Apprenticeships: how to address gender and ethnicity pay gaps
10 Jan 2019
Becci Newton, Deputy Director, Employment Policy Research
A recent report that nearly all employers responding to a CBI survey are taking action to address gender pay gaps has got me thinking. Here at IES, we do a lot of employer-facing work to support organisations to improve their pay and reward systems and address pay gaps – whether by gender, ethnicity, disability or other characteristics. At the same time, in what we call our ‘policy research’, we focus on how public policy can make the labour market and employment work better.
Often, public policy and employer practice operate in different worlds and talk different languages. But with gender and ethnicity pay rising up the agenda, is there more that we can do to bring these two worlds together?
Where our employer research and consultancy focuses on the picture within organisations and industries once people have gained employment and aims to ensure people attract fair and equal pay for the work they do, our policy research enables us to take a step back and look across the piece at, for example, what is happening in earlier stages of the transition between education and work and how this affects later pay rates.
In the field of pay disparities, our policy research has focused strongly on occupational and gender stereotypes – which take root from a very early age and are a strong determinant of later labour market experiences; differential uptake of STEM subjects by gender; and differential uptake of accredited training such as apprenticeships.
For example, just over five years ago we completed a study for Unionlearn to unpick under-representation in apprenticeships by gender and race. This led to an early intersectional analysis on the take-up of apprenticeships showing not only the differences by each demographic factor, but also the additive effect between factors.
While overall participation in apprenticeships back then was balanced between genders, marked differences in terms of gender composition could be seen across sector subject areas. The general picture of gender segregation we found held true across ethnic groups – occupational segregation by gender cuts across ethnic and cultural identities. We were also able to explore differences in apprenticeship pay using the Apprenticeship Pay Survey data (2011) – although due to survey size, only by gender and race. Here, there was less evidence of intersectional effects and instead age and gender predominated pay outcomes. The finding on age was unsurprising as apprenticeships operate to minimum wage rates that are set by age as elsewhere in our economy. However, without taking a closer look, age-related findings could obscure gender and ethnicity outcomes.
On gender, while female apprentices received a higher rate of pay on average than male apprentices, once age was accounted for (as older apprentices typically receive better pay and more female apprentices are older), within each age band, female apprentices were, on average, paid less than male apprentices. Occupational segregation complicated this further, with women over-represented in hairdressing, which had the lowest pay levels, and in team leadership and management, which had the highest average pay; while men were over-represented in construction apprenticeships which had below-average pay.
On ethnicity, while ethnic minority apprentices earned more on average than white apprentices, as with the figures for women, ethnic minority apprentices also tended to be older (so the pay differences were more a reflection of the age profile).
Looking at the most recent (2016) apprenticeship pay survey data, a few points can be discerned even without analysing them in the same detail. Taking into account intermediate and advanced apprenticeships to match our earlier analysis:
The picture remains skewed by age: female apprentices are still older on average than male apprentices: 47 per cent of female apprentices are under 25 compared to 70 per cent of male apprentices. Ethnic minority apprentices also remain older than white apprentices: 44 per cent of ethnic minority apprentices are aged under-25 compared to 60 per cent of white British apprentices.
The median pay of female apprentices remains higher than that of males, but their mean pay is lower, suggesting the age/pay effect persists.
Occupational pay segregation unsurprisingly continues within apprenticeships, following established industry patterns. Hairdressing (a sector in which women still predominate) continues to have the lowest pay – the 2016 median hourly pay at £3.47 is woefully low even compared to other female-dominated sectors such as childcare (£5.04). Potentially better news is available for women if the trend in their representation in management training continues, as this attracts some of the best apprenticeship pay (£8.75 per hour; median). However, the published statistics do not allow us to explore take-up of sector subject areas by gender (or other demographic factors).
For traditionally male-stereotyped occupations, hourly median apprenticeship pay sits mid table – construction attracts £6.00; engineering/manufacturing £6.44 and electrotechnical £6.52. To put this in context, these are not bad pay rates given the likelihood of male apprentices being younger, on average, than female apprentices.
Beyond the gender stereotypes, what is perhaps most surprising is that service sectors are attracting relatively high hourly median apprenticeship pay: hospitality and catering £6.78; retail £6.95; health, social care and sport £7.19 and customer service £7.21. But, the headline figures may be hiding underlying trends.
So what does this all mean for future efforts – by public policy and employers – to address pay disparities and close gaps?
First and most obviously, there is still much more that we can do to address occupational segregation in education, training and apprenticeships. We would argue that apprenticeships data show clearly how people’s career paths and pay prospects are determined long before the point that they’re established in work.
Our work in 2004 for the then EOC (now EHRC) following the launch of its General Formal Investigation demonstrates this precisely and little has changed.
Young people’s career choices pre-16 focus on those occupations where their gender is more prevalent, influenced by family and school factors. This matters because attitudes and interest have a stronger influence on job choice than ability. At this age, young people are less informed by labour market ‘facts’ such as the different levels of pay for different jobs. As a result, their early occupational stereotypes inform their choices for GCSEs which, in turn, serve to narrow education, training and occupation options post-16 at a time when their careers ideas may be more open to change.
There is a huge focus on employer engagement in education and careers provision although no requirement to cover pay; it is time to stop being British and start talking about the pay and other rewards that different jobs attract.
Secondly, as our Director Tony Wilson has pointed out in relation to gender pay, in some cases positive action by employers could serve to widen gender pay gaps in the short term – if, for example, they increase recruitment of women into entry-level apprenticeship or training programmes, which tend to have lower than average pay.
So, policymakers and employers need to get below the simple headline measures of pay, and look at pay gaps for different groups of employees (as many of the best ‘narrative’ gender pay gap reports by employers do) and at participation in work and career stage alongside pay. This will become even more important with ethnicity and disability reporting – where our research suggests the success of gender pay reporting may not be as easily replicated as different issues are raised.
Finally, we need to look beyond single measures of inequality and see how different factors can interact.
This can’t just be a numbers exercise. As the gender pay debate increasingly focuses on employer deeds rather than words, it will be more important than ever to build evidence and share learning – both in public policy and employer practice – on how we can narrow gaps and address inequalities in the labour market.
Any views expressed are those of the author and not necessarily those of the Institute as a whole.