
Number
Ilulissat	447	419	374	361	359	317	291
Ittoqotoormiit	25	23	23	32	22	19	25
Kangaatsiaq	45	48	37	35	28	35	32
Maniitsoq	302	291	316	302	293	283	279
Nanortalik	185	151	144	135	105	111	115
Narsaq	215	198	179	172	172	154	162
Nuuk	1.134	1.068	1.015	1.015	1.023	1.000	980
Paamiut	231	191	193	186	183	176	158
Qaqortoq	393	356	306	302	279	281	309
Qasigiannguit	149	140	126	112	106	108	93
Qeqertarsuaq	74	75	72	62	60	68	62
Qaanaaq	64	49	54	51	47	43	43
Sisimiut	545	522	533	499	509	510	495
Tasiilaq	168	153	153	153	142	144	135
Total	4.421	4.115	3.941	3.822	3.729	3.653	3.527
Upernavik	43	39	45	38	26	36	29
Uummannaq	91	75	73	71	69	68	52
Aasiaat	310	317	298	296	306	300	267

See the table in our Statbank: SOXBS02

Economically disadvantaged

Tabel 7: Economically disadvantaged by inventory variable, age, gender and year

SODOU01_raw <- 
  statgl_url("SOXOU01", lang = language) %>% 
  statgl_fetch(
    "inventory variable" = c("Andel50", "Andel60"),
    gender = px_all(),
    year = px_top(1),
    .col_code = TRUE
  ) %>% 
  as_tibble()


SODOU01 <- 
  SODOU01_raw %>% 
  mutate(`inventory variable` = `inventory variable` %>% fct_inorder()) %>% 
  arrange(desc(gender)) %>% 
  mutate(gender = gender %>% fct_inorder() %>% fct_rev()) %>% 
  spread(gender, value)
  

SODOU01 %>% 
  select(-year) %>% 
  rename(" " = 1) %>% 
  statgl_table() %>% 
  pack_rows(index = SODOU01[["year"]] %>% table())

	Female	Male	Total
2022
fraction below 50%	4,3	5,0	4,7
fraction below 60%	8,4	9,2	8,8

See the table in our Statbank: SOXOU01

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Last updated: 17. april 2024