Employment and skills

Employment rates (%) by ethnic group, 2019 (source: Annual Population Survey)

All

UK - 76

West Midlands - 74

Asian

UK - 65

West Midlands - 62

Indian

UK - 76

West Midlands - 77

Pakistani / Bangladeshi

UK - 56

West Midlands - 52

Asian other

UK - 65

West Midlands - 55

Black

UK - 69

West Midlands - 62

Mixed

UK - 69

West Midlands - 61

White

UK - 78

West Midlands -77

White British

UK - 77

West Midlands - 77

White other

UK - 83

West Midlands - 84

Other

UK - 63

West Midlands - 56

BME groups are disproportionately concentrated in low paying jobs

BME groups (in aggregate) are more likely to receiving low pay in work.8 This is in part because of their concentration in low-paid occupations and sectors with limited prospects for progression.9 Examples for women include sales, catering, hairdressing, textiles and clothing, while for men there is an over-representation relative to the average in sectors such as hospitality and transport. A lack of movement out of low pay increases the risk of poverty.

In employment overall, Bangladeshi and Pakistani people have the lowest rates of employment, and face the highest ethnic pay gaps (compared with white workers) and highest rates of in-work poverty. Ethnic disparities in low pay have been exacerbated during the Covid-19 pandemic: in July 2020 during COVID-19, BME workers had suffered an average drop in earnings of 14% from February compared to a 5% drop for white workers.10

BME groups have been hit disproportionately hard by Covid-19

A review of recent evidence suggests that workers who are from a BME background have been one of the groups most negatively impacted economically by the Covid-19 outbreak. Analyses of Labour Force Survey data for the period of the first national lockdown in 202011 show that decreases in employment during widened the employment rate gap from 22 percentage points to 26 points for Black people and to 25 points for Asian people. Young people have been especially hard hit: the reduction in employment rates was four times greater for young Black people than for young white people, while the fall for young Asian people has been nearly three times greater. This comes on top of fewer BME young people being in employment prior to the Covid-19 pandemic (in part because of higher participation in education). It is likely that occupational and sectoral factors are key drivers here, with people from BME groups losing out more where employment contracted (e.g. in hospitality).

The lack of new job openings in many (but not all) occupations and sectors during lockdown has a negative effect on individuals’ career paths and overall productivity. While from summer 2021 vacancies have recovered in aggregate terms, the profile of vacancies is different from the picture before the pandemic. Taking account of individuals’ current and previous occupations and individuals’ pre-pandemic patterns of movement between occupations, analyses from the Institute for Fiscal Studies12 shows that new opportunities are strongest in lowest-paying occupations,

but a large share of workers still face reduced opportunities compared with before the Covid-19 pandemic. Within all sub-groups of the population this translates into increased opportunities for some and decreased opportunities for others. Figure 3 shows that groups more likely to see increased opportunities than average (based on pre-pandemic employment patterns) are those qualified to GCSE level or below (especially compared with graduates who are more likely to see reduced opportunities), young people and the Black ethnic group. Figure 4, showing changing job opportunities by job quality (measured according to pay, with the lowest third of the pay distribution being deemed ‘low’ quality, the middle third ‘medium’ quality and the top third ‘high’ quality). This shows that BME groups do not appear to be disadvantaged overall in terms of new opportunities relative to the White group. However, it needs to be borne in mind that for 64% of unemployed workers competition for relevant job openings is at least 10% greater than before the Covid-19 pandemic.

A report from Public Health England13 found that BME individuals are more likely to work in occupations with a higher risk of COVID-19 exposure (such as care workers, taxi and cab drivers, security guards and sales and retail assistants),14 and 15% of workers in the sectors most affected by the pandemic are from a BME group, compared to 12% of all workers. A survey by the Runnymede Trust found people from a minority ethnic background are ‘consistently more likely’ than people from a White background to have experienced negative financial impacts due to Covid-19 and lockdown: 32% of people from BME groups reported losing ‘some income’, compared with 23% of people from a white background.

Barriers facing BME working age adults to accessing/participating in learning and employment

Cultural and structural issues can post barriers to accessing/participating in learning and employment

Cultural issues include expectations about whether and what work is appropriate (including work alongside education which may help in gaining workplace skills and experience), and also non- work responsibilities, notably caring for other household members. First generation from BME groups may face barriers in the labour market because of limited/ low levels of English language proficiency and lack of knowledge about how the UK (and local) labour market operates.

Structural issues include living in deprived/ economically weak areas where there are deficiencies in the quantity and quality of jobs; living in poverty – with implications for constraints on costs for transport, broadband, etc.; access to networks which may be helpful in accessing work and learning; and racism and associated discrimination. BME groups are twice as likely to be living in an area of high deprivation than white groups,15 which ceteris paribus tends to be associated with poorer literacy and numeracy skills. Some access to useful labour market experience and recruitment to jobs takes place through informal networks, so lack of access to relevant networks by some individuals and sub-groups can create barriers to job entry (at various skill levels) and progression.16

In aggregate, BME groups face greater challenges in transitioning from unemployment to employment

In aggregate, unemployment rates are higher for most BME groups than the White British majority. However, analyses using the UK Household Longitudinal Study suggest that for most BME groups there is almost no difference in the probability of transitioning from a paid job into unemployment (suggesting that the productivity of BME workers is no lower than for the white majority). However, amongst those who transition into unemployment, most BME groups experience unemployment durations that are longer than those of White British majority.17 These differences remain largely unexplained after the inclusion of various individual and household characteristics, so highlighting that it is the duration of unemployment that is a key factor in ethnic unemployment differentials. This suggests that discrimination may be a factor in ethnic unemployment differentials.

Another study18 adopting a longitudinal perspective investigates lower employment rates amongst BME groups than their white majority counterparts, likewise finds that ethnic differentials are reduced, but not eliminated, when taking account of disadvantaged family origins. This suggests some of the employment gap is driven by the disadvantages faced by their parents that persist across generations and are reduced, but not eliminated, by educational success.

A further longitudinal study using data from the UK Household Longitudinal Study for the period 2009 to 2014 also points to the cumulative nature of BME disadvantage (in aggregate).19 Further disaggregation shows that this is especially pronounced for Black African, Black Caribbean, Pakistani and Bangladeshi groups facing higher risks of unemployment – including due to delayed re-entry to the labour market once unemployed (even with similar levels of prior unemployment) - and lower levels of earnings than do their White British counterparts over the life course.

In aggregate, BME graduates are more likely to face delays gaining employment after graduation

BME groups are more likely than the white majority to gain university qualifications, but experience worse labour market outcomes on average. Analyses of employment and earnings of BME graduates with those of White British graduates suggest that a substantial part of persistent ethnic differences in earnings can be explained by differences in parental background, local area characteristics or differences in university careers.20

Gaining relevant employment relatively quickly after graduation seems to be crucial to the future prospects of BME graduates. The analysis finds almost no earnings differences by ethnicity between graduates six months after graduation, and when continuously employed over the next few years there is little difference in earnings growth. But there are substantial inequalities in the probability of being employed, with BME graduates much less likely to find employment six months after graduation.

The importance of local area characteristics and parental background in explaining part of the ethnic differences in earnings and employment suggest that some BME groups may face a deficit in networks and support which could help facilitate the transition to the labour market. This highlights a role for careers advice for students from disadvantaged areas to help reduce ethnic differences in employment as they transition to beginning their careers. Other policies that might help include encouraging participation in extra- curricular activities to help develop soft skills. Likewise, evidence of workplace-relevant hard skills could be helpful for labour market access.

The hiring practices of large employers may also indirectly discriminate against ethnic groups through hiring practices focusing on universities with fewer BME graduates.

The role of ethnic penalties and discrimination in understanding the labour disadvantage of some BME groups

In aggregate, BME groups are less likely to be employed or to have good jobs than the white majority group. This difference has been attributed to ethnic penalties:21 the difference between the measured effect (e.g. the (un) employment rate) between various BME groups compared with the majority group once other differences have been controlled for (e.g. location, age, human capital, social capital, etc.). Ethnic penalties are not necessarily the result of discrimination (i.e. where employers are less likely to hire an ethnic minority applicant than a majority applicant with identical credentials and a very similar CV), but it would be expected that higher ethnic penalties are associated with discrimination.

A recent academic article has connected results from two UK-based field experiments (where similar applications with different names associated with different ethnic groups are sent to employers) with ethnic penalties estimated from comparable samples of the UK Labour Force Survey and Understanding Society to show the relation between hiring discrimination and labour market penalties for several BME groups.22 The results of the analyses suggest that higher hiring discrimination is associated with ethnic employment penalties, and generally groups that experience worse hiring discrimination have higher ethnic penalties in employment.

It identifies variations between ethnic groups. First, Black African, Pakistani/Bangladeshi, Middle Eastern and North African, and Black Caribbean applicants display high employment penalties and relatively severe hiring discrimination. Secondly, Other White minorities face low employment penalties in line with lower hiring discrimination.

Thirdly, Chinese and Indian groups display lower employment penalties than the hiring discrimination they face would suggest. The results also suggest that the highly qualified are more resistant to hiring discrimination, while those with low and middle level qualifications face greater challenges in finding appropriate work.

This evidence points to the need to tackle hiring discrimination. In large organisations there is a clear role for HR in terms of raising issues regarding conscious and unconscious bias and ensuring relevant guidelines are in place for those staff involved in recruitment and selection of potential workers. In small organisations there is less likely to be a dedicated HR function to devote responsibility to this. There are also broader issues about how and where opportunities are advertised, which may result in some groups being more likely to see adverts than others. This highlights the value of organisations reviewing and assessing the recruitment channels that they use. Recruitment through networks is likely to accentuate homophily (i.e. to result in recruits similar to the existing workforce, given that contact amongst similar people tends to occur at a higher rate than amongst dissimilar people).

There are a number of ways that BME groups (and indeed individuals in all groups) can be supported to behave strategically to reduce the impact of hiring discrimination on ethnic (or other) penalties. The way an application is presented to an employer is important, and all candidates can gain from focusing on employer requirements and what candidates can bring

to a role. Job search strategy is important too; job seekers may benefit from advice on the relative merits of a targeted vis-à-vis a scattergun approach to job search, which may involve changing the number and quality of jobs applied to. It is notable that discrimination appears to be lower in the public than in the private sector; hence for some groups facing hiring discrimination including applications to the public sector (as applicable) may be appropriate. For all individuals, searching through social networks rather than through more formal methods is one way of seeking knowledge about upcoming opportunities and also intelligence about what is required from candidates. Some individuals avoid.

Demographics – differentials by sub-group and gender

Disadvantaged White British pupils perform poorly at school

Across ethnic groups, pupils eligible for Free School Meals underperform compared to their more affluent peers. However, White British and White Other children from low income homes are the lowest performing groups at primary school.23 White British pupils also make the least progress throughout secondary school, ranking in the lower half of achievement rates for GCSE (five A*-C grades by age 19 years), A levels (2 or more A levels by age 19 years) and permanent exclusions. Despite having amongst the lowest higher education participation rates, white graduates have amongst the highest rates of high skilled employment and white adults have high employment rates.

Gypsy Roma students have the lowest educational attainment levels and also the highest permanent exclusion rates.

Black pupils achieve less

Analyses of ethnic inequalities from 2015 to the eve of the Covid-19 pandemic24 show that ethnic inequalities in the proportion of children achieving good level of development (measured by the Early Years Foundation Stage profile) have narrowed for most ethnic groups, but increased for Black children. Although not starting school in a disadvantaged position in terms of prior attainment, Black children in aggregate fall behind relative to other ethnic groups in terms of achievement. Black boys tend to have lower levels of achievement in education than Black girls.

Evidence suggests that Black pupils and BME students more generally, are more disadvantaged by inadequate careers advice. This impacts on transitions from school to higher education and the labour market. Girls are also disproportionately affected.25 This has been attributed partially to the fact that BME students rely on official routes of careers guidance more than their white peers.26 It points to the importance of a well-functioning publicly funded system of careers advice, especially for those individuals/groups who lack access to social networks that can help them in education and employment.

Indian and Chinese students perform well Indian and Chinese students remain the most advantaged ethnic groups across educational measures. Ethnic inequalities in education have increased at GCSE and A Level for students from most ethnic backgrounds compared to Chinese and Indian students who have the highest achievement rates. Indian and Chinese workers on average earn more than white workers.

Further education (FE) and apprenticeships

Latest data from the ESFA for the West Midlands 7-met local authority area on new apprenticeship and FE course starts between August and January (2020/2021) suggests that different ethnic groups are well represented by the system, with Black and Asian ethnic minorities both represented at a level greater than their percentage share of the population

in the 2011 Census.27 National level analysis highlights that there are differences by level between ethnic groups, with greater representation of BME individuals on Level 4 and above apprenticeships (16%) than at Levels 2 and 3 (13%).28 The analysis found that apprentices from BME backgrounds accounted

for 20% of those on Level 7 programmes. Apprentices from Asian backgrounds were more likely than people from any other background to be on a Level 7 programme, while apprentices from Black backgrounds were more likely than people from any other background to be on Level 5 programmes.

Ethnicity	WP Pop (2011)	Apprentices	Further education
Asian/ Asian British	14.20	12.1	25.0
Black/ African/ Caribbean/ Black British	4.40	5.6	14.3
Mixed	2.90	4.3	6.6
Unknown	0	2.0	3.8
Other	1.2	1.1	5.2
White	77.5	74.9	45

The slightly greater participation of women in FE is consistent across ethnic groups, suggesting that no specific gender-ethnicity combinations are uniquely overlooked in FE provision.

Ethnicity Group	Female	Male
English/ Welsh/ Scottish/ Northern Irish/ British	51,510	49,510
Pakistani	15,627	13,995
African	9,991	7,677
other white background	7,989	5,650
Indian	5,991	4,993
Caribbean	4,758	3,988
Not provided	4,578	3,782
Bangladeshi	4,338	3,210
White and Black Caribbean	3,850	3,198
any other Asian background	3,042	3,018
Arab	3,252	2,380
Any other ethnic group	2,920	2,499
Any other black/African/Caribbean background	2,465	2,069
Any other mixed/multiple ethnic background	1,893	1,640
White Asian	1,518	1,390
White and Black African	705	719
Irish	426	390
Chinese	359	225
Gypsy or Irish traveller	97	108

More detailed information by gender is provided in the tables below, first for females and then for males. Salient points emerging from the analysis include the relatively large proportion of Bangladeshi females who are on Entry level courses. By contrast, relatively high proportions of White British and Black Caribbean females are on Level 2 courses.

Female

Asian/ Asian British	Entry	1	2	3	4
Any other	45.5	16.7	18.5	9.0	0.5
Bangladeshi	41.7	13.2	16.5	16.8	1.3
Chinese	38.6	13.9	16.7	13.2	0.5
Indian	23.6	15.7	26.3	15.0	0.6
Pakistani	25.3	14.5	25.8	19.9	0.4

Black / African/ Caribbean/ Black British	Entry	1	2	3	4
African	39.3	17.1	22.8	11.8	0.8
other	21.5	19.1	30.0	16.0	1.2
Caribbean	10.1	19.4	34.9	18.6	1.3
Mixed	13.9	17.5	33.1	21.2	0.8
Unknown	27.6	16.7	24.0	15.1	0.6
Other	45.8	15.5	18.6	9.9	0.4

White	Entry	1	2	3	4
Any	30.3	16.3	26.4	12.5	1.6
English/ Welsh/ Scottish/ Northern Irish/ British	8.0	16.0	33.7	20.3	1.0
Gypsy or Irish Traveller	23.6	23.9	26.2	10.0	0.5
Irish	6.4	14.1	33.5	13.4	1.7

Male

Asian/ Asian British	Entry	1	2	3	4
Any	39.4	16.6	23.4	10.8	0.7
Bangladeshi	23.0	15.5	27.0	21.4	2.8
Chinese	28.7	12.9	17.9	23.9	1.3
Indian	16.1	17.2	32.6	18.5	0.8
Pakistani	12.4	17.2	34.5	23.2	0.8

Black/ African/ Caribbean/ Black British	Entry	1	2	3	4
African	30.7	18.1	27.2	13.8	0.9
any	18.9	20.9	32.4	13.7	1.0
Caribbean	11.2	24.6	36.8	12.7	1.1
Mixed	13.6	21.7	34.3	15.6	0.8
Unknown	23.5	15.9	24.2	24.7	1.5
Other	41.2	15.8	22.8	10.8	0.5

White	Entry	1	2	3	4
Any other	23.2	16.8	30.1	13.5	2.4
English/ Welsh/ Scottish/ Northern Irish/ British	10.1	20.8	34.3	14.8	1.0
Gypsy or Irish Traveller	23.7	25.1	32.8	3.6	0.2
Irish	8.8	23.0	33.9	10.8	0.5

Looking at the proportion of different ethnicities who take different courses reveals some modest differences. Most significant are the greater proportion of Black African/ Caribbean and Other Ethnic Group (which includes people of Arab descent as well as unclassified) who take the Preparation for Life and Work course, an introductory course meant to support employability and to enable access to other programmes. This may indicate a lower level of preparation for the workforce for these ethnic groups when coming out of school.

29 Murphy H. and Jones E. (2021) op cit.

BME groups in general are under-represented in Construction, Planning, and the Built Environment, while students of Arab descent are specifically under-represented in Health, Public Services, and Care. National level research29 with providers and employers suggests that the traditional cultural acceptability of certain occupations is a factor here. This is exemplified by one provider delivering digital degree-level apprenticeships reporting that half of their intake was from BME backgrounds, predominantly Asian, while most apprentice on construction sector higher- level apprenticeships were White.

Course	Asian/Asian British	Black/African/ Caribbean/ Black British	Mixed	Other	White
Preparation for Life and work	39.8	43.6	36.5	57.8	35.0
Science and Mathematics	14.7	12.2	12.7	10.7	10.3
Health, Public services and care	8.2	8.9	9.0	4.7	9.0
Languages, Literature and culture	7.0	6.8	7.2	5.6	6.9
Business, Admiration and law	7.7	5.7	5.7	4.2	5.6
Construction, planning, and the built environment	2.5	3.0	5.0	1.8	5.5
Arts, media and publishing	2.4	2.9	4.4	1.8	5.0
Retail and commercial enterprise	2.0	2.3	3.8	1.6	4.9
Information and communication technology	4.7	4.4	3.5	4.9	4.4
Engineering and manufacturing technologies	3.3	3.0	2.9	2.2	3.4
Leisure, travel and tourism	1.3	2.5	3.0	1.1	2.7
Social sciences	3.2	2.4	3.0	1.8	2.6
Agriculture, horticulture, and animal care	0.1	0.2	0.6	0.1	1.2
Education and training	0.8	0.5	0.7	0.5	0.9
History, Philosophy and Theology	1.2	0.7	0.8	0.5	0.8

Looking at FE by level of study, students of Black Caribbean/African or Arab descent are under-represented in Level 3 studies, and over-represented at Entry Level. Students of Asian descent were over-represented at both extremes, being more likely to study at Entry Level or at Level 3 compared to the average. White students were least likely to be studying at Entry Level, and most likely to study at Level 2.

Given that Level 2 qualifications are in higher demand than levels 1 and 3, this indicates some advantage to white students in attaining qualifications more likely to lead to employment. The total sample sizes for Level 4 and 5 (1,915 and 191) are too small to draw any conclusions about the greater representation of white students at these levels. An important caveat however: though there is a clear inequity in terms of level of study, the drop-off at higher levels is not dramatic, making it clear that FE is an important source of social mobility for BME groups, though it could be better in this respect.

Ethnicity	Entry	Level 1	Level 2	Level 3	Level 4	Level 5
Asian/ Asian British	20.8	15.4	34.4	28.5	0.8	0.5
Black/ African/ Caribbean/ Black British	24.4	17.9	34.3	22.4	0.9	0.1
Mixed	13.4	18.1	40.6	27.2	0.8	0.0
Other	41.7	15.7	26.3	15.8	0.5	0.0
White	11.7	16.8	42.5	27.4	1.5	0.2

Higher education

As noted above, while BME groups attend university in large numbers, they are less likely to attend high tariff universities and are less likely to get a first class or upper second class degree than white students. This suggests that success in higher education may be less salient for the job market for some BME groups than for their white peers, even though on aggregate they are attaining tertiary qualifications at higher rates. This is the case even though students from BME groups are more likely to study subjects that normally bring higher earnings returns.30.

Interestingly, Pakistani graduates have the lowest earnings at the age of 30 years of all ethnic groups, at £23,000 for men and £19,000 for women. However, their returns from going to university are the highest of any ethnic group; Pakistani graduates would have earned much less had they not gone to university.

Age and gender

Young people (aged 16-24 years) have the lowest employment rates in 201931. Variations by ethnic group are particularly pronounced in this age group, with the highest rates for the White Other (59%) and White British groups

(58%), and lowest rates for the Other (30%) and Other Asian groups (31%). Employment rates are highest amongst those aged 25-49 years but ethnic differentials in employment rates are least pronounced in the 50-64 years age group (see Figure 5).

A major study of ‘women of colour’ in the job market32 finds that compared with white women and white men, women from BME groups are less likely to be in paid work. At UK level, Pakistani and Bangladeshi women aged 16-64 years had the lowest employment rate in 201933 at 39%, compared with 78% for White Other women, 74% for White British women, 69% for Indian women and 67% for Black women and for women from Mixed ethnic groups, and 73% of Pakistani and Bangladeshi men. The gender gap in employment rates was smallest in the Black ethnic group, with an employment rate of 67% for women and 71% for men in 2019 (see Figure 6).

Figure 5: Employment rates (%) by ethnic group and age group (UK), 2019 (source: Annual Population Survey)

All

16-24 - 54

25-49 - 85

50-64 - 73

All - 76

Asian

16-24 - 34

25-49 - 75

50-64 - 67

All - 65

Indian

16-24 - 38

25-49 - 84

50-64 - 75

All - 76

Pakistani / Bangladeshi

16-24 - 34

25-49 - 65

50-64 - 55

All - 56

Asian other

16-24 - 31

25-49 - 74

50-64 - 70

All - 65

Black

16-24 - 37

25-49 - 78

50-64 - 75

All - 69

Mixed

16-24 - 46

25-49 - 83

50-64 - 68

All - 69

White

16-24 - 58

25-49 - 87

50-64 - 73

All - 78

White British

16-24 - 58

25-49 - 86

50-64 - 73

All - 77

White other

16-24 - 59

25-49 - 88

50-64 - 79

All - 83

Other

16-24 - 30

25-49 - 71

50-64 - 68

All - 63

Figure 6: Employment rates (%) by ethnic group and gender (UK), 2019 (source: Annual Population Survey)

All

Total - 76

Men - 80

Women - 72

Asian

Total - 65

Men - 77

Women - 54

Indian

Total - 76

Men - 82

Women - 69

Pakistani / Bangladeshi

Total - 56

Men - 73

Women - 39

Asian other

Total - 65

Men - 74

Women - 58

Black

Total - 69

Men - 71

Women - 67

Mixed

Total - 69

Men - 71

Women - 67

White

Total - 78

Men - 81

Women - 74

White British

Total - 77

Men - 80

Women - 74

White other

Total - 83

Men - 89

Women - 78

Other

Total - 63

Men - 73

Women - 54

Context

In general, BME groups fare well in terms of educational attainment.

On a variety of indicators BME groups in aggregate fare relatively well in education, but some inequalities and disadvantages compared with the white group persist. Analyses using linked Census data to track outcomes across generations within families indicate that second- generation ethnic minority adults, who were born and brought up in the UK, did much better in the education system than the white majority despite much less advantaged economic backgrounds. This was true, albeit with variations between BME groups, for all the main minority groups2. Overall, nearly 60% of second-generation Indian and Bangladeshi men and around 50% of Indian, Bangladeshi and Caribbean women have tertiary qualifications, compared with under 30% of their white majority comparators.

However, BME students are less likely to attend higher tariff universities3. Analyses indicate that BME students applying to Russell Group universities in 2016 faced significantly lower odds of receiving an offer compared to white applicants4, even after accounting for prior attainment. Once at university, BME students are 13% less likely to get a first or upper second degree than white students5. This has implications for employment outcomes given that in their selection processes some employers specifically seek students with higher classes of degree or from specific institutions.

Although educational achievement is associated with positive labour market outcomes, the educational success of BME groups does not translate fully into success in the labour market.

Pakistani, Bangladeshi and black Caribbean second-generation men and women are all more likely to be highly educated than their white majority counterparts, but they are less likely to be employed6.

BME groups have lower employment rates and higher unemployment and inactivity rates

Employment rates are consistently lower than average, while unemployment rates and economic inactivity rates are consistently higher for BME groups. Figure 1 illustrates the trend over recent years for the West Midlands Combined Authority area.

Employment rates are lower for BME groups than for white groups. The difference in the employment rates for White people and those from all other ethnic groups combined decreased from 16 percentage points in 2004 to 11 percentage points in 2019. The biggest increases were in the combined Pakistani and Bangladeshi ethnic group (from 44% to 56%) and the White Other ethnic group (from 71% to 83%), while the smallest increase was in the White British ethnic group (from 74% to 77%).

For most ethnic groups, employment rates are lower in the West Midlands7 than at UK level (see Figure 2); the exceptions are the Indian and White Other groups where the employment rate is slightly higher in the West Midlands than the UK average.

Inactivity rates remain higher for BME groups than for white groups in the WMCA (7-Met) area, with 22.5% of White UK-born and White UK nationals economically inactive in the year ending June 2019, compared with 28.9% of UK born ethnic minorities and 29% of ethnic minority UK nationals.

Compared with White British men, in aggregate women in BME groups consistently earn less per hour with pay gaps ranging from 10% for Indian women to 28% for Pakistani women. A range of factors contribute to these pay gaps, including location (i.e. a greater likelihood of living in deprived areas);34 unpaid caring responsibilities - 14% of economically inactive White British women do unpaid care in the home compared with 49% of Bangladeshi and 47% of Pakistani women and 25% of Somali women, while Black Caribbean women are particularly likely to use formal childcare services in order to access work; occupation35 and contract-type.36 Analyses suggest that pay penalties for BME women persist when age, occupation and location are controlled for.37

As noted above, pre-existing inequalities between white and BME groups have been accentuated by developments during the Covid-19 pandemic and this is evident for women. During COVID-19, BME mothers had been furloughed at a higher rate (48%) compared to white mothers (34%) and nearly half of BME mothers had lost working hours or their jobs compared to a third of white mothers.38

Employment penalties are also identifiable on ethno-religious grounds. Analyses using Labour Force Survey data with a sample of women aged 19–65 years, shows that most Muslim women face significant penalties compared with their White-British Christian counterparts. The penalties are greater for Pakistani, Bangladeshi and Black Muslim women than for Indian and White-Muslim women.39

Opportunity cost – what is the implication of not having more BME residents in learning and employment?

Key findings highlighted in this review are:

In aggregate, most BME groups perform well in the education system – at all levels. Black boys and the Gypsy and Roma group are exceptions here. Otherwise White British pupils from disadvantaged backgrounds are amongst the poorest performers in the educational system. The generally favourable educational performance of BME groups in aggregate is not necessarily translated into labour market performance. The Indian and Chinese groups stand out as displaying high educational achievement at all stages and strong labour market outcomes.
The evidence suggests that durations of unemployment tend to be longer for BME groups than for the White British group. This suggests that a focus on earlier assistance with job search for BME groups might be appropriate, given the cumulative implications of longer durations spent out of employment for lifetime earnings.
The main opportunity cost for BME residents lies in not being in employment reflective of educational levels. This is the case for many BME groups, but is the case particularly for Pakistani and Bangladeshi groups. As a consequence, BME residents in aggregate are concentrated in low- paid sectors with limited prospects for progression.
Sub-optimal returns to qualifications are evident in under-employment following participation in higher education. BME residents are more likely to be over- qualified for the jobs they work in. This is especially the case for Black African and Bangladeshi graduates.

The ‘Race in the Workplace’ McGregor-Smith review of BME talent and progress at work published in 2017 estimated that having full representation of BME workers in the labour market, through improving both their rates of progression out of low-paid roles and increased access to higher paid and more senior jobs, would benefit the UK economy by £24 billion a year.40

WMCA programmes – what does access look like for these

The WMCA is setting up a Race Equalities Taskforce focusing on improving equality of opportunity – looking at the extent to which different ethnic groups (including White groups) experience different social and economic outcomes and concentrate on developing policy solutions in areas which the WMCA has roles and responsibilities in delivering, enabling and influencing.

It will be important for the Taskforce and wider WMCA to consider how WMCA programmes impact different groups across the region. From an employment and skills perspective BME enrolment onto WMCA programmes is above that of White residents in many cases. For example, in 2020/21

60% of enrollees on AEB courses were from BME backgrounds compared to 35% for those from White backgrounds. However, when looking at progression into employment 54% of BME residents on WMCA programmes employment and skills programmes progressed into employment compared to 44% for white residents. There are nuances that should be considered when reviewing these figures, including the motivation behind learning for individuals/groups and the reasons for not progressing into employment, however, the data should be used by employment and skills leads to inform skills provision and programme leads should work understanding the aspirations of learners that engage with its programmes.

Observations

The positive performance of many (but not all) BME groups in education demonstrates what is achievable through commitment and motivation. The national evidence suggests that particular attention at school needs to be focused on those children from deprived backgrounds. White British children eligible for free school meals are one group deserving of particular attention. Black boys are another group who are underperforming.

The analyses suggest that particular attention needs to focus on helping the more disadvantaged BME groups to access good quality stable employment. This is important given the longer average durations of unemployment suffered by BME residents and how cumulative ethnic disadvantage can become entrenched.

The role of structural barriers in shaping employment opportunities points to a need to focus on systematically identifying and changing aspects of the education and employment infrastructure to address discrimination.

The evidence suggests that BME residents with poor skills suffer the acutest ethnic penalties. Hence, a focus on improving basic skills – and English language skills – remains important. Likewise, it is important to focus on those BME residents facing particular challenges. Women from Pakistani and Bangladeshi groups are characterised by high inactivity rates associated with traditional expectations about staying at home and caring for family members. There is scope to learn from previous Jobcentre Plus initiatives involving provision of community support to provide a social situation where women are enabled to take steps towards employment – including developing skills, as appropriate - and familiarise themselves with the job market.4

There is a role for policy to address deficits in support networks that can help residents access work experience and job opportunities. The evidence suggests that BME residents rely more on formal sources of careers advice than their White British peers. Alongside evidence for hiring discrimination (which is more acute for some sub-groups than for others), this highlights the particular need to ensure that BME residents are able to access timely advice services in order to make positive transitions from education to work.

At a time of labour and skills shortages the time is ripe for employers to explore new/ non-traditional recruitment channels, reconsider their selection practices and redesign jobs on offer (as appropriate) to enhance their quality and attractiveness. This provides a step change in opportunities to challenge hiring discrimination and reduce ethnic penalties in employment. Bringing the voice and experience of BME residents to employers facing skills shortages and to other stakeholders designing employment and training policies would be helpful here.

The employment and skills team should consider how this, and other analysis can inform skills provision for specific groups and how intelligence on the aspirations of leaners can be gathered to support provision and procurement decisions.

Furlough Briefing

This briefing note presents some potential implications for the West Midlands because of the Coronavirus Job Retention (furlough) scheme ending on 30 September 2021. Analysis from think tanks such as the Resolution Foundation, Institute for Fiscal Studies and the New Economics Foundation is used to provide a framework for considering the impact of the scheme ending. Local furlough data analysis produced by the Office for Data Analytics and Economic Intelligence Unit and other labour market intelligence is then used to supplement this and outline potential issues for the region.

Key Headlines

The National Institute for Economic and Social Research recently estimated that there could be a 0.06 percentage point rise in the national unemployment figures by the end of the year. If this same proportionate increase is applied to the unemployment figures in the region the increase would be close to 9,000 extra people being unemployed after September.
It is estimated that 10% of the region’s employees are on low pay and when applied to furlough figures this amounts to 16,000 workers in the region who could struggle if their employment conditions change (i.e. reduced hours) following the ending of the furlough scheme.
Birmingham is the main outlier in the region for furlough, with 34,400 people accessing support, this is at least three times higher than any other local authority area in the region and is more than both the BCLEP and CWLEP areas.
Research by the International Monetary Fund suggests that there could be up to a 50% change in people’s social mobility (voluntary social distancing) because of a rise in COVID-19 cases. This voluntary social distancing is perhaps linked to a 2% fall in retail footfall seen across the UK in August 2021.
There are 4,770 furloughed employees in the arts, entertainment and recreation in the region which is 6% of the total number of people employed in the sector – lower than the 15% furloughed workers in the sector nationally.

In considering the national context and the proxies above it is important to note that Birmingham is the main outlier in the region, with 34,400 people on furlough, this is at least three times higher than any other local authority area and is more than both the BCLEP and CWLEP areas.

Local Authority Area	Total Employment Furloughed	Total Eligible Employment	Take-up rate (%)
Birmingham	34,400	413,000	8.3
Bromsgrove	2,800	42,800	6.5
Cannock Chase	2,800	44,400	6.3
Coventry	9,200	144,800	6.4
Dudley	8,500	131,800	6.4
East Staffordshire	3,300	55,300	6.0
Lichfield	3,100	45,300	6.8
North Warwickshire	2,000	29,200	6.8
Nuneaton and Bedworth	3,600	60,000	6.0
Redditch	3,100	39,800	7.8
Rugby	3,000	53,800	5.6
Sandwell	9,700	132,900	7.3
Solihull	7,400	91,800	8.1
Stratford on Avon	4,000	58,100	6.9
Tamworth	2,200	36,100	6.1
Walsall	8,000	110,600	7.2
Warwick	4,100	66,000	6.2
Wolverhampton	6,900	107,000	6.4
Wyre Forest	2,500	41,100	6.1
WM 7 Met	84,000	1,132,000	7.4
Black Country LEP	33,100	482,300	6.9
Coventry and Warwickshire LEP	25,900	411,900	6.3
Greater Birmingham and Solihull LEP	61,600	809,600	7.6
WMCA	120,600	1,703,800	7.1
WEST MIDLANDS REGION	159,400	2,424,300	6.6
UK	1,857,400	28,692,200	6.5

Scenarios

The New Economics Foundation think tank has produced a briefing note which outlines a number of scenarios that could occur following the ending of the furlough scheme. This note considers some of these scenarios and uses regional data to supplement the analysis.

Scenarios 1: Cautious public attitudes to social mixing and mobility

New Economics cites a number of sources which have modelled scenarios where the public continue to persist with social distancing measures after the July lifting of lockdown and ending of the furlough scheme in September. Research by the International Monetary Fund suggests that up to a 50% change in people’s social mobility (voluntary social distancing) is down to a rise in COVID-19 cases. Furthermore, the latest Weekly Monitory produced by the Office for Data Analytics shows that, in the week to 21 August 2021, the volume of overall retail footfall in the UK decreased by 2% from the previous week (week to 14 August 2021). This is the first week to see a decrease in footfall across the UK since the week to 19 June 2021. In the latest week, footfall in high streets saw the greatest decrease of 2%, while footfall in retail parks and shopping centres both saw slight decreases of 1%, compared with the previous week.

New Economics notes the London School of Hygiene and Tropical Medicine scenario which suggests that mobility in retail and recreation (i.e., travel to places such as restaurants, shopping centres, museums, and cinemas) and public transport sectors will only reach 95% and 80% of pre-pandemic levels respectively following unlocking on 19 July. ONS data suggests that, between 14 and 27 June, 15% of the arts, entertainment and recreation workforce, and 11% of the transportation workforce, were still furloughed. Regional analysis by the Economic Intelligence Unit provides a breakdown of furlough numbers by sector. The data shows that 4,770 furloughed employees in the arts, entertainment and recreation which is 6% of the total number of people employed in the sector – lower than the 15% furloughed employees nationally. However, 7% (17,650) of employees in the wholesale and retail trade sector are still furloughed in the region.

Broad Sector	Furloughed 30 June 2021
Manufacturing	21,000
Construction	6,730
Wholesale and retail, repair of motor vehicles	17,650
Transportation and storage	8,410
Accommodation and food services	19,530
Information and communication, financial and insurance and real estate	6,260
Professional, scientific and technical	9,250
Administrative and support services	11,860
Education	3,450
Health and social work	4,840
Arts, entertainment and recreation	4,770
Other service activities	5,180
Other	1,550
Total	120,600

Total employments furloughed by broad sector for the WMCA (3 LEP) as of 30th June

Further work is needed to understand the regional picture, however these estimates have potential implications for some sectors, especially those that operate in our towns and centres.

Scenarios 2: Factors outside the pandemic

New Economics also considers potential non- pandemic-related economic issues that are also appearing to be driving demand for the furlough scheme. The think tank states that furlough rates in manufacturing and states that these no longer appear to be linked to the level of public health restrictions, with the rate actually increasing slightly in June to around 4.8% of the workforce. The automotive sector provides the strongest example, as it has seen rates rose from around 7% during the January 2021 peak in virus cases, to 21% in late June. It is said that these changes appear to be linked to international trade issues – notably a global semiconductor shortage and potentially also to Brexit-related impacts. In the region, as of 30 June 2021 21,000 (10%) of the region’s manufacturing employees are still on furlough.

Scenarios 3: Jobs at risk after September Modelling by the New Economics Foundation

estimates that 830,000 jobs could be at risk nationally following the ending of the furlough scheme. There is said to be significant variations by sector, although this analysis isn’t provided. In considering the risk in the region HR1 notifications can be used help understand the outlook. In the latest analysis for July 2021, the data shows that there are variations in the number of HR1 notifications seen per sector. For example:

The ICT sector had the most notifications in the BCLEP area in July 2021, whilst CWLEP and GBSLEP saw no ICT notifications.
Admin and support services accounted for the most notifications across CWLEP in July 2021. The numbers were significantly lower in this sector across the GBSLEP and BCLEP areas.
Manufacturing had the most notifications by sector across GBSLEP in July 2021, the figure was double the amount seen in the BCLEP and CWLEP areas combined.

Longer term analysis is needed to support a fuller picture and information from businesses organisations would also be helpful in highlighting any sectoral risks post September.

Observations

The ending of furlough comes at challenging time for the region, given the challenging position around unemployment, question over why people are not accessing the over 100,000 plus vacancies available.
It should be noted that not all the risk around redundancies and job losses can be attributed to the pandemic. For example, there are other structural issues in the manufacturing sector at play as noted.

Women in Learning

This paper is produced by Professor Anne Green at WMREDI42 for the WMCA’s Skills Advisory Panel and Jobs and Skills Delivery Board. It should be used inform thinking around education, skills and employment policy and programmes in the region, specifically the adult education budget and inform the update of the 2021/22 Local Skills Report.

This paper focuses on women in learning and explores the barriers women face to accessing and participating in learning (especially for women in low paid and insecure employment), the implications for women post learning, sectoral issues (with a particular focus on STEM) and the implications of not having more women in learning. It concludes by presenting implications for stakeholders in the West Midlands.

Headlines

Women perform well in the education system, so issues concerning women’s position and performance in the labour market are not just about encouraging an increase in learning.
A focus on the supply-side is insufficient – a demand-side policy of promoting good quality jobs, open to flexible working is important if women are to have enhance opportunities to participate in learning and utilise their skills in employment. The availability of affordable transport is also important to enable women to participate in many learning and employment opportunities.
There is an ongoing issue about subject choices and the relatively low uptake of maths-based STEM subjects by women. Given developments in the economy and the growing importance of the digital economy, this is an issue meriting continuing emphasis – but attitudes to STEM are evident prior to the post-16 learning stage. Emphasis on ensuring that women have a stake in the digital economy is important for the West Midlands (as elsewhere). Role models and mentors demonstrating what can be achieved, and how, are also important in encouraging women’s participation in STEM learning and jobs.
Women caring for children (especially lone mothers with small children) face particular barriers to participation in learning. Structural issues - such as available and affordable childcare - are of fundamental importance for participation in learning and take up of employment utilising that learning.

Outlook for women

The barriers women face to accessing/ participating in learning – particularly women in work in low paid and insecure employment Access to training is highly unequal - between and within businesses – with low paid, low qualified workers less likely to have opportunities to develop their skills, so widening inequalities and skill gaps.43 The Covid-19 pandemic has exacerbated divides in access to and uptake of learning and training. This is evident at school level where children from poorer families have been particularly hard hit by disruption to schooling, with potential long-term effects on their educational progression and labour market performance.44 This underlines the importance of long-term investment in schools to help catch up for lost learning, as well as assistance for recent education leavers who were particularly negatively impacted by the Covid-19 pandemic.

The 2020 Adult Participation in Learning Survey45 found that during the 2020 lockdown those who would most benefit from training, were the least likely to participate. For example, one 20% of adults who left school at the first opportunity took part in lockdown learning, compared to 57% of adults who stayed in education until the age of 21 years. Only 29% of adults in lower socio-economic groups took part in lockdown learning compared to 57% of adults in higher socio- economic groups 34% of adults who were out of work took part in lockdown learning, compared to over 52% of those who were in employment.

Sector and establishment size matters – analysis of Employer Skills Survey data for 2019 (and previous years) shows that employers in lower wage, lower productivity sectors (e.g. retail and hospitality) are less likely to provide training and investment in training has fallen most in these sectors; (these are important sectors for women and for young people).46 Higher value, more knowledge- intensive sectors have increased investment in training. Employer investment in training fell sharply during the pandemic, but employer investment in skills was declining before this; in the 2019 Employer Skills Survey 61% of establishments reported providing training over the last 12 months, in comparison with 66% in 2017. Training days and training expenditure per employee has declined consistently since 2015. Nearly three-quarters of employers who do not offer training do not see the need to do so. This underscores the fact that a ‘low skills equilibrium’ (sometimes called ‘low skills traps’) exists in some local areas and some low-skill sectors (such as hospitality and retail). Here the potential for supply-side action on skills is limited as there is not impetus to invest in skills.47

Progression in work – and learning for progression – needs to be viewed in a broader context of prior experience of education, family lives and financial circumstances (i.e. ‘context’ is important). Progression in work is often measured on the basis of increases in earnings, although non-monetary definitions such as the number of hours worked, job titles, job security, skills levels and career development are relevant too. Whatever measure is used, a key point is that some women prioritise other issues over work and training. For example, some parents – and single parents in particular (albeit they are not a homogenous group) – may prioritise the needs of their children.48 In general, single parents face particular issues in relation to participation in learning and the labour market; they are more likely to be low- paid and less likely to progress out of low-paid work than other working parents. Barriers limited their progression in work include lack of flexibility, part-time working, level of education, and time out of the labour market. More generally, working part-time is associated with poorer progression outcomes than working on a full-time basis – in part due to a lack of quality part-time jobs. Evidence suggests that parents with the youngest children are amongst the least likely to access learning. They may also struggle to retain engagement in learning once they commence.

The level of education is an important factor for women (and men) for progression in work. There is consistent evidence that progression is harder to achieve with lower levels of education. This underlines the importance of educational levels achieved during compulsory education and in subsequent further and higher education.

While individual experience and attainment in education matters, once in work managerial decisions matter too. Managerial decisions regarding training of different groups of workers can contribute to reproduction of traditional gender divides in training - so disadvantaging women. Analysis of data from the European Sustainable Workforce Survey (including the UK), drawing on data from over 228 organisations and focusing on older workers, shows that in comparison with men of the same age, older women on average work for organisations that are more likely to offer training for their staff (the public sector) but within these organisations they tend to have jobs that make them less likely to receive training.49 This underscores the point that workplace dynamics and managerial decisions are important in understanding gender divides in access to training.

The implications for women post learning

A review of the literature suggests that the gender pay gap is multi-dimensional and complex in its causes and consequences.50 The gender pay gap is relatively small for young and single people, and increases with age, marriage and parenthood. Disparities in pay and progression accumulate to affect women’s income not only in work but also over their life course.

The gender pay gap is also associated with the types of occupations (often low-paid) in which women are traditionally concentrated – e.g. care, catering, cleaning, etc. It is also noteworthy that private sector gender differentials in pay are higher than in the public sector.

In general, qualifications have a particular significance for ethnic minority women (and men) in the labour market; with those with no qualifications and non-UK qualifications being the most disadvantaged. However, there are variations in experience between ethnic minority groups – in part reflecting sectoral (and associated occupational) differences in employment between ethnic groups.

There is an apprenticeship pay gap that negatively impacts on women. Apprenticeships offer paid employment, on-the-job training and a qualification. National and regional data indicate that women are well represented in apprenticeships.51 The latest figures (covering the period from August 2020 through to April 2021) on apprenticeship completions in the West Midlands metropolitan area show a larger number of women completing apprenticeships overall. This is the case across all levels of apprenticeship study, with a greater number of women having taken higher apprenticeships, though with higher apprenticeships comprising a slightly smaller share of the total when compared to men (see the Table below).

Gender	Female	Male
Advanced Apprenticeship	2,990	2,110
Higher Apprenticeship	2,160	1,680
Intermediate Apprenticeship	1,690	1,330
Proportion higher	46%	49%
Total	6890	5120

The gap in apprenticeship completions (also original uptake) between women and men differs markedly between ethnic groups. The gap is slightly narrower for White people (as shown in the Table below).

Ethnicity	Female	Male	Difference (%)
Asian/ Asian British	1040	790	24
Black/ African/ Caribbean/ Black British	500	280	44
Mixed	310	110	65
Unknown	150	140	7
Other	30	30	0
White	4960	3910	21
Total	6990	5260	25

When breaking down apprenticeships by subject, the gender gap is striking. Women make up a negligible share of construction, engineering, and manufacturing apprenticeships, and are a distinct minority in ICT. They are, however, greatly over - represented in education and health and care, the latter sector being particularly important because it takes such a large absolute number of apprentices. Women also comprise a larger share in business, administration, and law (see Table below).

Row	Female	Male	Difference (%)
Construction, Planning and the built environment	30	620	1967
Engineering and manufacturing technologies	100	1230	1130
Leisure, travel and tourism	10	40	300
Information and communication technology	140	340	143
Arts, media and publishing	30	40	33
Agriculture, horticulture and animal care	50	60	20
retail and commercial enterprise	600	420	30
Business, Administration and Law	2440	1690	31
Education and Training	270	80	70
Health, public services and care	3320	740	78

These differences are significant given the different earnings prospects they confer, and because they reinforce structural differences in the workforce in which occupations and sectors which employ more women often pay less.

Women in senior positions experience a gap in their pay compared with men. This may be a function of bias resulting from skills and attributes traditionally associated with men (for example, risk-taking) being recognised in recruitment and reward structures, while characteristics associated with women (such as communication skills and team-working) may be less well recognised. So, this highlights the importance of behaviours and cultural norms impacting on gender differences in experience.

Sectoral implications – with a particular focus on STEM
Women perform well in the education system – at primary, secondary and higher education levels.52 This favourable educational performance. But the qualifications that they achieve are not necessarily translated into labour market performance. In part this relates to employment choices that women make relative to men and also to confidence in their own abilities in technical and STEM subjects. It also reflects cultural expectations and gender stereotypes that are evident from a young age.

Men routinely self-report more skills than women in LinkedIn.53 This correlates with women’s lower confidence in their own technical abilities. There is evidence that even very high achieving women are held back by shortcomings in their confidence/ self- efficacy in particular subjects. This is already evident at secondary school age, and suggests that tackling ‘attitudes’ to STEM subjects is important. There is a large gender gap in the likelihood of taking maths and physics at A-level, even among high-achieving pupils.54 Girls perform as well as boys in STEM subjects at GCSE level, and more girls than boys rate science as a favourite subject. However, far fewer girls and women study STEM at further education (FE) and HE level.55

The number of women taking STEM based subject has increased and in some STEM subjects (e.g. biological sciences, life sciences) women outnumber men.56 But this has not been the case in maths-intensive science fields – including subjects such as computer science (and also AI and data science which are becoming increasingly important) and engineering which remain male dominated. Within STEM fields of work there are also differences in the types of jobs where women are concentrated (e.g. in data science and

AI women are more likely to occupy a job associated with less status and pay – usually within analytics, data preparation and exploration, than in more prestigious jobs in engineering and machine learning).57

There is a good deal of up-to-date evidence on gender equality in STEM and what works at different educational stages to include engagement in a range of STEM-based activities; increase confidence and motivation to pursue STEM study; creating gender inclusive learning environments; providing positive role models/ mentors; targeting parents as key influencers.58 At post 16 and higher education levels project-based, authentic subject-specific activities can positively impact engagement of women in STEM, as can using positive media portrayals of women in STEM and implicit affirmation training can contribute to improving women’s perceptions of STEM careers. The support of a female mentor improves retention of women in STEM subjects. Peer-led and small group working have proved successful, as have teams composed of only female members. Using an online virtual learning environment can encourage women’s participation in STEM learning and improve diversity on courses.

It is clear that the gender gap in tertiary education in maths-based STEM subjects results partly from factors that are evident before that time and which influence ‘educational preparedness’. This encompasses general educational achievement, achievement in mathematical subjects (which are important for many STEM programmes), comparative advantage in subjects requiring mathematical versus literacy proficiency and course-taking in upper secondary influencing field of study and/ or ‘educational preparedness’ for subsequent choices. As these factors work cumulatively and in combination, there is no single factor that can be recommended to change these patterns in a substantial way.

The implications of not having more women in learning

It has been noted above that women perform well in the education system – at primary, secondary, further and higher education levels. A key issue is that favourable educational performance is not necessarily translated into labour market performance. In part this relates to employment choices. But training has a role to play too. A study investigating the role of training over the lifecycle for women (using longitudinal panel data) finds that training can play an important role in reducing the wage gap that arises from part-time work post children, especially for women who left education after secondary school.59

The choices currently made by some women – in terms of participation in learning and about employment – results in an under-utilisation of skills in the economy, which in turn results in a loss of productivity. Some of the choices made by women relate to differential opportunities (and constraints) for flexible working across sectors and occupations, and here a salient point is that there is growing demand for flexible working across genders and age groups.60 Under-utilisation of skills also results in a shortfall of spending in the economy (as a result of women being on lower wages than if their skills were fully utilised). At an individual level it means greater insecurity and unfulfilled potential for some women themselves.

There are also implications for families, including on children. Educational achievement across different groups is complex, and different social, economic and cultural factors contribute to this: parental income levels, parental career and educational achievement, geography, family structure, and attitudes towards education within the family and wider community.

Evidence on early years points to family and parental background, geography and poverty having a strong influence on outcomes. It highlights the contribution of parents to supporting a child’s learning is significant and a stable home provides a supportive context for children to complete homework, ask for assistance and develop their confidence and wellbeing. This can be provided by different sorts of family units but of key importance is the need for stability and resilience.62

Among the core dimensions of socio-economic status, maternal education is the most strongly associated with children’s cognitive development, and is a key predictor of other resources within the family that strongly predict children’s well-being. Existing research demonstrates a strong relationship between maternal education and family circumstances, and between family circumstances and children’s development, establishing the importance of both material resources and family relationships for children.62

WMCA interventions

WMCA adult learning interventions shows that female uptake is higher than male for three of the four interventions listed in the table below. The biggest gap between male and female uptake is for community learning, where female uptake is three times that for males at 76%. There is also a notable difference for AEB enrolment at 63% and 37%, whilst Level enrolment is similar at 49% and 51%. Male enrolment onto AEB stood at 64% compared to 36% for females.

WMCA Interventions	Female	Male
Community learning enrolment(19/20)	76	24
SWAPs Enrolment (20/21)	36	64
Level 3 enrolment (20/21)	49	51
AEB Enrolment (19/20)	63	37

Observations

Gender-inclusive labour market policies need to be implemented and enforced across sectors. One option for consideration here is to develop ambitious gender-specific targets for increasing learning/ training and good quality job outcomes (in addition to aggregate targets). More nuanced targets could be relevant for specific sectoral initiatives (such as Sector-Based Work Academies) given the current patterns of sectoral and occupational segregation.
Paying attention to gender-inclusivity is particularly important with regard to women’s ability to participate in the digital economy on the same footing as men, as well as being important for accessing training and retraining more generally. The role of Digital Bootcamps is particularly important here – given the flexibility of provision that they offer. The specific targeting of women63 (and other under- represented groups in tech sectors) as part of this provision is in line with the wider evidence base on ‘what works’ in terms of opening up opportunities for women in STEM. It is important to continue to monitor outcomes from this initiative and to evaluate what elements of provision are attractive to whom and why. In order to gain insights into gaps in current provision, it would be informative to analyse how participants found out about Digital Bootcamp opportunities and (if possible) to collate and analyse information on those individuals registering initial interest who subsequently did not meet eligibility criteria for free provision or who otherwise decided not to undertake training.
On employment support and training programmes the evidence points to the importance of women role models in demonstrating what might and can be achieved. Evidence from the Connecting Communities initiative emphasises the role of coaches/ mentors in listening to participants and understanding the specific demands of different training schemes/job roles in the context of wider challenges particular sub-groups of women face in participating in learning and employment.
There is a need to work to influence the demand-side of the labour market – for instance, by increasing the number of ‘good jobs’ offering flexible and part-time work in order that individuals have greater opportunities to utilise their skills. Local employment charters, promoted by local/ regional stakeholders, can play a role here in badging employers who have made a commitment (or are committed to working towards) to improving characteristics of good employment,64 so making them more identifiable as ‘employers of choice’. There is also scope here for working with organisations such as Timewise/ Women Like Us who have a track record of experience on such issues. (However, it should be noted that the demand for flexible working is not gender specific – it is influenced by work/ life balance, commuting issues, leisure or study interests, etc. – and has become more prominent in debates about hybrid working.)
Job redesign is an important factor – it has the potential to open up possibilities for progression. In the context of growing labour and skills shortages there is an opportunity for regional stakeholders to work with employers (particularly in sectors facing most recruitment [and retention] challenges to consider how jobs might be redesigned and/or current recruitment/ employment practices revised (especially in sectors where employment is heavily dominated by one gender) to attract a wider base of applicants than they typically consider. Once in the workplace managers’ decisions are important in opening up opportunities for training and progression. Enhanced managerial awareness of perceptual and objective barriers women face in putting themselves forward for such opportunities would be beneficial here.

There is scope for greater attention to be paid to making more information available about the transferability of skills across sectors and also about progression routes in different sectors. Linked to this is the importance of targeted and timely careers support, recognising that aspirations regarding progression evolve over time. For mothers such aspirations are often related to children’s ages and school stages, with many mothers being most open to learning/ progression when the youngest child starts primary school/ secondary school.
For many parents cheaper and more accessible childcare would open up

opportunities to engage in a wide range of learning/ training and employment. For some help with transport costs might also help overcome barriers to participation. Polices to subsidise training for recent mothers can increase their disposable income and overall welfare – albeit policies that lessen incidence of part-time work (e.g. improved childcare availability) may be more important.

Cultural expectations and gender stereotypes are pervasive and being at a young age. In STEM in post 16 and higher education evidence points to the importance of the following points related to delivery of training:

Project-based, authentic subject-specific activities can positively impact female engagement in STEM;
Using positive media portrayals of females in STEM and implicit affirmation training can contribute to improving females’ perceptions of these types of careers and developing ‘a sense of belonging’;
The support of a female mentor improves retention rates of women in STEM subjects;
Using an online/ virtual learning environment to deliver STEM subject content can encourage more female participation and increase diversity on courses;
Peer-led tutoring and small group working have proved successful;
Short/ limited interventions can positively impact women’s engagement in STEM; Single-sex STEM programmes have the potential to increase females’ sense of belonging.
In strategically important sectors for the future proactive steps are needed to ensure the inclusion of women in design and development of key technologies. (This would be a UK government requirement to scrutinise and disclose the gender composition of R&D teams rather than an action to be taken regionally).

Over 50s in the Labour Market

This paper is produced by Professor Anne Green at WMREDI for the WMCA’s Skills Advisory Panel and Jobs and Skills Delivery Board. It should be used inform thinking around education, skills and employment policy and programmes in the region, specifically the adult education budget and inform the update of the 2021/22 Local Skills Report.

This paper focuses on people aged 50 years and over and their labour market experience. It explores broad features of demographic change – notably the ageing of the population - and implications for individuals’ working lives. It summarises the age profile of sectors and occupations and associated implications for worker retention and future labour supply. It identifies barriers facing people aged 50 years and over in learning and employment, and the challenges that they face if they lose their job in searching for new employment. It addresses the implications of not having more people aged 50 years and over in learning

and employment, both for the individuals themselves and for the broader economy and society. It presents insights into the role of policy to help over 50s in the labour market, including the role for place-based policy.

It concludes by presenting implications for stakeholders in the West Midlands.

Headlines

There is a general trend of population ageing which has contributed to a growth in people aged 50 years and over in employment, albeit this trend is less marked in the West Midlands CA area than nationally. In the next ten years all of the large post World War II ‘baby boomer’ generation will have reached State Pension Age.
Until the Covid-19 crisis there was a trend towards increasing labour market participation of the over 50s – particularly amongst people in their 60s. However, the long-term trend towards more years of healthy life in retirement is now stalling and there are marked inequalities in the economic and health experience of the over 50s; the over 50s are a heterogeneous group with some much more able to support themselves in retirement than others. Nationally, the Covid-19 pandemic has had a disproportionate impact on older (as well as younger) workers, with a particularly marked reduction in the employment rates of older women, but this is less evident in the West Midlands than nationally. Formerly their employment rates had been increasing, alongside the rise in the State Pension Age for women amongst the younger member of the ‘baby boomer’ cohort.
Sectors such as Education and Manufacturing have a ‘top heavy’ age structure. This highlights the importance of replacement demand considerations as workers retire. Some occupations in the construction sector and HGV drivers, as well as skilled trades, secretarial & administrative and elementary occupations also have a disproportionate share of older workers, accentuated by a reduction in migrant worker in-flows, given that migrant workers tended to reduce the average age of these occupations.
The over 50s face a range of barriers in accessing learning and employment. Employers are less likely to train older than younger workers. If older workers are made redundant or become unemployed they are likely to take longer to find employment than younger age groups. A lack of up-to- date skills and qualifications hampers some (but by no means all) older job seekers.
The over 50s are also more likely to lack experience of recent job moves and job search. A substantial proportion of the over 50s perceive age discrimination as a factor in their difficulties in finding employment. Evidence suggests that flexible working practices and part-time employment play a key role in keeping older workers in the workforce for longer. Moves towards enhanced emphasis on the quality of jobs are likely to benefit all labour market sub- groups, and the economy as a whole, at a time of labour and skills shortages.
Evidence suggests that the over 50s benefit from targeted employment support initiatives addressing the distinctive challenges that they face; they tend to be less well catered for by generic programmes. There is scope for personalised, segmented and place-based support, delivered by advisers of a similar age.

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West Midlands Local Skills Report Supporting Evidence 2022