UK graduate labour update: 10 June

This week's update from Charlie Ball includes a series of new labour market dashboards, research into skills needs and levelling up, and a look at whether technological change creates or replaces jobs

I'm back from holiday, so welcome to our regular graduate labour market updates from the UK and Ireland, brought to you by Prospects Luminate and Data Analytics at Jisc.

The latest rapid indicators of economic and social change are now available from the Office for National Statistics (ONS):

• Potential redundancies increased to 67% of their pre-coronavirus level in the week to 29 May 2022, while the number of employers proposing redundancies was 83% of this.
• Small business sales decreased by 9% in April 2022 compared with March 2022 and were 120% of the level seen in April 2019. All sectors experienced negative sales growth in the latest month.
• Small business jobs fell 4% in April 2022 when compared with the previous month and were 90% of the level seen in 2019. All sectors experienced negative job growth except 'Other services', which increased by 1% compared with March 2022.
• Total online job adverts increased by 7% in the last week of May, to 142% of their February 2020 average level. Vacancies for new graduates went up the most (partly through timing) - it's large businesses driving a lot of recruitment.
• 12% of businesses reported that they were experiencing a shortage of workers in late May, remaining flat for several months. 25% expect to recruit this month, 39% experienced difficulties recruiting in April.

The ONS have also published an analysis of young people in the labour market from 2014 to 2021:

Young people (those aged 16 to 24 years) have a lower participation in the labour market than older people. Their economic inactivity rates have been increasing since the early 1990s, in comparison with other age groups. This has been mainly because student numbers have been increasing steadily in the last three decades.

Young people are also less resilient to shocks in the labour market than older people. They suffered disproportionately during the 2008 to 2009 global financial downturn, with unemployment rates rising fast and reaching the highest levels on record at the end of 2011. They were also among the most affected by the impact of the pandemic on the labour market, with unemployment and economic inactivity rates increasing by more in comparison with those aged 25 years and over.

• There has been a steady increase in the percentage of young people (aged 16 to 24 years) from the highest socio-economic background between 2014 and 2021.
• Young people (excluding full-time students, who make up 43% of the whole cohort) who had lived in a workless household (at age 14 years) were less likely to be in employment and more likely to be economically inactive than young people from every other socio-economic background.
• This has remained generally consistent across time and sex since data on socio-economic background began to be collected in the Labour Force Survey (LFS), in 2014.
• Those aged 25 to 34 years and 35 to 49 years were also less likely to be employed and more likely to be economically inactive if they had lived in a workless household, compared with other socio-economic backgrounds.

New graduate labour market statistics are out from the Department for Education.

• Working-age graduates and postgraduates continue to have higher employment rates than non-graduates. The employment rates for working-age graduates and postgraduates increased in 2021 while it fell for working-age non-graduates, widening the gap between these groups, and representing the first time that the gap has widened between graduates and non-graduates since 2013.
• In 2021, the employment rate for working-age graduates (those aged 16 to 64) was 86.7%, an increase of 0.4 percentage points on 2020 (86.3%). For working-age postgraduates the employment rate was 88.2%, an increase of 0.1 percentage points on 2020 (88.1%). For working-age non-graduates the employment rate was 70.2%, a decrease of 0.9 percentage points on 2020 (71.1%).
• In 2021, 65.2% of working-age graduates were in high-skilled employment, compared to 77.4% of postgraduates and 24.3% of non-graduates - but note this is the proportion of all graduates, not just those in employment. In 2021, the percentage of working-age graduates and postgraduates in high-skilled employment was 0.4 percentage points lower and 0.6 percentage points lower than in 2020 respectively. In comparison, the percentage of working-age non-graduates in high-skilled employment was 0.1 percentage points higher than in 2020.
• In 2021, the median salary for working-age graduates was £36,000. This was £10,000 more than working-age non-graduates (£26,000) but £6,000 less than working-age postgraduates (£42,000). The gap in median salaries between working-age graduates and non-graduates has not changed from 2020.
• Working-age disabled graduates had lower employment (73.8%) and high-skilled employment (50.8%) rates than non-disabled graduates (88.8% and 67.6%, respectively). The inactivity rate for disabled graduates (22.6%) was more than double the rate for non-disabled graduates (8.5%).
• White working-age graduates had the highest employment rate (87.2%) and high-skilled employment rate (66.5%). 'Other Ethnic Group' graduates saw the lowest employment rate (82.8%), while Black, African, Caribbean, or Black British graduates saw the lowest high-skilled employment rate (51.6%).
• Inactivity rates exhibited the least variation between ethnic groups - only 2.5 percentage points. Black or African or Caribbean or Black British graduates had the lowest inactivity rate (9.8%). Graduates in the Other Ethnic Group had the highest inactivity rate (12.3%).
• Black, African, Caribbean, or Black British graduates had the highest unemployment rate (7.0%), more than twice the rate of white graduates, who had the lowest rate (2.7%).
• Males employed in construction and in transport and communications had the highest median salaries across both working-age (£45,000) and young (£34,000) populations. Similarly, females employed in transport and communications saw the highest median salaries across both working-age (£39,500) and young (£32,500) populations.
• Males and females employed in distribution or hotels, or restaurants had the lowest median salaries across both working-age (£29,500 and £24,500 respectively) and young (£24,000 and £22,500 respectively) populations.
• Across all industries and age groups, males had higher median salaries than females.

The House of Commons Library have published a dashboard for examining the UK economy. Also from the House of Commons Library, a review of research into good work across the UK and Europe:

• In the UK, around 65% of women and 69% of men were in quality work in 2018, but there was variation between age groups, localities, and ethnic groups. The Good Work Monitor observes that the coronavirus pandemic affected people and places with the lowest levels of quality work the most. In particular, lack of good work options closely correlated with poor health outcomes like COVID-related illness and mortality during the pandemic.
• In the EU, working conditions have generally improved since 2000, with working time quality and physical working environment improving significantly. Jobs now require more skills and provide more autonomy than previously.

On the subject of dashboards, here are some new ones from the Department for Education:

• The first looks at Longitudinal Educational Outcomes (LEO) data on industry of employment for graduates.
• Another This looks at the post-16 qualifications of employees.
• And this looks at FE outcomes.

These dashboards come from the Future Skills Unit and their predecessor organisation, the Skills and Productivity Board, which has also issued a set of reports on the opportunities and challenges of producing LMI:

• We cannot measure skills matching directly because of limited data on the skills supplied and demanded across the workforce. Instead, we rely on indirect measures.
• The Employer Skills Survey (ESS) asks employers about their skills challenges, both in terms of their existing workforce and when recruiting. However, it has insufficient coverage to analyse sub-nationally and does not cover the skills employees may possess that they do not use (but could be productively employed in another job).
• Local Skills Reports produced by Skills Advisory Panels capture some information on local demand, and upcoming Local Skills Improvement Plans (LSIPs) should continue this - but not consistently across all areas.
• It may be possible to capture skills directly from job vacancies in future for some occupations.
• A limitation is a lack of common language relating to skills across these and related data sources.
• Employers demand skills for workers to perform specific jobs, it is therefore possible to indirectly infer demand for skills from demand for occupations.
• Future demand depends on changes in occupational structure (which in turn depends on future trends, whether economic, demographic, or policy-driven), as well as changes in skills used within occupations.
• DfE-commissioned Working Futures data projects employment by occupation but does not provide projections of changing skills within occupations.
• Qualitative measures (such as interviews) can provide some insight on changing skills needs within occupations but can only be focused on a small number of areas due to their relative high cost

That leads into this report on understanding skills needs:

• The analysis identifies a set of 'core transferable skills' that are currently in high demand across many occupations, including in the priority areas, and are likely to continue being in high demand in the future. These include communication skills, digital and data skills, application of knowledge skills, people skills, and mental processes. Because these skills are valuable across a wide range of jobs, firms have weaker incentives to invest in them than in firm-specific skills. Investing in the development of these core transferable skills is therefore likely to be worthwhile for government, as they equip people with skills that are important in many occupations, are transferable across occupations, and are at risk of under-investment from employers.
• Skills that are expected to increase in importance, especially those that are in shortage now, are likely to be another worthwhile investment, as they at risk of shortage in future. Skills that are growing in importance and used across many occupations in the economy include people skills, mental processes and application of knowledge skills, and skills associated with being able to teach others and be a good learner. Skills that are growing in importance, even though they are used in relatively fewer occupations, include STEM knowledge (particularly relevant for Health and Science and Technology occupations, and already likely to be in shortage now), care skills, important for Health occupations, and a range of management skills.
• To understand whether the skills identified as being in high and/or growing demand are also in shortage, we need to be able to compare the demand for these skills with the supply of these skills. However, we are limited by the available data, especially on the supply side. This makes it difficult to assess whether there is a genuine undersupply of these skills, or whether there is a more general issue with the labour market which is preventing efficient matching between people that possess these skills and the jobs that require these skills.
• Better data is needed to understand the underlying drivers of any perceived skills shortage and develop the appropriate policy response. Without this information, we risk investing heavily in certain skills, perhaps unnecessarily, while seeing shortages remain.

There's also a report on skills and productivity in 'levelling up' regions:

• Levelling up requires an improvement in the productivity of poorer areas of the country. Only with higher productivity are we likely to see higher real wages. The contribution of skills and skills systems will vary from locality to locality.
• The immediate priority is to achieve better matching between the demand and supply of skills. However, this will make only a modest contribution to levelling up in an area where the demand is for relatively low-level skills; the quality of production and of jobs will need to be improved. To make a greater impact, skills plans need to be part of a larger planning exercise.
• Success of such locally based strategies will depend, to an extent, on obtaining better local labour market information. In part, the responsibility for providing this information rests with employers.

And an analysis of LEO data to look at the mobility of graduates and the returns on moving:

• There are very high returns to qualifications, in terms of both employment and earnings. Reaching Level 2 (the equivalent of 5 GCSEs at A*-C) - something almost one in six people under 27 do not do - increases the probability of employment for women (men) by around 19 (10) percentage points and earnings by around 22% (13%).
• The returns to qualifications are higher in areas of the country that are economically poorer performing. Reaching Level 2 increases employment prospects for women (men) by around 25 (13) percentage points for individuals living in areas in the bottom quartile based on the Index of Multiple Deprivation (IMD, the 25% most deprived areas of the country), but only around 18 (8) percentage points in other areas.
• Differences are smaller, but still present, at higher levels of qualification. Mobility across labour markets is relatively low, particularly among non-graduates. Just one in six people whose highest qualification is below Level 6 (degree level) has moved commuting area by the age of 27 compared to around one in three with an undergraduate or postgraduate degree. Local moves are more common for non-graduates, with more than half of both graduates and non-graduates moving neighbourhoods by age 27.
• Mobility across labour markets is lower for individuals from areas of the country that are economically poorer performing. Among non-graduates, individuals who grow up in areas in the bottom quartile based on the IMD, are around 40% less likely to move commuting zone than those who grow up in the top quartile. Among graduates the difference is smaller but still sizeable, with those growing up in the bottom quartile around 25% less likely to move than those from the top quartile.
• Low mobility among non-graduates might be driven by low (short-term) returns to moving. Among those non-graduates who do move, men see a small increase in employment prospects but no change in earnings, while women are less likely to be employed but have slightly higher earnings if they are employed. By contrast, for graduates there are strong positive effects of moving on both employment and earnings (although these are reduced somewhat if we account for differences in the cost of living across areas).
• Investment in skills alone is unlikely to be sufficient to 'level up' economically poorer performing areas. The returns to education in poorer performing areas are strong, with the benefits of upskilling highly likely to remain within those areas, highlighting the importance of skills investments for the levelling up agenda.
• However, around a third of the difference in earnings between areas cannot be explained by individual characteristics such as education or skills, highlighting that other features of the areas, aside from the individuals that live there, play an important role in driving earnings. This suggests that complementary investments to improve those features of poorer performing areas may be required to fully realise the benefits of skills investments, and truly 'level up'.

The Recruitment & Employment Confederation (REC) have released their new Report on Jobs for May:

• May survey data pointed to a further robust increase in hiring activity across the UK, though there were signs of a further slowdown in overall growth. Notably, permanent staff appointments expanded at the softest rate since March 2021, while temp billings increased at the weakest pace in 15 months. Recruiters often mentioned that candidate shortages had weighed on placements.
• A further marked deterioration in overall candidate supply was seen in May. This was despite the rate of reduction easing to the softest in four months. Underlying data indicated that permanent candidates continued to decline at a faster pace than that seen for temporary workers. Moreover, the latest reduction in short-term staff supply was the least severe for just over a year. Panel members often mentioned that greater caution around the outlook, widespread skills shortages and fewer foreign workers had weighed on staff availability.
• Demand for staff continued to rise at a historically sharp pace in May. Broken down by job type, permanent vacancies continued to expand at a quicker rate than that seen for temp roles. Notably, demand for short-term staff increased at the softest pace for five months.
• With the supply of workers falling further and demand for staff remaining robust, recruiters noted sustained upward pressure on rates of starting pay in May. Despite softening to a four-month low, permanent starters' salaries rose at a rapid pace that was among the quickest since the survey began in October 1997.
• IT & computing posted the strongest increase in demand for permanent staff in May, closely followed by hotel & catering. Nonetheless, steep increases in vacancies were also seen across the other eight monitored job categories.

The Social Market Foundation has found that poor childcare provision means that new mums miss out on nearly £70,000 of earnings on average over the following decade:

• A woman who had her first child in 2010/11 typically suffered a cumulative income loss of £66,434 over the following nine years, relative to what would have happened if she had remained childless.
• Looking at women who were 25 to 35 in 2009/10, the SMF found that the typical woman who remained childless would have seen her earnings rise by around third over the next decade. By contrast, a woman who had a first child in 2010/11 was earning 10% less.
• Women on lower incomes face the biggest losses in earnings in their child's early years, the SMF found. In the first three years after having a baby, women on lower incomes saw their earnings fall by around 30%. But women on higher wages saw their earnings drop by around 20%.

New research from Business In The Community finds that 55% of workers find it hard to switch off from work. While 45% of employees feel that they can switch off from work, the other 55% stated they feel pressured to respond to calls or check emails after working hours. Of the employees who worked from home, 49% said they feel they can switch off from work, compared to 45% of employees who travel to and from work each day.

• 54% of employees said that they had too many priorities/targets, up from 51% in 2020.
• Less than a third of employees (29%) have the flexibility to alter the start and finish times of their working day.
• 56% of workers on temporary and zero hours contracts are less likely to be able to take their annual leave, compared to 79% of employees on fixed and permanent contracts.

And to finish, this interesting paper from Vox EU looks at the evidence on whether technological change creates or replaces jobs:

• Many technologies are designed to save human labour by replacing workers with machinery. However, economic theory suggests that several compensating mechanisms can counterbalance the initial labour-saving impact of new technologies.
• First, technological change can increase the demand for labour by creating new jobs that are directly associated with the new technology. Furthermore, technology-induced increases in productivity release production resources that can raise the demand for labour in other tasks within the same firm or industry.
• Second, technology can raise the demand for labour through increased consumer demand. This occurs when new technologies boost productivity growth and, in turn, lead to lower production costs and consumer prices. Moreover, new technologies can raise the marginal product of labour and capital, resulting in both higher wages and returns to capital. The two latter effects contribute to a rise in real income.
• The number of studies that support the labour replacement effect is more than offset by the number of studies that support the labour-creating/reinstating and real income effects. This observation is reaffirmed when looking at the studies that analyse the net employment effect of technological change, which in turn suggests the net impact of technology on labour to be rather positive than negative.
• The findings for the five distinct technology categories show broadly similar patterns, but with some subtle differences which are worth highlighting.
• ICT: there is no evidence that the replacement effect dominates the reinstatement and real income effects combined. However, the results suggest that the reinstated jobs qualitatively differ from the jobs replaced. The diffusion of ICT mostly has positive employment implications for high-skill, non-routine, and service jobs.
• Robots: we observe that the labour-saving impact is generally offset by robot-induced reinstatement of labour. In contrast to the ICT studies, robot studies tend not to touch upon the complementarity between robots and human labour in the performance of tasks. Hence, the labour-creating effect of robots is most likely related to the production, operation, and maintenance of this type of technology.
• Innovation: studies that rely upon innovation as a measure of technology often argue that the employment impact depends on the type of innovation. While product innovation is shown to be mostly labour-creating, the evidence on the employment impact of process innovation remains mixed.
• Productivity: when considering productivity improvements as a proxy for technological change, we find a roughly equal balance between the number of empirical studies that provide support for the replacement and the two labour-creating mechanisms. The employment gains have been mostly favourable for non-production, high-skill, and service jobs. These studies are often linked to theories that argue that technological change leads to structural change with a reallocation of economic activity down the supply chain from more primary towards increasingly processed sectors and services. Nonetheless, the net employment effects observed in these studies are rather negative than positive.
• Other: lastly, the findings from studies that rely on other/indirect measures of technology indicate that the labour replacing effect is offset by the labour-creating effect. The employment effects have been mostly positive for non-production labour, yet some studies also find positive employment effects for low-skilled workers, particularly in service jobs.
• Despite the fact that the researchers found no strong evidence for a negative net employment effect in quantitative terms, the qualitative impact and distributional aspects of technological change on employment cannot be neglected. In particular, low-skill production and manufacturing workers have been adversely affected by technological change.

Written by

Charlie Ball

Senior consultant: labour market intelligence

Jisc