From 992dca410196a523078fa6f30eaf7f2b5705eeb3 Mon Sep 17 00:00:00 2001 From: IGRiosTC <32395357+IGRiosTC@users.noreply.github.com> Date: Wed, 6 Mar 2024 18:45:36 +0000 Subject: [PATCH] Update UC and CC to Jan 2024 --- .../claimant_count_and_universal_credit.Rmd | 50 +- .../claimant_count_and_universal_credit.html | 700 +++++++++--------- 2 files changed, 388 insertions(+), 362 deletions(-) diff --git a/unemployment_covid-19/claimant_count_and_universal_credit.Rmd b/unemployment_covid-19/claimant_count_and_universal_credit.Rmd index 1a82785..ed7b977 100644 --- a/unemployment_covid-19/claimant_count_and_universal_credit.Rmd +++ b/unemployment_covid-19/claimant_count_and_universal_credit.Rmd @@ -23,7 +23,7 @@ basech <- "January 2020" # URL: https://www.nomisweb.co.uk/sources/cc # Licence: Open Government Licence -cc_trafford <- read_csv("https://www.nomisweb.co.uk/api/v01/dataset/NM_162_1.data.csv?geography=1811939363&date=latestMINUS47-latest&gender=0&age=0&measure=1,2&measures=20100") %>% +cc_trafford <- read_csv("https://www.nomisweb.co.uk/api/v01/dataset/NM_162_1.data.csv?geography=1811939363&date=latestMINUS48-latest&gender=0&age=0&measure=1,2&measures=20100") %>% select(period = DATE_NAME, area_code = GEOGRAPHY_CODE, area_name = GEOGRAPHY_NAME, @@ -33,8 +33,8 @@ cc_trafford <- read_csv("https://www.nomisweb.co.uk/api/v01/dataset/NM_162_1.dat latest <- as.character(max(as.yearmon(cc_trafford$period))) latestm12 <- as.character(max(as.yearmon(cc_trafford$period))-1) -latestch <- "December 2023" -latestm12ch <- "December 2022" +latestch <- "January 2024" +latestm12ch <- "January 2023" cc_traf <- cc_trafford %>% mutate(period=as.yearmon(period, "%b %Y"), @@ -50,7 +50,7 @@ cc_traf <- cc_trafford %>% # Claimant Count in Trafford -The [Claimant Count](https://www.nomisweb.co.uk/sources/cc) indicates the number of people claiming benefits principally for the reason of being unemployed. The Claimant Count in `r latestch` was `r filter(cc_traf,period==as.yearmon(latest, "%b %Y"))$change`% higher than in `r basech`. The rate of claimants to residents aged 16 to 64 reached `r filter(cc_traf,period==as.yearmon(latest, "%b %Y"))$rate`% in `r latestch` representing an increment of `r filter(cc_traf,period==as.yearmon(latest, "%b %Y"))$rate-cc_traf$rate[1]`% from `r basech`. The number of claimants has been decreasing from April 2021 but started to increase again from January to April 2023 and at similar level since May 2023. +The [Claimant Count](https://www.nomisweb.co.uk/sources/cc) indicates the number of people claiming benefits principally for the reason of being unemployed. The Claimant Count in `r latestch` was `r filter(cc_traf,period==as.yearmon(latest, "%b %Y"))$change`% higher than in `r basech`. The rate of claimants to residents aged 16 to 64 was `r filter(cc_traf,period==as.yearmon(latest, "%b %Y"))$rate`% in `r latestch` representing an increment of `r filter(cc_traf,period==as.yearmon(latest, "%b %Y"))$rate-cc_traf$rate[1]`% points from `r basech`. The number of claimants has been decreasing from April 2021 but started to increase again from January to April 2023 and at similar level since May 2023.
@@ -180,7 +180,7 @@ c_rate <- c_count %>% ``` -The Trafford MSOA with the largest share of claims in `r latestch` was `r filter(c_count, Share == max(Share)) %>% pull(area_name)` with `r filter(c_count, Share == max(Share))$Share`% of all claimants within Trafford, almost the double of claims than Gorse Hill, the MSOA with the second largest number of claims. 13 MSOAs out of 28 have more claims in `r latestch` compared to `r latestm12ch`. `r arrange(c_count, desc(additional_claims))$area_name[1]` have the largest increase in number of claims with `r abs(arrange(c_count, desc(additional_claims))$additional_claims[1])` more, followed by `r arrange(c_count, desc(additional_claims))$area_name[2]` (`r arrange(c_count, desc(additional_claims))$additional_claims[2]`) and `r arrange(c_count, desc(additional_claims))$area_name[3]` (`r arrange(c_count, desc(additional_claims))$additional_claims[3]`). `r arrange(c_count, additional_claims)$area_name[1]` has the largest reduction in number of claims with `r abs(arrange(c_count, additional_claims)$additional_claims[1])` less, followed by `r arrange(c_count, additional_claims)$area_name[2]` (`r arrange(c_count, additional_claims)$additional_claims[2]`). +The Trafford MSOA with the largest share of claims in `r latestch` was `r filter(c_count, Share == max(Share)) %>% pull(area_name)` with `r filter(c_count, Share == max(Share))$Share`% of all claimants within Trafford, almost the double of claims than Gorse Hill, the MSOA with the second largest number of claims. 12 MSOAs out of 28 have more claims in `r latestch` compared to `r latestm12ch`. `r arrange(c_count, desc(additional_claims))$area_name[1]` have the largest increase in number of claims with `r abs(arrange(c_count, desc(additional_claims))$additional_claims[1])` more, followed by `r arrange(c_count, desc(additional_claims))$area_name[2]` (`r arrange(c_count, desc(additional_claims))$additional_claims[2]`) and `r arrange(c_count, desc(additional_claims))$area_name[3]` (`r arrange(c_count, desc(additional_claims))$additional_claims[3]`). `r arrange(c_count, additional_claims)$area_name[1]` has the largest reduction in number of claims with `r abs(arrange(c_count, additional_claims)$additional_claims[1])` less, followed by `r arrange(c_count, additional_claims)$area_name[2]` (`r arrange(c_count, additional_claims)$additional_claims[2]`) and `r arrange(c_count, additional_claims)$area_name[3]` (`r arrange(c_count, additional_claims)$additional_claims[3]`). ```{r CCMSOATable} @@ -225,7 +225,7 @@ tbl2 <- reactable( cell_function(value,table2$additional_claims) } ), - Share = colDef(name = "Share Dec 23", maxWidth = 80, align = "left"), + Share = colDef(name = "Share Jan 24", maxWidth = 80, align = "left"), percentage_change = colDef(name = "% Change", maxWidth = 80, align = "right"), latestm12_r = colDef(name = latestm12, maxWidth = 60, align = "left"), latest_r = colDef(name = latest, maxWidth = 60, align = "left"), @@ -251,7 +251,7 @@ tbl2 ``` -The MSOAs with higher claimant rate in `r latestch` were `r arrange(table2, desc(latest_r))$area_name[1]` with `r arrange(table2, desc(latest_r))$latest_r[1]`%, `r arrange(table2, desc(latest_r))$area_name[2]` with `r arrange(table2, desc(latest_r))$latest_r[2]`%, `r arrange(table2, desc(latest_r))$area_name[3]` with `r arrange(table2, desc(latest_r))$latest_r[3]`% and `r arrange(table2, desc(latest_r))$area_name[4]` with `r arrange(table2, desc(latest_r))$latest_r[4]`%. From the top 5 MSOAs with the highest rates, only Partington had a lower rate when comparing `r latestm12ch` to `r latestch`. The MSOAs where the claimant rate have decreased more from `r latestm12ch` to `r latestch` were `r arrange(table2, Change)$area_name[1]` and `r arrange(table2, Change)$area_name[2]` with a decrease in rate of `r arrange(table2, Change)$Change[1]`%. +The MSOAs with higher claimant rate in `r latestch` were `r arrange(table2, desc(latest_r))$area_name[1]` with `r arrange(table2, desc(latest_r))$latest_r[1]`%, `r arrange(table2, desc(latest_r))$area_name[2]` with `r arrange(table2, desc(latest_r))$latest_r[2]`%, `r arrange(table2, desc(latest_r))$area_name[3]` with `r arrange(table2, desc(latest_r))$latest_r[3]`% and `r arrange(table2, desc(latest_r))$area_name[4]` with `r arrange(table2, desc(latest_r))$latest_r[4]`%. From the top 5 MSOAs with the highest rates, only Partington had a lower rate when comparing `r latestm12ch` to `r latestch`. The MSOAs where the claimant rate have decreased more from `r latestm12ch` to `r latestch` was `r arrange(table2, Change)$area_name[1]` with `r arrange(table2, Change)$Change[1]`% points decrease.
@@ -348,7 +348,7 @@ ggplot() + # URL: https://www.nomisweb.co.uk/sources/cc # Licence: Open Government Licence -raw_cc_age <- read_csv("https://www.nomisweb.co.uk/api/v01/dataset/NM_162_1.data.csv?geography=1811939363&date=latestMINUS47-latest&gender=0&age=0,2,10,11,3,12...16,4,17...20&measure=1&measures=20100") +raw_cc_age <- read_csv("https://www.nomisweb.co.uk/api/v01/dataset/NM_162_1.data.csv?geography=1811939363&date=latestMINUS48-latest&gender=0&age=0,2,10,11,3,12...16,4,17...20&measure=1&measures=20100") cc_age <- raw_cc_age %>% @@ -381,7 +381,7 @@ table4 <- age_bands %>% select(Age, `latestm12`= latestm12, `latest` = latest, a ``` -In Trafford from `r latestm12ch` to `r latestch` 5 age bands has increased the number of claimants, the age band 18 to 24 had the most increase with 50 more. When considering the reduction of claims the age band `r filter(table3, additional_claims == min(additional_claims)) %>% pull(Age)` years had the largest reduction with `r filter(table3, additional_claims == min(additional_claims)) %>% pull(additional_claims)` less claims. +In Trafford from `r latestm12ch` to `r latestch` only 3 of the age bands has decreased the number of claimants. The age band 18 to 24 and 40 to 44 had the most increase with 45 more each. When considering the reduction of claims the age band `r filter(table3, additional_claims == min(additional_claims)) %>% pull(Age)` years had the largest reduction with `r filter(table3, additional_claims == min(additional_claims)) %>% pull(additional_claims)` less claims.
@@ -582,7 +582,7 @@ plotdf2 <- quinary %>%
-More than half of Trafford's claimants are residents of 8 of the 28 MSOAs. Around half of the claimants are between 25 and 44 years. +More than half of Trafford's claimants are residents of 1 quarter of the MSOAs. Around half of the claimants are between 25 and 44 years.
@@ -655,13 +655,17 @@ cc_msoa_agep<- cc_msoa_age %>% stats <- cc_msoa_agep %>% summarise(totalm =sum(n)) %>% ungroup() %>% - mutate(perM = totalm*100/sum(totalm)) + mutate(perM = totalm*100/sum(totalm)) %>% + arrange(desc(perM)) %>% + mutate(incre = cumsum(perM)) stats2 <- cc_msoa_agep %>% ungroup() %>% group_by(period, ageband) %>% summarise(totalm =sum(n)) %>% - mutate(perM = totalm*100/sum(totalm)) + mutate(perM = totalm*100/sum(totalm)) %>% + arrange(desc(ageband)) %>% + mutate(incre = cumsum(perM)) ggplot(cc_msoa_agep, aes(x = area_name, y = percent, width = total, fill = ageband)) + geom_col(position = "stack", colour = NA) + @@ -724,7 +728,7 @@ query <- list(database = unbox("str:database:UC_Monthly"), `str:field:UC_Monthly:V_F_UC_CASELOAD_FULL:COA_CODE` = list( map = list("str:value:UC_Monthly:V_F_UC_CASELOAD_FULL:COA_CODE:V_C_MASTERGEOG11_LA_TO_REGION:E08000009")), `str:field:UC_Monthly:F_UC_DATE:DATE_NAME` = list( - map = as.list(paste0("str:value:UC_Monthly:F_UC_DATE:DATE_NAME:C_UC_DATE:",c(202001,202002,202003,202004,202005,202006,202007,202008,202009,202010,202011,202012,202101,202102,202103,202104,202105,202106,202107,202108,202109,202110,202111,202112,202201,202202,202203,202204,202205,202206,202207,202208,202209,202210,202211,202212,202301,202302,202303,202304,202305,202306,202307,202308,202309,202310,202311,202312)))) + map = as.list(paste0("str:value:UC_Monthly:F_UC_DATE:DATE_NAME:C_UC_DATE:",c(202001,202002,202003,202004,202005,202006,202007,202008,202009,202010,202011,202012,202101,202102,202103,202104,202105,202106,202107,202108,202109,202110,202111,202112,202201,202202,202203,202204,202205,202206,202207,202208,202209,202210,202211,202212,202301,202302,202303,202304,202305,202306,202307,202308,202309,202310,202311,202312,202401)))) )) %>% toJSON() request <- POST( url = path, @@ -847,7 +851,7 @@ query <- list(database = unbox("str:database:UC_Monthly"), `str:field:UC_Monthly:V_F_UC_CASELOAD_FULL:COA_CODE` = list( map = as.list(paste0("str:value:UC_Monthly:V_F_UC_CASELOAD_FULL:COA_CODE:V_C_MASTERGEOG11_MSOA_TO_LA:E0", seq(2001259, 2001286, 1)))), `str:field:UC_Monthly:F_UC_DATE:DATE_NAME` = list( - map = as.list(paste0("str:value:UC_Monthly:F_UC_DATE:DATE_NAME:C_UC_DATE:",c(202212,202312)))) + map = as.list(paste0("str:value:UC_Monthly:F_UC_DATE:DATE_NAME:C_UC_DATE:",c(202301,202401)))) )) %>% toJSON() request <- POST( url = path, @@ -879,13 +883,15 @@ table7 <- uc %>% ``` -The Trafford MSOA with the largest share of claims in `r latestch` was `r filter(table7, Share == max(Share)) %>% pull(area_name)` with `r filter(table7, Share == max(Share))$Share`% of all Universal Credit claims in Trafford. 27 out of the 28 MSOAs have more claims in `r latestch` compared to `r latestm12ch`. The MSOAs with more additional claims when comparing `r latestm12ch` to `r latestch` were `r arrange(table7,desc(additional_claims))$area_name[1]` (`r arrange(table7,desc(additional_claims))$additional_claims[1]`), `r arrange(table7,desc(additional_claims))$area_name[2]` (`r arrange(table7, desc(additional_claims))$additional_claims[2]`) and `r arrange(table7, desc(additional_claims))$area_name[3]` (`r arrange(table7,desc(additional_claims))$additional_claims[3]`). The MSOAs with more increase in percentage change from `r latestm12ch` to `r latestch` were `r arrange(table7, desc(percentage_change))$area_name[1]` with `r arrange(table7, desc(percentage_change))$percentage_change[1]`% change and `r arrange(table7, desc(percentage_change))$area_name[2]` with `r arrange(table7, desc(percentage_change))$percentage_change[2]`% change. The MSOA with reductions in claims when comparing `r latestm12ch` to `r latestch` is `r arrange(table7,additional_claims)$area_name[1]` (`r arrange(table7,additional_claims)$additional_claims[1]`). The MSOA with reduction in percentage change from `r latestm12ch` to `r latestch` was `r arrange(table7, percentage_change)$area_name[1]` with `r arrange(table7, percentage_change)$percentage_change[1]`% change. +The Trafford MSOA with the largest share of claims in `r latestch` was `r filter(table7, Share == max(Share)) %>% pull(area_name)` with `r filter(table7, Share == max(Share))$Share`% of all Universal Credit claims in Trafford. 27 out of the 28 MSOAs have more claims in `r latestch` compared to `r latestm12ch`. The MSOAs with more additional claims when comparing `r latestm12ch` to `r latestch` were `r arrange(table7,desc(additional_claims))$area_name[1]` (`r arrange(table7,desc(additional_claims))$additional_claims[1]`), `r arrange(table7,desc(additional_claims))$area_name[2]` (`r arrange(table7, desc(additional_claims))$additional_claims[2]`) and `r arrange(table7, desc(additional_claims))$area_name[3]` (`r arrange(table7,desc(additional_claims))$additional_claims[3]`). The MSOAs with more increase in percentage change from `r latestm12ch` to `r latestch` were `r arrange(table7, desc(percentage_change))$area_name[1]` with `r arrange(table7, desc(percentage_change))$percentage_change[1]`% change and `r arrange(table7, desc(percentage_change))$area_name[2]` with `r arrange(table7, desc(percentage_change))$percentage_change[2]`% change. ```{r UCMSOATable} #The MSOAs that increased the number of claims from `r latestm12ch` to `r latestch` were `r arrange(table7, desc(additional_claims))$area_name[1]` with `r arrange(table7, desc(additional_claims))$additional_claims[1]` and `r arrange(table7, desc(additional_claims))$area_name[2]` with `r arrange(table7, desc(additional_claims))$additional_claims[2]`. +#The MSOA with reductions in claims when comparing `r latestm12ch` to `r latestch` is `r arrange(table7,additional_claims)$area_name[1]` (`r arrange(table7,additional_claims)$additional_claims[1]`). The MSOA with reduction in percentage change from `r latestm12ch` to `r latestch` was `r arrange(table7, percentage_change)$area_name[1]` with `r arrange(table7, percentage_change)$percentage_change[1]`% change. + tbl7 <- reactable( table7, pagination = FALSE, @@ -903,7 +909,7 @@ tbl7 <- reactable( } ), percentage_change = colDef(name = "% Change", maxWidth = 80), - Share = colDef(name = "Share Dec 23", maxWidth = 80, align = "left"), + Share = colDef(name = "Share Jan 24", maxWidth = 80, align = "left"), latestm12_rate = colDef(name = latestm12, maxWidth = 60, align = "left"), latest_rate = colDef(name = latest, maxWidth = 60, align = "left"), `Change` = colDef( @@ -928,13 +934,15 @@ tbl7 ``` -The MSOAs with higher rates of Universal Credit claims in `r latestch` were `r filter(table7, latest_rate == max(latest_rate)) %>% pull(area_name)` with `r filter(table7, latest_rate == max(latest_rate))$latest_rate`% and `r arrange(table7, desc(latest_rate))$area_name[2]` with `r arrange(table7, desc(latest_rate))$latest_rate[2]`%. The MSOAs where the rate of Universal Credit claims has increased more from `r latestm12ch` to `r latestch` were `r arrange(table7, desc(Change))$area_name[1]` with `r arrange(table7, desc(Change))$Change[1]`% and `r arrange(table7, desc(Change))$area_name[2]` with `r arrange(table7, desc(Change))$Change[2]`% increase. The MSOA where the rate of Universal Credit claims has decreased from `r latestm12ch` to `r latestch` was `r arrange(table7, Change)$area_name[1]` with `r arrange(table7, Change)$Change[1]`%. +The MSOAs with higher rates of Universal Credit claims in `r latestch` were `r filter(table7, latest_rate == max(latest_rate)) %>% pull(area_name)` with `r filter(table7, latest_rate == max(latest_rate))$latest_rate`% and `r arrange(table7, desc(latest_rate))$area_name[2]` with `r arrange(table7, desc(latest_rate))$latest_rate[2]`%. The MSOAs where the rate of Universal Credit claims has increased more from `r latestm12ch` to `r latestch` were `r arrange(table7, desc(Change))$area_name[1]` with `r arrange(table7, desc(Change))$Change[1]`% ponts increase and `r arrange(table7, desc(Change))$area_name[2]` with `r arrange(table7, desc(Change))$Change[2]`% points increase.
```{r UCMSOADotPlot, fig.height=7} +# The MSOA where the rate of Universal Credit claims has decreased from `r latestm12ch` to `r latestch` was `r arrange(table7, Change)$area_name[1]` with `r arrange(table7, Change)$Change[1]`%. + uc_plot_dot <- table7 %>% select (area_name,{{latestm12}}:=latestm12_rate, {{latest}}:=latest_rate) %>% gather(period,value,-one_of("area_name")) %>% @@ -1020,7 +1028,7 @@ query <- list(database = unbox("str:database:UC_Monthly"), `str:field:UC_Monthly:V_F_UC_CASELOAD_FULL:COA_CODE` = list( map = list("str:value:UC_Monthly:V_F_UC_CASELOAD_FULL:COA_CODE:V_C_MASTERGEOG11_LA_TO_REGION:E08000009")), `str:field:UC_Monthly:F_UC_DATE:DATE_NAME` = list( - map = as.list(paste0("str:value:UC_Monthly:F_UC_DATE:DATE_NAME:C_UC_DATE:",c(202001,202002,202003,202004,202005,202006,202007,202008,202009,202010,202011,202012,202101,202102,202103,202104,202105,202106,202107,202108,202109,202110,202111,202112,202201,202202,202203,202204,202205,202206,202207,202208,202210,202211,202212,202301,202302,202303,202304,202305,202306,202307,202308,202309,202310,202311,202312)))), + map = as.list(paste0("str:value:UC_Monthly:F_UC_DATE:DATE_NAME:C_UC_DATE:",c(202001,202002,202003,202004,202005,202006,202007,202008,202009,202010,202011,202012,202101,202102,202103,202104,202105,202106,202107,202108,202109,202110,202111,202112,202201,202202,202203,202204,202205,202206,202207,202208,202210,202211,202212,202301,202302,202303,202304,202305,202306,202307,202308,202309,202310,202311,202312,202401)))), `str:field:UC_Monthly:V_F_UC_CASELOAD_FULL:AGE_CODE` = list( map = as.list(paste0("str:value:UC_Monthly:V_F_UC_CASELOAD_FULL:AGE_CODE:C_UC_AGE_BAND:",c(1,2,3,4,5,6,7,8,9,10,999)))) @@ -1219,7 +1227,7 @@ query <- list(database = unbox("str:database:UC_Monthly"), `str:field:UC_Monthly:V_F_UC_CASELOAD_FULL:COA_CODE` = list( map = as.list(paste0("str:value:UC_Monthly:V_F_UC_CASELOAD_FULL:COA_CODE:V_C_MASTERGEOG11_MSOA_TO_LA:E0", seq(2001259, 2001286, 1)))), `str:field:UC_Monthly:F_UC_DATE:DATE_NAME` = list( - map = as.list(paste0("str:value:UC_Monthly:F_UC_DATE:DATE_NAME:C_UC_DATE:",c(202312)))), + map = as.list(paste0("str:value:UC_Monthly:F_UC_DATE:DATE_NAME:C_UC_DATE:",c(202401)))), `str:field:UC_Monthly:V_F_UC_CASELOAD_FULL:CCCONDITIONALITY_REGIME` = list( map = as.list(paste0("str:value:UC_Monthly:V_F_UC_CASELOAD_FULL:CCCONDITIONALITY_REGIME:C_UC_CONDITIONALITY_REGIME:",c("AA","AB","BC","BD","DF","CE")))) @@ -1249,7 +1257,7 @@ uc_msoa_cond<- uc_condition %>% ``` -The [Conditionality](https://stat-xplore.dwp.gov.uk/webapi/metadata/UC_Monthly/Conditionality%20Regime.html) regimen for entitlement to Universal Credit is associated to work-related things that claimants will have to do to maintain eligibility. In Trafford the largest proportion of Universal Credit claims are in the "No work requirements" category however there is also a significant proportion of claims under "Searching for work". In Old Trafford, 35% of Universal Credit claimants were under "No work requirements" whilst 27% where "Searching for work". In Partington, 46% of claims were under "No work requirements" whilst 21% of claims were under "Working - no requirements", and 19% under "Searching for work". +The [Conditionality](https://stat-xplore.dwp.gov.uk/webapi/metadata/UC_Monthly/Conditionality%20Regime.html) regimen for entitlement to Universal Credit is associated to work-related things that claimants will have to do to maintain eligibility. In Trafford the largest proportion of Universal Credit claims are in the "No work requirements" category however there is also a significant proportion of claims under "Searching for work". In Old Trafford, 35% of Universal Credit claimants were under "No work requirements" whilst 28% where "Searching for work". In Partington, 46% of claims were under "No work requirements" whilst 19% of claims were under "Working - no requirements", and 17% under "Searching for work". ```{r UCConditionMMK, fig.height=5.5,fig.width=7} @@ -1295,7 +1303,7 @@ query <- list(database = unbox("str:database:UC_Monthly"), `str:field:UC_Monthly:V_F_UC_CASELOAD_FULL:COA_CODE` = list( map = list("str:value:UC_Monthly:V_F_UC_CASELOAD_FULL:COA_CODE:V_C_MASTERGEOG11_LA_TO_REGION:E08000009")), `str:field:UC_Monthly:F_UC_DATE:DATE_NAME` = list( - map = as.list(paste0("str:value:UC_Monthly:F_UC_DATE:DATE_NAME:C_UC_DATE:",c(202001,202002,202003,202004,202005,202006,202007,202008,202009,202010,202011,202012,202101,202102,202103,202104,202105,202106,202107,202108,202109,202110,202111,202112,202201,202202,202203,202204,202205,202206,202207,202208,202210,202211,202212,202301,202302,202303,202304,202305,202306,202307,202308,202309,202310,202311,202312)))), + map = as.list(paste0("str:value:UC_Monthly:F_UC_DATE:DATE_NAME:C_UC_DATE:",c(202001,202002,202003,202004,202005,202006,202007,202008,202009,202010,202011,202012,202101,202102,202103,202104,202105,202106,202107,202108,202109,202110,202111,202112,202201,202202,202203,202204,202205,202206,202207,202208,202210,202211,202212,202301,202302,202303,202304,202305,202306,202307,202308,202309,202310,202311,202312,202401)))), `str:field:UC_Monthly:V_F_UC_CASELOAD_FULL:CCCONDITIONALITY_REGIME` = list( map = as.list(paste0("str:value:UC_Monthly:V_F_UC_CASELOAD_FULL:CCCONDITIONALITY_REGIME:C_UC_CONDITIONALITY_REGIME:",c("AA","AB","BC","BD","DF","CE")))) diff --git a/unemployment_covid-19/claimant_count_and_universal_credit.html b/unemployment_covid-19/claimant_count_and_universal_credit.html index 988191e..aa73e49 100644 --- a/unemployment_covid-19/claimant_count_and_universal_credit.html +++ b/unemployment_covid-19/claimant_count_and_universal_credit.html @@ -236,7 +236,8 @@ setFloat64:function 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+
-

+

-

The Trafford MSOA with the largest share of claims in December 2023 -was Old Trafford with 13.2% of all claimants within Trafford, almost the +

The Trafford MSOA with the largest share of claims in January 2024 +was Old Trafford with 13.8% of all claimants within Trafford, almost the double of claims than Gorse Hill, the MSOA with the second largest -number of claims. 13 MSOAs out of 28 have more claims in December 2023 -compared to December 2022. Timperley East have the largest increase in -number of claims with 35 more, followed by Broadheath & Firsway (30) -and Firswood (25). Partington has the largest reduction in number of -claims with 25 less, followed by Lostock & Stretford Meadows +number of claims. 12 MSOAs out of 28 have more claims in January 2024 +compared to January 2023. Old Trafford have the largest increase in +number of claims with 60 more, followed by Gorse Hill (50) and Firswood +(35). Partington has the largest reduction in number of claims with 30 +less, followed by Lostock & Stretford Meadows (-20) and Sale Central (-20).

Change in the Claimant Count for Trafford's MSOAs
-
- +
+
-

The MSOAs with higher claimant rate in December 2023 were Old -Trafford with 7.3%, Gorse Hill with 5.6%, Partington with 5.6% and -Firswood with 5%. From the top 5 MSOAs with the highest rates, only -Partington had a lower rate when comparing December 2022 to December -2023. The MSOAs where the claimant rate have decreased more from -December 2022 to December 2023 were Lostock & Stretford Meadows and -Partington with a decrease in rate of -0.5%.

+

The MSOAs with higher claimant rate in January 2024 were Old Trafford +with 7.6%, Gorse Hill with 5.9%, Partington with 5.5% and Firswood with +5.1%. From the top 5 MSOAs with the highest rates, only Partington had a +lower rate when comparing January 2023 to January 2024. The MSOAs where +the claimant rate have decreased more from January 2023 to January 2024 +was Partington with -0.6% points decrease.

-

+

-

+

-

In Trafford from December 2022 to December 2023 5 age bands has -increased the number of claimants, the age band 18 to 24 had the most -increase with 50 more. When considering the reduction of claims the age -band 60-64 years had the largest reduction with -40 less claims.

+

In Trafford from January 2023 to January 2024 only 3 of the age bands +has decreased the number of claimants. The age band 18 to 24 and 40 to +44 had the most increase with 45 more each. When considering the +reduction of claims the age band 60-64 years had the largest reduction +with -35 less claims.

Change in the Claimant Count by age bands in Trafford
-
- +
+
Change in the Claimant Count by broad age bands in Trafford
-
- +
+
@@ -2178,24 +2200,24 @@

Claimant Count in Trafford

fluctuating from April 2023.

-

+

-

+

-

More than half of Trafford’s claimants are residents of 8 of the 28 -MSOAs. Around half of the claimants are between 25 and 44 years.

+

More than half of Trafford’s claimants are residents of 1 quarter of +the MSOAs. Around half of the claimants are between 25 and 44 years.

Claimant Count for Trafford's MSOAs by age band
-
- +
+
-

+

@@ -2206,62 +2228,58 @@

Claimant Count in Trafford

Universal Credit claims in Trafford

The Universal -Credit claims in Trafford have increased 95.9% from January 2020 to -December 2023. The rate of claims as a proportion of people age 16 to 64 -increased form 6.1% to 12% from January 2020 to December 2023.

+Credit claims in Trafford have increased 97.3% from January 2020 to +January 2024. The rate of claims as a proportion of people age 16 to 64 +increased form 6.1% to 12.1% from January 2020 to January 2024.

Change in the Universal Credit claims for Trafford
-
- +
+
-

+

-

The Trafford MSOA with the largest share of claims in December 2023 +

The Trafford MSOA with the largest share of claims in January 2024 was Old Trafford with 11.8% of all Universal Credit claims in Trafford. -27 out of the 28 MSOAs have more claims in December 2023 compared to -December 2022. The MSOAs with more additional claims when comparing -December 2022 to December 2023 were Partington (168), Broadheath & -Firsway (167) and Old Trafford (161). The MSOAs with more increase in -percentage change from December 2022 to December 2023 were Ashton upon -Mersey South with 18% change and Timperley South with 18% change. The -MSOA with reductions in claims when comparing December 2022 to December -2023 is Sale North (-12). The MSOA with reduction in percentage change -from December 2022 to December 2023 was Sale North with -2% change.

+27 out of the 28 MSOAs have more claims in January 2024 compared to +January 2023. The MSOAs with more additional claims when comparing +January 2023 to January 2024 were Old Trafford (196), Partington (187) +and Broadheath & Firsway (147). The MSOAs with more increase in +percentage change from January 2023 to January 2024 were Ashton upon +Mersey South with 23% change and Trafford Park East & Sevenways with +19% change.

Change in the Universal Credit claims for Trafford's MSOAs
-
- +
+
-

The MSOAs with higher rates of Universal Credit claims in December -2023 were Partington with 31.1% and Old Trafford with 25.9%. The MSOAs +

The MSOAs with higher rates of Universal Credit claims in January +2024 were Partington with 31.5% and Old Trafford with 26.2%. The MSOAs where the rate of Universal Credit claims has increased more from -December 2022 to December 2023 were Partington with 3.4% and Broadheath -& Firsway with 2.7% increase. The MSOA where the rate of Universal -Credit claims has decreased from December 2022 to December 2023 was Sale -North with -0.2%.

+January 2023 to January 2024 were Partington with 3.7% ponts increase +and Old Trafford with 2.5% points increase.

-

+

-

+

-

In Trafford from December 2022 to December 2023 the Over 65 age band +

In Trafford from January 2023 to January 2024 the Over 65 age band has the largest increment in percentage change of Universal Credit -claims with 67%. When considering the number of additional claims from -December 2022 to December 2023 the 5-year age band 40-44 years had the -highest with 351 more claims. The age bands 20-24 had a decrease in +claims with 60%. When considering the number of additional claims from +January 2023 to January 2024 the 5-year age band 40-44 years had the +highest with 379 more claims. The age bands 20-24 had a decrease in number of Universal Credit claims.

Change in the Universal Credit claims by age band for Trafford
-
- +
+

Following the start of the Covid-19 pandemic in March 2020 the number of claims for all ages rose dramatically from pre-pandemic levels at a @@ -2275,10 +2293,10 @@

Universal Credit claims in Trafford

particularly from January 2023.

-

+

-

+

The Conditionality @@ -2288,21 +2306,21 @@

Universal Credit claims in Trafford

claims are in the “No work requirements” category however there is also a significant proportion of claims under “Searching for work”. In Old Trafford, 35% of Universal Credit claimants were under “No work -requirements” whilst 27% where “Searching for work”. In Partington, 46% -of claims were under “No work requirements” whilst 21% of claims were -under “Working - no requirements”, and 19% under “Searching for +requirements” whilst 28% where “Searching for work”. In Partington, 46% +of claims were under “No work requirements” whilst 19% of claims were +under “Working - no requirements”, and 17% under “Searching for work”.

-

+

Claimants continue to move across from legacy benefits to Universal credit therefore, the number of claimants with no work requirements -continue to grow. In December 2023, 38% (6,625) of claims in Trafford -were under “No work requirements”, 23.3% (4,069) were under “Searching -for work”, 18.2% (3,179) were under “Working - no requirements”, 14% -(2,451) were under “Working - with requirements”, 5.2% (910) were under -“Preparing for work”, and 1.2% (217) where under “Planning for work”. +continue to grow. In January 2024, 38.2% (6,714) of claims in Trafford +were under “No work requirements”, 23.1% (4,055) were under “Searching +for work”, 19% (3,346) were under “Working - no requirements”, 13.5% +(2,368) were under “Working - with requirements”, 5% (882) were under +“Preparing for work”, and 1.2% (209) where under “Planning for work”.
-

+