Skip to main content

Foreword

Performance against quantitative targets

Main findings

The programme supported over 3,250 participants with at least three meaningful interventions in a six-to-ten-week period (meeting 82% of the target). Other participants were supported with three meaningful interventions over a longer period.

The programme had most success in relation to job starts for out of work participants. There were 1,123 job starts claimed until December 2021 (106% of the target). Sustainment in work was below target (89% at 13 weeks, 85% at 26 weeks and 66% at 52 weeks). Sixty- eight per cent of participants starting work remained in work 13 weeks later. The proportion of customers with a job outcome sustained at 13 weeks is largely the same between cohorts.

The programme supported 458 working participants to progress in work (46% of the target). Participants who were out of work at the start of the programme accounted for just under half (44%) of the employed progression claims. Providers explained that participants in some groups wanted to seek work for a few hours as a steppingstone to help them transition gradually back to work, and then progress by increasing the number of hours worked.

Providers were less able to reach people already in work that might have benefited from or have wanted support to progress.

Regression analysis exploring who found work showed the likelihood of finding work was significantly higher among the Rapid Progression cohort than the Hardest to Help group. After controlling for other factors in the model, participants whose completed actions had included identifying possible jobs that matched their skills were twice as likely to achieve a job outcome than those who did not complete this action. Participants for whom the programme was being delivered by a small or medium organisation were twice as likely to achieve a job outcome than those whose delivery was provided by an organisation in the East Birmingham & Solihull Urban Partnership. Participants without a health condition or disability, those not claiming benefit, and participants in the mid-age ranges (25–34 and 45– 54 years) were also more likely than others to secure a job outcome.

Regression analysis focused on 13-week sustainment found that, when controlling for other factors, participants aged over 25 and completing actions to identify possible jobs that matched their skills were more likely to remain in work after 13 weeks.

Logistic regression to investigate the likelihood of a customer progressing in work, found that when controlling for other factors, participants who were not claiming benefits were more likely to progress in work compared with participants who were claiming benefits.

This chapter uses audited claims data and analysis of management information to summarise the programme’s performance against targets up until March 2022.

Programme engagement

Claims data shows that over 4,000 residents were engaged on the programme. Of these, 3,255 were embedded2 (82% of the target) (Figure 2-1). There was more attrition between registering and significant involvement in the support than anticipated during programme design, with 19 per cent of registrants not embedding compared to an initial estimate of 12 per cent.

The programme targeted groups based on their length of time out of work. The sample of management information found the Hardest to Help group, those out of work for more than two years, made up 36 per cent of participants3. The Harder to Reach group, out of work for between one and two years, made up 14% of participants, the Rapid Progression group 35 per cent and Employed Progression 16 per cent (Table 2.1).

Table 2-1 Total number of participants engaged, by cohort

 
Participants engaged
%

Harder to Reach (out of work 1-2 years)

557 14

Hardest to Help (out of work more than 2 years)

1418 36

Rapid Progression (out of work less than one year)

1372 35

Employed progression (in work)

619 16
Total
3966
100

 

Figure 2-1 Progress towards programme targets

 

Target - 4500

Actuals - 4039

Target - 3974

Actuals - 3255

Target - 1058

Actuals - 1123

Target - 861

Actual - 768

Target - 711

Actuals - 602

Target - 531

Actuals - 349

Target - 1004

Actual - 458

The programme engaged people from a range of demographic groups across the communities. Figure 2.2 summarises some of the characteristics of programme participants. A little over half of participants were female (53%) and around half were male (47%) (N = 3,957) (Table A-3). Participants’ ages ranged from 16 to 75 years of age (N = 3,864). Around one in three participants were aged 16–24 (29%), with one in ten (10%) aged 50 or older (Table A-4). Reflecting the ethnic diversity in the communities, a little under half (42%) of participants identified as being from a minority ethnic background (N = 3,831), with participants from an Asian ethnic background making up around one third (29%) of all participants (Table A-5). The ethnic diversity of participants varied between areas reflecting their varied ethnic profiles, with participants at Cannock North (98%), Glascote (96%), Camp Hill, Nuneaton & Bedworth (89%) and Chelmsley Wood (89%) predominantly from white ethnic backgrounds, whereas those participating in Washwood Heath mostly identifying as from an Asian, Black or other minority ethnic group (83%) (Table A-6).

Other characteristics indicate the extent to which specific barriers to work might be identified among participants. With regards to prior education, one third of participants (30%) reported that their highest qualification was at level 2 such as a GCSE at grade A* – C, a little under a fifth had an entry level or level 1 qualification such as GCSE grade D–G or no qualification (22% and 17% respectively), just over a fifth (23%) had a level 3 qualification such as an level 3 NVQ, and eight per cent were qualified to degree level or higher (N = 3,187) (Table A-7).

One third of participants (34%) were caring for a child under the age of 16 years (N = 2,676) (Table A-8). Around one in five parents of children under 16 (21%) reported that they were the sole carer for their children (N = 1,732) (Table A-9). Five per cent of participants reported that they were caring for an adult dependent (N = 2,663) (Table A- 10).

One quarter of participants (25%) reported having a health condition or disability and less than one per cent either chose not to disclose or the information was not collected (N = 3,812) (Table A-11). Among those who specified a health consideration for their work and career development (N=930), around a third reported experiencing mental health issues, such as anxiety (34%) or depression (31%), and just under a fifth (18%) had dyslexia or another learning difficulty (Table A-12).

Figure 2.2 Demographic characteristics of programme participants

male - 47%

female - 53%

BME - 42%

White - 58%

16-24 (29%)

25-34 (25%)

35-44 (21%)

45-54 (15%)

55+ (10%)

No qualifications (17%)

Entry level (4%)

Level 1 (18%)

Full level 2 (30%)

Full level 3 (23%)

Level 4-6, level 7+ (8%)

Child under 16 (34%)

Adult dependant (4%)

No caring responsibility (62%)

Yes (25%)

No (74%)

Choose not to say (1%)

The Employed Progression group were in work at the time of enrolment (N=619) and gave details of their current work. It should be noted that not all providers reported these data, so findings should be treated with caution. Most participants in employment worked less than 16 hours a week (44%). Just 13 per cent worked full-time (35 hours a week or more) (N = 371) (Table A-13). Most participants in the employed progression group (67%) earned less than £199 per week on average (36% earned less than £100 per week, 31% earned £100-199 per week on average) (N = 328) (Table A-14). Most participants in the employed progression group were on a permanent contract (54%), a quarter were on zero hours (26%), and one in ten were on a fixed-term contract or other variable work such as seasonal work (8% and 9% respectively, N = 365) (Table A-15).

Work outcomes

It is in relation to securing outcomes for out of work participants that the programme has had most success. Claims data shows there were 1,123 job starts until March 2022 (106% of the target). However, job sustainment was below target on all measures; with 89 per cent of the 13-week sustainment target met (768 participants); 85 per cent of the 26 week sustainment target met (602 participants); and 66 per cent of the 52 week sustainment achieved (349 participants) (Figure 2-1). There are notable variations in job outcomes rates between providers. Using management information, the proportion of embedded out-of-work participants finding work is 41 per cent overall but varied from 59 per cent to 21 per cent between providers (Table A-16). Job sustainment for 13 weeks also varied between providers (Table A-17). Possible explanations for these differences are discussed in Chapter 5.

The likelihood of finding work varies between the three out of work cohorts. Error! Reference source not found. illustrates the number of participants in each cohort achieving the programme outcomes, and the conversion rate at each point, using management information. For example, Rapid Progression participants (56%) are more likely than the Hardest to Help (30%) and Harder to Help (36%) groups to start a job. These differences were anticipated by the programme and are inbuilt into the payment model as they reflect varied labour market attachment, recency of work history and barriers to work. The proportion of customers with a job outcome sustained at 13 weeks is largely the same (between 65% and 68%).

Looking at outcomes for the in-work group claimed by providers, 458 working participants were supported to progress in work: 46 per cent of the target (Error! Reference source not found.). The proportion of in-work progressions claimed for the embedded in-work group varied between providers, with zero to 55 per cent of participants enrolled in this group achieving a progression in work. Providers that had higher numbers of job outcomes were also stronger at delivering in-work progressions, in part because just under half (44%) of employed progressions claimed, resulted from participants who were out of work at the start of the programme. Providers explained that participants in some groups wanted to seek work with a few hours as a steppingstone to help them transition gradually back to work, and then progress by increasing the number of hours worked.

Figure 2.3 Job outcomes by cohort
 
Starts
Starts embedded
Job outcome
13 weeks
26 weeks
In work progression
Hardest to help
1328 1087 328 222 153 56
Hard to help
516 443 158 107 77 21
rapid
progression
1273 973 536 351 272 98
Employed
579 462 N/A N/A N/A 221
Which groups found work?

Some groups of embedded participants who were out of work when they joined the programme were more likely than others to secure a job outcome (Figure 2-4):

  • Participants without a health condition or disability, were more likely than participants with a health condition or disability to gain a job outcome (44% compared to 34%) (Table A-18). This mirrors findings from the Work Programme evaluation (DWP, 2014) and reflects the fact that the national disability employment rate is considerably lower than the overall employment rate (53% in Q2 2021, compared to 81%) (ONS, 2021).

  • Participants without caring responsibilities for children under the age of 16, were more likely than participants with these caring responsibilities to find work (44% compared to 38%). The context of the closure of schools during the pandemic is worth noting here (Table A-19).

  • Males (44%) were more likely than females (39%) to find work (Table A-20).

  • Participants from a white ethnic background (41%), were as likely as those from ethnic minority backgrounds (40%) to find work (Table A-21).

  • Participants in receipt of benefits were less likely (36%), than those not claiming benefits (51%) to find work (Table A-22).

  • Participants in the youngest age range (16–24 years), and mid-age range (45–54 years), were more likely than other age groups to gain a job outcome (45% and 44% respectively, compared to 36% of participants aged 25–34, 38% of participants aged 35–44, and 39% of those aged 55 or over) (Table A-23).

Figure 2-4 Proportion of participants achieving a job outcome, by characteristics

Not claiming benefits - 51%

Claiming benefits - 36%

White - 41%

Ethnic minority - 40%

Rapid Progression - 55%

Hardest to help - 30%

Harder to reach - 36%

55 and older - 39%

45-54 - 44%

35-44 - 38%

25-34 - 36%

16-24 - 45%

female - 39%

male - 41%

No caring responsibilities (u16) - 44%

Caring responsibilities (children U16) - 38%

No health condition - 44%

Health condition - 34%

To explore the interaction between these demographic characteristics, regression was undertaken. In the MI, there were 3,347 participants who belonged to the Harder to Reach, Hardest to Help and Rapid Progression cohorts. However, participants from Lot 5, Cannock North, were excluded because no data on actions completed was available. Participants whose support was delivered by a provider in the Colleges group were also excluded as there was a large proportion of data missing for this group. In the remaining sample, 2,550 participants had complete data in the relevant fields to be included in the analysis.

Logistic regression analysis was undertaken to investigate which personal characteristics or actions completed were significant influences on customers achieving a job outcome (Table A-24). The model found that, when controlling for other variables, the following factors significantly affected the achievement of a job outcome.

  • Cohort – Compared with the Hardest to Help cohort, the Rapid Progression group were 1.8 times more likely to have a job outcome after controlling for other factors in the model.
  • Type of provider - After controlling for other factors in the model, participants for whom the programme was being delivered by a small or medium organisation were two times as likely to achieve a job outcome than those whose delivery was provided by an organisation in the East Birmingham & Solihull Urban Partnership.

  • Health – After controlling for other factors in the model, participants who did not have a health condition were 1.4 times more likely to achieve a job outcome than participants who did have a health condition.

  • Highest qualification – Compared with participants with no qualifications, after controlling for other factors in the model, participants with qualifications at Levels 1 & 2 were 2.9 times as likely to have a job outcome, participants with a Level 3 qualification were 3.9 times as likely to have a job outcome, and participants with a qualification that was Level 4 or higher were 3.2 times as likely to have a job outcome.

  • Age – Compared with participants aged 25–34 years, participants aged 35–44 years 1.4 times more likely and those aged 45–54 years were 1.6 times more likely to have a job outcome after controlling for other factors in the model. This could be related to caring responsibilities for children aged under 16, especially in the context of the pandemic with school closures, but due to missing data, caring responsibilities was not controlled for in the regression model.

  • Claiming benefits – After controlling for other factors in the model, participants who were not claiming benefits were 1.3 times more likely to have a job outcome compared with participants who were claiming benefits.

  • Attending coaching sessions – After controlling for other factors in the model, participants who did not attend coaching sessions were 1.6 times more likely to have a job outcome compared with those who accessed this support. It could be that this type of support was offered in a targeted way to those who were less employment ready, whereas those who were closer to employment may have been able to progress straight to searching and applying for roles.

  • Matching skills with suitable jobs – After controlling for other factors in the model, participants whose completed actions included identifying possible jobs that matched their skills were 2.2 times as likely to achieve a job outcome than those who did not complete this action.

Which groups sustained job outcomes (13 weeks)?

Some groups who found work were more likely than others to sustain work for 13 weeks than others (Figure 2.5):

  • Participants without a health condition or disability, were more likely than those with a health condition or disability to sustain work (60% compared to 68%) (Table A-25).

  • Participants without caring responsibilities for children under the age of 16, were about as likely as participants with these caring responsibilities to sustain work for 13 weeks (63% compared to 65%) (Table A-26).

  • Females (68%) were slightly more likely than males (65%) to sustain work for 13 weeks (Table A-27).

  • Participants from a white ethnic background (65%), were less likely than those from ethnic minority backgrounds (69%) to sustain work for 13 weeks (Table A-28).

  • Participants in receipt of benefits were less likely (56%), than those not claiming benefits (69%) to sustain work for 13 weeks (Table A-29).

  • Participants in the youngest and oldest age ranges (16–24 years, and 55 or over) (both 63%) were less likely than other age groups to sustain work for 13 weeks (Table A-30).

 

Figure 2.5 Proportion of participants with a job outcome sustaining at 13 weeks, by characteristics

Not claiming benefits - 69%

Claiming benefits - 65%

White - 65%

Ethnic minority - 69%

Rapid progression - 65%

Hardest to help - 68%

Harder to reach - 68%

55 and older - 63%

45-54 - 69%

35-44 - 68%

25-34 - 67%

16-24 - 63%

female - 68%

male - 67%

No caring responsibilities (u16) - 63%

Caring responsibilities (children U16) - 65%

No health condition - 68%

Health condition - 60%

 

In the MI, there were 1,022 participants who belonged to the Harder to Reach, Hardest to Help and Rapid Progression cohorts who had achieved a job outcome. To be consistent with the sample used for job outcomes analysis, participants from Lot 5, Cannock North, where no data on actions completed was available were excluded; and participants whose support was delivered by a provider in the Colleges group were also excluded as there was a large proportion of data missing in this group. In the remaining sample, 823 participants had complete data in the relevant fields to be included in further analysis.

Logistic regression analysis was undertaken to investigate which personal characteristics or actions completed were significant influences on the likelihood of a customer staying in work for 13 weeks once they had achieved a job outcome (Table A-31). The model found that, when controlling for other variables, the following factors significantly affected the likelihood of remaining in employment after 13 weeks.

  • Age – Compared with participants aged 16–24 years, participants aged 35–44 years were 1.8 times more likely, those aged 45–54 years were 2.0 times as likely to have a job outcome after controlling for other factors in the model.

  • Matching skills with suitable jobs – After controlling for other factors in the model, participants whose completed actions had included identifying possible jobs that matched their skills were 2.0 times as likely to still be in work 13 weeks after achieving a job outcome.

Which groups progressed in work?

Looking at participants in the Employed Progression group when they joined the programme, some groups were more likely than others to secure in-work progression, either increasing hours worked or pay (Figure 2.6):

  • Participants without a health condition or disability, were more likely than those with a health condition or disability to progress in work (50% compared to 41%) (Table A-33).

  • Participants without caring responsibilities for children under the age of 16, were more likely than participants with these caring responsibilities to progress in work (49% compared to 38%) (Table A-34).

  • Females (50%) were more likely than males (44%) to progress in work (Table A-35).

  • Participants from a white ethnic background (48%), were as likely as those from ethnic

    minority backgrounds (49%) to progress in work (Table A.36).

  • Participants in receipt of benefits were less likely (36%), than those not claiming benefits (54%) to progress in work (Table A-37).

  • Participants in the youngest age ranges (16–24 years, and 25–34), and mid-age range (45–54 years), were more likely than other age groups to progress in work (58%, 52% and 51% respectively), compared to 37% of participants aged 35–44, and 29% of those aged 55 or over) (Table A-38).

Figure 2-6 Proportion of participants achieving in-work progression, by characteristics

Not claiming benefits - 54%

Claiming benefits - 36%

White - 48%

Ethnic minority - 49%

55 and older - 29%

45-54 - 51%

35-44 - 37%

25-34 - 52%

16-24 - 58%

female - 50%

male - 44%

No caring responsibilities (u16) - 49%

Caring responsibilities (children U16) - 38%

No health condition - 50%

Health condition - 41%

 

In the MI, there were 619 participants who belonged to the employed group. To be consistent with the samples used for job outcomes and 13 weeks sustainment analysis, participants from Lot 5, Cannock North, were excluded where no data on actions completed was available. Participants whose support was delivered by a provider in the Colleges group were also excluded as there was a large proportion of data missing in this group. In the remaining sample, 491 participants had complete data in the relevant fields to be included in further analysis.

Logistic regression analysis was undertaken to investigate whether any personal characteristics or actions were significant influences on the likelihood of a customer progressing in work (Table A-39). The model found that, when controlling for other variables, the following factors significantly affected the likelihood of progressing in work. Please note that these findings should be treated with a degree of caution as the dataset used for analysis was small and it is possible that a similar analysis with a larger, more complete dataset may identify other factors that significantly influence the likelihood of progressing in work.

  • Claiming benefits – After controlling for other factors in the model, participants who were not claiming benefits were 1.8 times more likely to progress in work compared with participants who were claiming benefits. This may be explained by the complexity of needs among the group claiming benefits, or the interaction with the taper rate within Universal Credit.