Dr Kostas Kollydas discusses how geographic disparities in skills development and labour mobility can affect innovation in local skills policies in the UK.
The challenge of geographic disparities in skills and employment
One of the core challenges in local skills development is the misalignment between skills supply and demand, which is often driven by fragmented policy approaches. Skills shortages are compounded by the uneven geographical distribution of both workers and economic opportunities. For instance, some areas have an abundance of housing but limited job prospects. Other areas face a scarcity of affordable housing despite a high employment demand. This spatial imbalance requires place-based strategies that acknowledge the distinctive needs of different communities.
Labour market conditions and the skills needed for economic growth vary significantly across different places in the UK. Employment rates, for example, differ markedly by region, with the South East enjoying the highest employment, whereas areas like the North East and Wales fall behind [1]. Additionally, rural and isolated communities face particular challenges in attracting and retaining skilled workers. For instance, businesses in rural parts of the North East, South West, and West Midlands often struggle more with hiring suitable employees compared to those in urban areas. This suggests that investing in skills alone may not be sufficient to boost economically weaker areas. Additional support is necessary to attract workers to these areas. Specifically, rural areas, face obstacles such as inadequate public transport and a lack of affordable housing. This makes it harder to draw in talent. Moreover, economic inactivity rates vary significantly by area. For example, in 2022, inactivity rates (excluding students) in Northern Ireland ranged from 14.8% in Armagh City, Banbridge, and Craigavon in the southeast to 24.5% in Derry City and Strabane in the northwest. These disparities in employment and skills conditions may restrict the ability of some areas to pursue innovative local policies for skills development that could benefit broader population groups.
Industry-specific skills needs and regional specialisations
Different areas require distinct skill sets due to their varied industry specialisations. There is some overlap in skills identified as priorities in Local Skills Improvement Plans (LSIPs), but notable differences still exist. Almost all areas recognise AI and automation as emerging priorities. Other important sectors identified in many reports for different areas include manufacturing and engineering, health and social care, digital, and creative industries. Nevertheless, some industries are region-specific – for instance, London’s prominent finance sector, the nuclear industry in the North West of England, and agriculture concentrated in rural areas.
The table below reveals substantial differences in the emphasis placed on various skill types across the LSIP areas (see the table note for further explanations). Green skills, which were the only skills that employer representative bodies (ERBs) were mandated by legislation to address in relation to net zero targets, show higher levels of discussion in areas like Hull & East Yorkshire and Essex. Leadership skills also feature prominently (particularly in the West Midlands and Warwickshire), reflecting their importance in workforce development. Information and Communication Technology (ICT) and digital literacy have become important priorities, demonstrating the growing need for digital competence across different sectors. AI and automation, while covered to a lesser extent, vary more by LSIP area.
| Top 5 LSIP areas by type of skills discussed in LSIP reports (2023) | |||
| Green Skills & Net Zero | AI & Automation | ICT & Digital Literacy | Leadership Skills |
| Hull & East Yorkshire (0.62) | North East (0.38) | South Yorkshire (0.62) | West Midlands & Warwickshire (0.57) |
| Essex, Southend-on-Sea & Thurrock (0.62) | South Yorkshire (0.35) | Derbyshire & Nottinghamshire (0.60) | North of Tyne (0.55) |
| North of Tyne (0.59) | Hull & East Yorkshire (0.34) | North of Tyne (0.59) | Cumbria (0.54) |
| Tees Valley (0.59) | Cornwall & Isles of Scilly (0.34) | Essex, Southend-on-Sea & Thurrock (0.59) | Worcestershire (0.54) |
| Cornwall & Isles of Scilly (0.58) | Greater Lincolnshire (0.33) | Tees Valley (0.59) | Essex, Southend-on-Sea & Thurrock (0.53) |
| Average strength: 0.52 | Average strength: 0.29 | Average strength: 0.53 | Average strength: 0.47 |
| Note: The table shows the top 5 Local Skills Improvement Plan (LSIP) areas discussing each type of skill as highlighted in their corresponding LSIP reports. The average “strength of discussion”, shown at the bottom of each column, includes data from all 38 LSIP areas. The scores reflect the extent to which each skill type was discussed/addressed in the reports. Data were derived using AI-based analysis, including natural language processing (NLP) techniques and large language models (LLMs), which analysed the reports to determine “strength of discussion” by assessing semantic similarity scores. Source: Department for Education – AI analysis of local skills improvement plans | |||
Labour mobility and its impact on local skills policy innovation
Labour immobility is another considerable challenge, characterised by little movement across labour markets, especially amongst non-graduates. Graduates tend to cluster in certain urban areas like London, which may impede the efficient distribution of talent across the country. This situation further emphasises the significance of the priority stated recently by Skills England to work with regional bodies to ensure they meet local and national skills goals.
While remote work increased substantially during the COVID-19 pandemic, this pattern predominantly benefited higher socioeconomic groups. Around 51%-57% of the workforce continued to work outside their home in 2021, but there were remarkable differences based on geography, qualification levels, and employment conditions. Individuals with lower qualifications, those in routine jobs, and residents of more deprived areas were significantly less likely to work from home. Specifically, workers in the North East, the Midlands, Yorkshire and the Humber, the North East, Northern Ireland, and Wales were more exposed to in-person work compared to London residents, who had higher opportunities to work remotely. Key workers, including those in health, retail, and other essential services, were predominantly unable to work from home, reflecting a clear socioeconomic divide in mobility. Additionally, the shift to homeworking appears to have mixed effects on productivity, depending on individual circumstances. For instance, those without significant domestic duties were more likely to report enhanced productivity, whereas homeworking involving children and other domestic tasks was correlated with decreased productivity.
Such disparities in remote working patterns may conceal differences in access to innovative local skills policies, influenced by local population structure. In particular, workers commuting from areas outside large metropolitan subregions, often unable to work from home, may face challenges in benefiting from certain more flexible devolved skills programmes available only within those Combined Authorities (e.g. where adult skills funding is devolved). On the other hand, workplace-based skills initiatives could present an opportunity to bridge some of these disparities, as they can be directly accessed through employers irrespective of residential location. Hence, local population structure and mobility patterns likely matter for the successful implementation of place-based initiatives, as they determine the accessibility and effectiveness of skills development efforts across different population groups.
[1] Department for Education’s “Unit for Future Skills – Local Skills Dashboard” offers insightful local data on employment, skills, online job postings, and education outcomes by LSIP, Local Enterprise Partnership, and Mayoral Combined Authority areas. See also the ONS “Explore local statistics” tool.
This blog was written by Kostas Kollydas, Research Fellow, City-REDI, University of Birmingham.
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Disclaimer:
The views expressed in this analysis post are those of the author and not necessarily those of City-REDI or the University of Birmingham.