Report/Paper Title: Measuring Regional Skill Mismatches and Access to Jobs

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Report/Paper Title: Measuring Regional Skill Mismatches and Access to Jobs.

Author (s): Jyldyz Djumalieva, Stef Garasto and Cath Sleeman.

Thank you!

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About PIN

The Productivity Insights Network was established in January 2018 and is funded by the Economic and Social Research Council. As a multi-disciplinary network of social science researchers engaged with public, private, and third sector partners, our aim is to change the tone of the productivity debate in theory and practice. It is led by the University of Sheffield, with co- investigators at Cambridge Econometrics, Cardiff University, Durham University, University of Sunderland, SQW, University of Cambridge, University of Essex, University of Glasgow, University of Leeds and University of Stirling. The support of the funder is acknowledged. The views expressed in this report are those of the authors and do not necessarily represent those of the funders.

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Table of contents

Introduction 3 Access to jobs 4

Data and Methods 6 Online job adverts 6 Method for measuring skill mismatch 6 Background 6 Measuring skills supply 7 Measuring skill demands 8 Measuring skill mismatch 8 Methods for measuring access to jobs 9 Commuting measures 9 Job Accessibility Index 10 Accessibility maps 10 Accessibility curve 10

Results 11 Composition of skill demands 11 Nationwide skill demands 11 Regional skill demands 12 Composition and diversity of skills supply 15 Nationwide skills supply 15 Diversity of skills supply 16 Skill mismatch 18 Nationwide skill mismatch 18 Regional skill mismatch 21 Access to jobs 25 Distance 25 Travel time 27 Accessibility maps 27 Job Accessibility Index 30 Accessibility Curve 32

Conclusion 32

References 34

Appendices 37

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A skill mismatch is a discrepancy between skills that job seekers have and the skills that employers need. Evidence suggests that in recent years the UK has faced persistent skill mismatches (Department for Education, 2018). In 2017, UK employers struggled to fill 23% of vacancies (referred to as skills-shortage vacancies) due to a lack of skills, qualifications or experience among applicants.

Skill mismatches can hamper productivity, and ultimately are costly to the UK economy. According to OECD research, by reducing skill mismatches to OECD best practice levels, the UK economy could boost its productivity by 5% (McGowan and Andrews, 2015). And the Open University estimated that skill shortages, which are one kind of skill mismatch, cost the UK £2bn a year in higher salaries, recruitment costs and temporary staffing bills (Open University, 2017).

Despite the importance of this issue, there is a paucity of timely and detailed information on skill mismatches in the UK. The best available estimates come from the Employer Skills Survey (Department for Education, 2018). While the survey is able to shed light on the various causes of skill mismatches, and skill shortages in particular, it is only conducted once every two years and focuses on broad groups of skills.

Whereas surveys are typically restricted in their frequency and scope, novel sources of naturally occurring big data have the potential to provide more frequent and granular insights on skills. One such data source is online job adverts, which can offer a near real-time picture on skill demands. Moreover, job adverts may more accurately capture the skill needs of employers as the free text fields in adverts allow employers to exactly describe their needs, while skill surveys may force employers to select from a predefined list of skills.

Online job adverts also capture the locations of employment opportunities, which allows the demand for skills to be mapped by granular geographic regions. The ability to map regional skill needs is important as there is evidence of substantial differences between local economies (Haldane, 2019). The Centre for Progressive Policy found that among Local Enterprise Partnerships (LEPs) in England, the skill shortage rates for skilled trade roles varied from 26% in Cheshire and Warrington to 73% in the Black Country (Alldritt and Normal, 2018). It is likely therefore that the national picture of skill mismatches will be a poor proxy for any given region.

In light of the unique advantages of online job adverts, this research examines how adverts can be used to enhance the timeliness and granularity of existing statistics on skill mismatches. This research paper develops an alternative methodology for measuring skill mismatches. The methodology involves combining official labour market statistics with data on skill demands extracted from online job adverts. The aim is to provide a comprehensive analysis of skill mismatches across Great Britain.

Access to jobs

In the UK, the accessibility of jobs by car and public transport varies greatly. A recent report by the National Infrastructure Commission on Transport connectivity (National Infrastructure Commission, 2018) found that in certain urban areas (such as Burnley and Mansfield) jobs

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were up to three times less accessible by public transport than in other areas (such as in Brighton and Hove and Aberdeen).

Studies of skill mismatches generally overlook these geographic barriers, but as evidenced, poor transport infrastructure can exacerbate skill mismatches in regions. This research showcases a range of job accessibility measures for Travel to Work Areas (TTWAs), which can be used to evaluate the impact of transport on employers’ access to skills. These measures are created by collecting and analysing data on the duration of thousands of trips across a given TTWA to estimate the accessibility of different job locations within the region.

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Data and Methods

This research uses a combination of official statistics on employment, naturally occurring big data in the form of online job adverts, and open source information on commuting and transport in Great Britain.

Online job adverts

The dataset of online job adverts was provided by Burning Glass Technologies (Burning Glass Technologies, 2019). The dataset was generated by scraping active job postings for UK-based positions from thousands of web-pages on a daily basis. The resulting dataset contains information on the job title, salary, location, education and experience requirements for each posting. Each datapoint also contains a set of keywords extracted from the advert’s description, however the full job descriptions are not available. While the keywords are referred to as 'skills', these also include terms that describe personal characteristics, industry experience, knowledge and non domain-specific skills. The total dataset contains over 53 million adverts collected between January 2012 and December 2018.

Online job adverts are a rich source of information on skills, but are not without limitation. Not all work is advertised online and so adverts may not be representative of all vacancies (Carnevale et al., 2014, Kureková et al., 2015). This can lead to underestimation of demand for skills predominantly used by freelance workers. The skill requirements in online job adverts may also be incomplete and adverts may omit information on basic skills.

Method for measuring skill mismatch

Background

A skill mismatch is a mis-alignment between the attributes of job seekers and the requirements of employers. There are several forms of skill mismatch, as outlined by Green (2016). The focus of this research is on skill shortages and oversupply, which occur when employers cannot find workers with the right skills, or conversely skilled workers cannot find suitable employment. This research does not consider other forms of skill mismatch such as when workers lack the skills required for their job or alternatively their skills are underutilized by employers.

Typically skill mismatches are only measured for occupations and industries. This research provides estimates by skill categories. The estimates are also more granular than in previous research, both in terms of the number of skills considered and the number of regions for which mismatches are estimated. The use of job adverts also allows estimates to be made more frequently. Finally the methodology produces estimates of the drivers of skill mismatches, namely skill demands and skills supply.

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Measuring skills supply

The methodology assumes that workers (who represent the stock of skills supplied) possess all the skills necessary for their current jobs.1 This assumption enables the stock of skills supplied to be measured by identifying the skills required for each occupation and then counting the number of workers in these occupations.

The skills required for each occupation are identified by assigning job adverts (which mention skills) into occupations, where occupations are defined by the third-layer of occupations from the 2010 Standard Occupational Classification (henceforth called SOC). Different approaches are used to identify the appropriate occupation (SOC) code for a given advert, including information on SOC codes that has been provided by Burning Glass Technologies. Only ‘reliable’ adverts are used, those being adverts where the different coding approaches agree on the SOC code that should be assigned to the advert. Approximately 20% of adverts cannot be assigned to a reliable SOC code.

Having assigned job adverts to SOC codes, the skills required for adverts in a given code can be extracted. Each skill has been assigned to branches of the skills taxonomy, developed by Djumalieva and Sleeman (2018) (Figure 1). The taxonomy has three layers and its final layer contains 143 skill categories. Each SOC code can be described by its reliance on different combinations of these skill categories. To ensure that changes in skill needs are captured, the process of mapping SOC codes to skill categories is performed separately for each year of job adverts.

Figure 1. Fragment of the skills taxonomy by Djumalieva and Sleeman (2018).

After mapping skills to occupations, the second step to measuring skills supply is to determine the number of workers employed in each occupation. Although this information is readily available at a national level, the latest regional2 estimates of employment by SOC code come from the Census, which was conducted in 2011 (Office for National Statistics, 2016a). The risk is that the composition of employment, by SOC code, has changed over time. To allow for this,

1 The Employer Skills Survey suggests that 4.4% of the workforce is not fully proficient at their job indicating skills gap (Department for Education, 2018). 2 Regional estimates refer to statistics on employment in Lower Layer Super Output Areas.

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the national estimates of employment by SOC code for 2012 to 2018 are used to adjust the Census data to ensure that the distribution of employment within the broad occupation groups (first digit SOC) mimics the national distribution in that year. But this method does assume that the composition of occupations at the broad level (first digit SOC) has not changed significantly between 2011 and 2018.

The reason that qualifications are not used to measure skills supply is that we lack an accurate mapping of skills to qualifications. Future research using longitudinal educational outcomes data might enable the development of such mapping.

Diversity of skills supply

In addition to using the skills supply estimates to measure skill mismatch, the estimates are also used to compute a metric called the ‘diversity of skills supply’. This measure captures the variety of skills on offer within a given region from workers. Greater skill diversity may increase a region's resilience to economic shocks.

The diversity measure was inspired by measures of species diversity from the field of ecology. These measures take into account both the richness (number of different species) and abundance of species (Maignan, Ottaviano, Pinelli, and Rullani 2003).

Measuring skill demands

Skill demands are measured by identifying the flow of skills required by employers, as seen through the skills mentioned in job adverts. One risk is that certain occupations (and therefore skills) may be overrepresented or underrepresented in the adverts. To measure this, the distribution of adverts over occupations was compared to figures from the ONS’s Vacancy Survey (Office for National Statistics, 2018c), after addressing the added complication that this Survey reports vacancies for industries but not for occupations or regions.

Certain occupational groups were found to be overrepresented in the dataset of job adverts, and these include Science, Research, Engineering And Technology Professionals and Teaching and Education Professionals. The most underrepresented groups included the likes of Skilled Agricultural And Related Trades and Elementary Trades and Related Occupations.

In order to ensure that the adverts were representative, the adverts in every year were resampled to mimic the composition of vacancies as reported in the Vacancy Survey.

Measuring skill mismatch

For a given skill category and year, the skill mismatch is calculated as a ratio between:

● The relative supply of the skill. This is the supply of the skill as a share of all the skills supplied in a given region. ● The relative demand for the skill. This is the demand for the skill as a share of the demand for all skills in that region.

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A value less than 1 indicates a potential shortage of that skill within the region. The key limitation of this metric is that, for a given skill, the relative demand for the skill (based on vacancies which is a flow) may not be comparable to the relative supply of the skill (based on employment which is a stock). The lack of data on the lifecycle of vacancies means that it is not possible to accurately convert the flow of vacancies into a stock. Similarly, a lack of data on the flow of workers (between jobs and into and out of the workforce) means that it is not possible to convert the stock of workers into a flow. For these reasons the analysis cannot produce estimates of the absolute number of workers behind any skill shortage and instead must be content with comparing the relative supply of a skill to its relative demand.

Methods for measuring access to jobs

Access to jobs across regions is assessed through two measures: i) the average distance of a commute; ii) the average duration of a commute. The ideal metric for access would include a large number of variables, ranging from fares and the cost of owning a car to the cost of housing and the reliability of commuting routes (Litman, 2013, Mattingly and Morrissey, 2014). While ‘distance’ and ‘time’ may not reflect all aspects of access to jobs, these measures are an obvious starting point and are also easy to understand (Smith, 2018). The analysis compares access to jobs, via these metrics, across a set of regions known as Travel to Work Areas (or TTWAs). A TTWA is an area where most of the commuting occurs within its boundaries (Prothero, 2016). By definition, at least 75% of residents in a TTWA also work in the area, and at least 75% of the workers in a TTWA also live in the area. TTWAs have been defined by the ONS in partnership with Newcastle University. Currently, there are 228 non-overlapping TTWAs covering the whole of the UK.

Commuting measures

The average distance of a commute within a given TTWA is based on data from the 2011 Census which provides statistics on the typical commuting distance of workers in an Output Area (OA). This distance is computed as the straight line distance between the residence and the workplace postcodes. There are over 200,000 OAs in the UK. The average commuting distance estimates might be affected by a minority of people (up to one third of the population in a TTWA) commuting between TTWAs.

The data on commute duration was collected using a tool called Open Trip Planner (OTP) (OpenTripPlanner, 2009), which was originally developed by OpenPlans. The tool is open source and enables researchers to query commuting times (by varying transport modes and at different times) across Great Britain. It is based on data from OpenStreetMap (Haklay and Weber, 2008) and was queried using the propeR software (Data Science Campus, 2019). The analysis below focuses just on car journeys, as it is by far the most popular mode of transportation in Great Britain.

For each TTWA, twenty smaller areas (Lower Layer Super Output Areas) with the largest number of jobs were chosen as ‘destinations’.3 Lower Layer Super Output Areas (LSOA) are a

3 The 2011 Census was used to identify the LSOAs with the highest number of jobs, and so the method assumes that the prominent employment locations have remained relatively constant in recent years. This assumption could

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census geography with a generally consistent population size4. Thus, LSOAs with a smaller land area represent more densely inhabited locations. For every LSOA in a region, the travel times to the selected destinations were computed. Specifically, for each pair (origin and destination), two smaller areas (Output Areas) within the origin location were chosen at random, as were two areas within the destination location. The travel times between all four pairs were computed and an average was taken5.

Job Accessibility Index

The Job Accessibility Index (JA Index) captures the average proportion of the population6 that can reach a job location in a TTWA within 27 minutes. The JA Index varies between 0 and 1. A JA index with a value of 1 denotes perfect accessibility within the time constraints of all selected job locations in a given TTWA. The 27 minutes threshold was chosen because it is the average commuting time in Great Britain7.

One limitation of the approach is that the JA index depends on the way job locations are selected and their relative spatial distribution inside a TTWA. Performing a sensitivity analysis of the JA index is a possible mitigation strategy.

Accessibility maps

A range of accessibility maps are provided for the TTWAs within the West Midlands in the Results section. These maps are the most granular measures of Job Accessibility and show the proportion of jobs reachable from each LSOA within the region.

Accessibility curve

An ‘accessibility curve’ shows the percentage of jobs that can be accessed by a given percentage of the population within 27 minutes. The curve captures how quickly access to a job degrades as the proportion of the population who need to reach it increases. The ideal curve is one that is fixed at 100%, so that all the jobs are accessible by the region’s whole population within the time constraint.

be investigated by utilising more recent sources on the distribution of jobs, such as the Business Registry and Employment Survey. 4 LSOAs have an average population of 1500 people. 5 The OTP tool, which was used to collect the commuting times, does not account for the volume of traffic. This would require building a traffic model by sampling journeys from a commercial tool, such as Google Maps API. 6 The 2011 Census was used to determine the number of residents in each LSOA. 7 The average commuting time was computed from official ONS statistics (Office for National Statistics, 2018b).

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Results

Composition of skill demands

Nationwide skill demands

Figure 2 below shows estimates of skill demands, broken down by category. A large proportion of vacancies in Great Britain require skills from three broad categories: Business and Administration, Education, Sales and Marketing and Engineering, Construction and Transport. Together these account for over 76% of the skills mentioned in adverts. In contrast, the demand for Science and Research skills represents less than 1% of the skill requirements in adverts.

At a more granular level, the demand for skills across different skill categories varies to a great extent, with the largest five categories accounting for 56.7% of the skills mentioned in job adverts. The most demanded skills are in Sales, Construction, Maintenance and Transport, Management and HR, Caregiving and Rehabilitation and Administration and Law.

Figure 2. Estimates of skill demands by category.

Comparing the breakdown of skill demands between 2012 and 2018 shows that the share of vacancies requiring skills from both the broad and more granular categories has remained relatively stable over time.

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Regional skill demands

There is noticeable variation across Great Britain in the demand for skills by employers. Figure 3 shows the composition of skill demands in 2018 for Great Britain, Scotland and the 10 largest TTWAs within Scotland (ranked by their workplace population).

The chart shows that for Scotland as a whole, the composition of skills demanded looks similar to that for Great Britain. However, the composition varies substantially between regions (TTWAs) within Scotland. For instance, Aberdeen has greater demand than Edinburgh for skills relating to Engineering, Construction and Transport. Demand for IT skills also appears to be higher in urban areas.

Figure 3. Estimates of skill demands by category for Great Britain compared with that for Scotland and the 10 largest TTWAs in Scotland (by workplace population).

Local quotients were used to gauge the concentration of skill demands across regions. These show the extent to which the demand for skills in a given region differs from a larger region, where that larger region may be a nation or Great Britain. Figures 4 and 5 illustrate the variation in skill demands (via local quotients) for Wales and three regions within Wales. Negative values indicate that demand for the skill category is relatively weak in that area, while positive values indicate that the region has relatively higher demand for that skill than in the wider area.

Comparing Wales to the whole of Great Britain, it appears that vacancies which require Business Administration and IT skills are underrepresented in Wales, while vacancies in Engineering, Construction and Transport appear to be overrepresented in the country.

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Underrepresented ← → Overrepresented

Figure 4. Variation in skill demands (via local quotients) in Wales.

After comparing regions within Wales a more complex picture emerges. Cardiff’s skill demands are very similar, in composition, to the whole of Great Britain. But for smaller Welsh regions, such as Wrexham, there is a substantial deviation from the composition in Great Britain, with greater demand for Health and Social Care skills and relatively less demand for Logistics, Accounting, Education, Languages and Art and Sales skills.

In addition to analysing the skills demanded within a given region, it is also possible to explore the variation across regions in the demand for a specific type of skill. Figures 6 and 7 show monthly data on the regional demands (expressed as local quotients) for two skill categories across the four largest TTWAs in the West Midlands. The charts have the same scale and show how regional demand is much more variable for some skills than for others. Specifically, Figure 6 shows that the relative demand for Business Management skills is very similar between the regions. In contrast, the demand for skills related to Systems Administration varies a great deal between the regions and is highest in Birmingham and Coventry.

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Figure 5. Variation in skill demands (via local quotients) for Cardiff, Newport and Wrexham.

Figure 6. Monthly data on the regional demands (expressed as local quotients) for Business Management skills across the four largest (by workplace population) TTWAs in the West Midlands.

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Figure 7. Monthly data on the regional demands (expressed as local quotients) for Systems Administration skills across the four largest (by workplace population) TTWAs in the West Midlands.

Composition and diversity of skills supply

Nationwide skills supply

Figure 8 shows the composition of skills supply for Great Britain as a whole. The mix of skills supplied appears to be broadly similar to the mix of skills demanded (shown in the previous sub-section). Over 77% of workers are employed in occupations that primarily require skills from three of the broad skill categories, which are Business Administration, Education, Sales and Marketing and Engineering, Construction and Transport.

At a more granular level, the five skill categories that account for the largest share of employment are Construction, Maintenance and Transport, Sales, Management and HR, Caregiving and Rehabilitation, and Administration and Law. More than 53% of workers are assumed to have skills from these five categories.

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Figure 8. Estimates of skills supply for Great Britain.

Diversity of skills supply

‘Skill diversity’ is a novel indicator that was inspired by measures of species diversity that are used in the field of ecology. This indicator captures the variety of skills required across the occupations in a given region. The skill diversity indicator is based on the Shannon-Wiener index of diversity, which is one of the most commonly used measures.

Areas with greater skill diversity have a broader skill mix and are less reliant on just a few categories of skills. Raising skill diversity may help regions to become more resilient to structural changes in local labour markets. The areas with the highest skill diversity are Oxford, Reading, Brighton, Cambridge and Aberdeen (Figure 9). The five areas with the lowest skill diversity are Ullapool, Broadford and Kyle of Lochalsh, Fraserburgh, Fort William, and Campbeltown.

As shown in Figure 10, initial results suggest that the diversity of skills supply increases with an area’s population, and so cities tend to have higher diversity scores. However, there are instances when TTWAs with similar populations differ significantly on their diversity of skills supply. For example, St Andrews and Cupar is similar in workplace population size to Dumbarton and Helensburgh, but has a much higher diversity of skills supply. This might be due to the fact that St Andrews and Cupar hosts the University of St Andrews. In contrast, Dumbarton and Helensburgh is increasingly a commuter town for Glasgow following the decline of shipbuilding, glassmaking and whisky production.

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Figure 9. Map of skills supply diversity in Great Britain.

Figure 10. Relationship between Workplace population8 and Diversity9.

8 Log-transformed values for Workplace population are shown. 9 Diversity has been scaled to vary between 0 and 1.

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Across the larger regions of England and nations of Great Britain, Scotland appears to possess a slightly less diverse mix of skills (Table 1). While Scotland has several regions with high diversity (such as Aberdeen, Edinburgh and Inverness), it also contains a substantial number of low-populated areas that have less diversity.

Table 1. Skill diversity index for regions and countries in Great Britain.

Region/Country Mean diversity index

Scotland 5.129

Wales 5.324

East Midlands 5.338

North East 5.342

Yorkshire and The Humber 5.349

North West 5.368

East of England 5.380

South West 5.381

West Midlands 5.386

South East 5.457

London 5.515

Skill mismatch

Nationwide skill mismatch

The previous sections showed the composition of skill demands and skills supply separately, both nationwide and across regions. These two sets of estimates can be compared to gauge the extent of skill mismatches. While it is not possible to estimate the number of unfilled vacancies or the number of individuals whose skills are in oversupply, it is possible to measure the direction and magnitude of any skill mismatch.

Skill mismatch is the ratio of the relative supply of a given skill to the relative demand for that same skill. A value less than 1 indicates a potential skill shortage, while a value greater than one suggests a potential oversupply.

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Figure 11 below illustrates the measurement of skill mismatch for Social Work and Caregiving, which appears to have a skill shortage nationwide.10 The chart suggests that the skill shortage in social workers may have intensified over the last six years.

To arrive at an average skill mismatch for a given skill category, the skill mismatch ratio for each year was calculated and then averaged. Table 2 lists some of the skill categories with the lowest average skill mismatch ratio nationwide, indicating potential skill shortages.

Figure 11. Comparison of the relative skills supply and demand for Social Work and Caregiving from 2012 to 2018.

Table 2. Skill categories with the largest potential skill shortages between 2012 and 2018

Granular skill category Average skill mismatch Broad skill category ratio across years

Retail Management 0.720 Management and HR

Retail 0.835 Sales

Logistics Administration 0.860 Logistics

Social Work and Caregiving 0.863 Caregiving and Rehabilitation

General Sales 0.878 Sales

While the skill mismatch ratio will capture skill shortages and oversupply, it may also be influenced by several other factors. One of these factors is the rate of employee turnover in a given occupation. A higher turnover rate increases the number of job adverts and raises the

10 The estimate of relative supply for 2012 is shown in grey as it is the most reliable estimate. This is because the 2011 Census was used to determine the composition of 1-digit SOC groups by 3-digit SOC groups, and this composition may have changed over time.

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flow of vacancies, but not necessarily the stock. This may cause the demand for skills in these occupations to be overestimated. And this is likely to be the reason why Retail, Logistics Administration and General Sales have some of the highest mismatch ratios between supply and demand in the direction of a skill shortage.

Several of the skill categories shown in Table 2, like Logistics Administration and Social Work and Caregiving also offer some of the lowest salaries (the median offered salaries are £20K and £25K respectively) (Djumalieva and Sleeman, 2018). The strong demand for these skills might be related to a rise in the application of low-skill, high-turnover business models (Lee, Green and Sissons 2018) characterised by oversupply of low-skilled workers who face low probabilities of upward earnings mobility. As a result, low skilled workers cycle between insecure, low paid jobs, contributing to the observed rise of in-work poverty.

Hiring practices is another factor that can influence the skill mismatch ratio. Teaching is a good example since, unlike many other occupations, the recruitment for teachers is not evenly spread out across the year (House of Commons Education Committee, 2017). Schools will aim to fill vacancies at the start of the school year. Therefore, the annual peak in the number of teacher vacancies is probably a better measure of demand than a monthly average. New entrants are also recruited through structured Initial Teacher Training programmes. This means that some vacancies will not be reported as there is a trainee teacher preparing to fill the vacancy. Funding constraints may also prevent a school from filling teacher shortages, meaning that the positions are not advertised, and giving the impression that there is no shortage. And finally, it is not possible to identify situations where specialist teaching roles have been filled by non-specialists, leading to in-work skill shortages. For all these reasons, the demand for teaching skills might be underestimated, giving the false appearance over an oversupply in this skill category (Table 3).

Table 3. Skill categories with the smallest potential skill shortages between 2012 and 2018.

Granular skill category Average skill mismatch Broad skill category ratio across years

Heating, Ventilation and Plumbing 1.447 Construction, Maintenance and Transport

Construction 1.399 Construction, Maintenance and Transport

Teaching 1.385 Education, Languages and Art

Civil Engineering 1.203 Civil Engineering and Design

Electronics 1.192 Mechanical and Electrical Engineering

Several other categories that appear to have a skill mismatch in the direction of oversupply are linked to the construction industry. Employers in this industry have been previously found to rely on informal recruitment practices (Oswald et al, 2018). Because of that the number of construction vacancies is likely to be underrepresented in both online job adverts and in the Vacancy Survey, preventing the detection of a potential skill shortage.

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It is important to ensure that the methodology for measuring skill mismatch permits the disentanglement of different factors that might contribute to the observed misalignments between skills demand and supply. This issue will be addressed in future work and will entail estimating the number of people available to fill open vacancies including those who transition between jobs (e.g. churn) and those joining/leaving the workforce. It will also require collecting additional data to enable the conversion of vacancy data from a flow into a stock so that it can be compared to the stock of supply.

Regional skill mismatch

The table below shows the skill categories with the greatest skill mismatches for each of the ten largest TTWAs in Great Britain (by population). There are substantial differences between the potential skill shortages in these regions and the skill shortages at a nationwide level, suggesting that the former should not be used to proxy for the latter. Specifically, these large regions appear to have much greater shortages in highly-skilled services, such as Finance, IT and Marketing. Categories with skill mismatches in the direction of oversupply tend to include Skilled Trades, Healthcare and Teaching.

Table 4. Greatest skill mismatches for each of the ten largest TTWAs (by population).

TTWA 10 skill categories with largest 10 skill categories with largest mismatch mismatch in the direction of shortage in the direction of oversupply

London 1. Insurance and Lending, 1. Construction, 2. Marketing Research, 2. Heating, Ventilation and Plumbing, 3. Securities Trading, 3. Teaching, 4. App Development, 4. Civil Engineering, 5. Servers and Middleware, 5. Mental Health, 6. Seb Development, 6. Patient Assistance and Care, 7. BI and Data Warehousing, 7. Surgery, 8. Financial Asset Management, 8. Oncology, 9. Data Engineering, 9. Shipping and Warehouse 10. HR Management Operations, 10. Welding and Machining

Manchester 1. Legal Services, 1. Construction, 2. Financial Asset Management, 2. Heating, Ventilation and Plumbing, 3. Securities Trading, 3. Teaching, 4. Complex Sales, 4. Civil Engineering, 5. Payroll and Tax Accounting, 5. Construction Engineering, 6. HR Management, 6. Manufacturing Methods, 7. Marketing Strategy and 7. Welding and Machining, Branding, 8. Electrical Engineering, 8. Graphic and Digital Design, 9. Health, Safety and Environment, 9. Digital Marketing, 10. Electronics 10. General Sales

Slough and 1. Social Work and Caregiving, 1. Heating, Ventilation and Plumbing, Heathrow 2. Driving and Automotive 2. Teaching, Maintenance, 3. Construction, 3. Retail Management, 4. Civil Engineering, 4. Logistics Administration, 5. Construction Engineering, 5. Retail, 6. Web Development, 6. Payroll and Tax Accounting, 7. Manufacturing Methods, 7. General Sales, 8. Design and Process Engineering, 8. Patient Assistance and Care, 9. Graphic and Digital Design,

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9. HR Management, 10. Legal Services 10. Accounting and Financial Management

Birmingham 1. HR Management, 1. Construction, 2. Financial Asset Management, 2. Heating, Ventilation and Plumbing, 3. Securities Trading, 3. Teaching, 4. Marketing Strategy and 4. Mental Health, Branding, 5. Surgery, 5. Complex Sales, 6. Oncology, 6. Digital Marketing, 7. Civil Engineering, 7. Legal Services, 8. Patient Assistance and Care, 8. Business Analysis and IT 9. Social Work and Caregiving, Projects, 10. Manufacturing Methods 9. Graphic and Digital Design, 10. BI and Data Warehousing

Glasgow 1. HR Management, 1. Teaching, 2. Web Development, 2. Construction, 3. System Administration, 3. Health, Safety and Environment, 4. Software Development, 4. Heating, Ventilation and Plumbing, 5. BI and Data Warehousing, 5. Surgery, 6. Securities Trading, 6. Graphic and Digital Design, 7. Shipping and Warehouse 7. Legal Services, Operations, 8. Civil Engineering, 8. Networks, 9. Mental Health 9. Payroll and Tax Accounting, 10. Business Analysis and IT Projects

Newcastle 1. Driving and Automotive 1. Legal Services, Maintenance, 2. Construction, 2. Marketing Strategy and 3. Heating, Ventilation and Plumbing, Branding, 4. Teaching, 3. Complex Sales, 5. Civil Engineering, 4. General Sales, 6. Construction Engineering, 5. Digital Marketing, 7. Mental Health, 6. Web Development, 8. Surgery, 7. Financial Asset Management, 9. Patient Assistance and Care, 8. Retail, 10. Health, Safety and Environment 9. Securities Trading, 10. Data Engineering

Liverpool 1. Complex Sales, 1. Construction, 2. Marketing Strategy and 2. Heating, Ventilation and Plumbing, Branding, 3. Teaching, 3. HR Management, 4. Civil Engineering, 4. Financial Asset Management, 5. Construction Engineering, 5. Digital Marketing, 6. System Administration, 6. Retail Management, 7. Networks, 7. Graphic and Digital Design, 8. Software Development, 8. Driving and Automotive 9. Accounting and Financial Maintenance, Management, 9. Securities Trading, 10. Accounting Admin 10. Procurement

Leicester 1. Welding and Machining, 1. Teaching, 2. Driving and Automotive 2. Heating, Ventilation and Plumbing, Maintenance, 3. Construction, 3. Payroll and Tax Accounting, 4. Oncology, 4. Shipping and Warehouse 5. Mental Health, Operations, 6. Construction Engineering, 5. Social Work and Caregiving, 7. Health, Safety and Environment, 6. General Sales, 8. Surgery, 7. Complex Sales, 9. System Administration,

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8. Accounting Admin, 10. General Practice 9. Graphic and Digital Design, 10. Legal Services

Bristol 1. Legal Services, 1. Teaching, 2. Shipping and Warehouse 2. Construction, Operations, 3. Heating, Ventilation and Plumbing, 3. Driving and Automotive 4. Mental Health, Maintenance, 5. Oncology, 4. Payroll and Tax Accounting, 6. Surgery, 5. Complex Sales, 7. Civil Engineering, 6. Securities Trading, 8. Health, Safety and Environment, 7. Financial Asset Management, 9. Construction Engineering, 8. HR Management, 10. Patient Assistance and Care 9. Retail Management, 10. Marketing Strategy and Branding

Leeds 1. Legal Services, 1. Teaching, 2. Payroll and Tax Accounting, 2. Construction, 3. Web Development, 3. Heating, Ventilation and Plumbing, 4. Financial Asset Management, 4. Mental Health, 5. Securities Trading, 5. Oncology, 6. Complex Sales, 6. Surgery, 7. HR Management, 7. Patient Assistance and Care, 8. BI and Data Warehousing, 8. Civil Engineering, 9. Marketing Strategy and 9. Social Work and Caregiving, Branding, 10. Health, Safety and Environment 10. Accounting and Financial Management

Given the noticeable variation in skill mismatches and, in particular, skill shortages across regions, this section explores the extent to which skill mismatch ratios for skill categories varies across the 160 largest TTWAs (ranked by workplace population). The reason for focusing on the largest areas is due to concerns about sample sizes and sampling errors for smaller TTWAs, which tend to have a population of less than 60,000 (Office for National Statistics, 2016b).

Table 5 shows the skill categories that have the greatest variation in the level of mismatch across the largest TTWAs. On the one hand it is surprising that Teaching has the highest variation in skill mismatch across TTWAs. However, regional differences in teacher supply are widely acknowledged. To some extent these may be caused by the concentration of Initial Teacher Training in more urban locations (House of Commons Education Committee, 2017).

Skill mismatches for several of the IT skill categories vary widely across regions. Potential shortages of these skills are very high in Glasgow, Leeds and London, and low in Kenal, Perth and Elgin. The distribution of the skill mismatch ratio across the largest TTWAs is shown in Figure 12 to illustrate the regional variation.

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Table 5. Skill categories with the greatest variation in their levels of mismatch across TTWAs.

Granular skill category Standard deviation of TTWAs with largest TTWAs with smallest skill mismatch ratio potential shortages potential shortages across largest TTWAs

Teaching 0.605 Ayr, Kilmarnock and Glasgow, Kendal, Irvine, Elgin Cheltenham

Legal Services 0.493 Tunbridge Wells, Swansea, Blyth and Aberdeen, Leeds Ashington, Blackpool

Welding and Machining 0.389 Livingston, Dunfermline Whitehaven, Boston, and Kirkcaldy, Motherwell St Austell and and Airdrie Newquay

Web Development 0.385 Glasgow, London, Leeds Kendal, Perth, Elgin

Construction 0.351 Kilmarnock and Irvine, Hereford, Kendal, Motherwell and Airdrie, Skegness and Louth Dundee

Heating, Ventilation and 0.340 Kilmarnock and Irvine, Kendal, Hereford, Plumbing Motherwell and Airdrie, Torquay and Paignton Livingston

Graphic and Digital 0.310 Boston, Leeds, Kendal, Scarborough, Design Birmingham Isle of Wight

Data Engineering 0.302 Glasgow, London, Leeds Kendal, Perth, Elgin

Software Development 0.299 Glasgow, London, Leeds Kendal, Elgin, Skegness and Louth

Electronics 0.298 Barrow-in-Furness, Colwyn Bay, Whitehaven, Livingston Cinderford and Ross- on-Wye, Northallerton

Figure 12. Distribution of skill mismatch ratio for Data Engineering across TTWAs.

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Access to jobs

Distance

Figure 13 shows the average commuting distance (in kilometers) for workers in each TTWA across Great Britain.11 Purple indicates the shortest commuting distances, while yellow indicates regions where commuters travel further. Differences in commuting distances across the country are not negligible, and workers in some areas, such as Chelmsford, commute almost twice as far as workers in other areas, such as Edinburgh.

Most of the areas with the longest commuting distances are located in the South East and in the East of England (Table 6), with a particular concentration around London (e.g. Stevenage and Welwyn Garden City) and on commuter routes into London (e.g. Grantham). On average, workers in the London TTWA commute for 8.75 kilometers. However, most TTWAs surrounding London show average distances of over 11 kilometers. This result might be partially explained by workers who do not commute to London from surrounding areas and face generally longer commutes across their regions. Further investigation of this result would require gathering data on the commutes of individuals living and working in the TTWAs that surround London.

Figure 13. Average commuting distances (in km) for TTWAs in Great Britain.

11 Grey areas have been excluded from this and the following analysis owing to their small size which makes them more susceptible to the influence of outliers.

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Table 6. Top ten TTWAs with the longest average commuting distance.

TTWA name Region/Country Avg. commuting distance (km)

Banbury East Midlands 12.24

High Wycombe and Aylesbury South East 12.32

Grantham East Midlands 12.37

Stevenage and Welwyn Garden East of England 12.50 City

Medway South East 12.55

Southend East of England 12.79

Thetford and Mildenhall East of England 12.79

Huntingdon East of England 13.33

Tunbridge Wells South East 13.65

Chelmsford East of England 13.96

The areas with the shortest commuting distances are concentrated in Scotland (Table 7). However, Scotland has also the greatest variability in commuting distances, which makes London the TTWA with the lowest average commuting distance for its workers overall despite having a population of around 8 million people. That said, there is more variability within regions than between regions (Table 8). Following Scotland, the South East and the North East have the next highest levels of variation in commuting distances.

Table 7. Top ten TTWAs with the shortest average commuting distance.

TTWA name Region/Country Avg. commuting distance (km)

Edinburgh Scotland 7.13

Glasgow Scotland 7.25

Dundee Scotland 7.82

Inverness Scotland 7.85

Torquay and Paignton South West 7.88

Aberdeen Scotland 8.01

Blackpool North West 8.04

Plymouth South West 8.11

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Hawick and Kelso Scotland 8.23

Dudley West Midlands 8.28

Table 8. Comparison of average commuting distances (km) across regions and countries in Great Britain.

Region 25% percentile Median 75% percentile Standard deviation

East Midlands 9.90 10.50 11.26 1.00

East of England 11.13 11.80 12.50 1.14

London - 8.75 - -

North East 9.01 9.63 11.27 1.21

North West 9.12 9.47 10.18 1.02

Scotland 8.24 9.61 10.33 1.33

South East 10.14 10.94 11.92 1.22

South West 9.38 9.66 10.70 1.14

Wales 9.97 10.24 11.47 0.91

West Midlands 9.42 9.63 10.62 0.95

Yorkshire and The 8.75 9.65 10.29 1.01 Humber

Travel time

The length of time it takes to commute is arguably a more important indicator of job accessibility than the distance travelled. This section focuses on the duration of commute by car.

The analysis is limited to regions in the West Midlands, as the data collection is computationally demanding. However, as part of this project, infrastructure has been built to enable the collection of travel time information for the whole of the UK including multiple modes of transport.

Accessibility maps

Figures 14 and 15 show accessibility maps for the TTWAs in the West Midlands, at the granularity of Lower Layer Super Output Areas (LSOAs). In these maps, the colour for a given LSOA shows the percentage of jobs in the rest of the region that residents in the given LSOA can reach by car within 27 minutes. The larger the proportion of the TTWA that is coloured in blue, the greater the accessibility of jobs in that region. The red markers indicate the 20 locations (LSOAs) that contain the greatest number of jobs.

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Regions in the West Midlands vary substantially both in terms of their spatial distribution of jobs and their accessibility profile. Dudley, Telford and Stafford appear to have good accessibility, while Birmingham and Hereford do not. And even areas that have a similar distribution of jobs can have vastly different accessibility profiles. For example, both Hereford and Coventry have few job clusters, which indicates that jobs are more evenly spread. However, Coventry appears to offer greater job accessibility.

Variation in accessibility

Table 9 shows the variation in the percentage of accessible jobs across LSOAs in a region. The minimum and maximum represent the worst- and best-case scenario, in terms of the percentage of jobs accessible from an LSOA. The median value shows the percentage of jobs that the residents of a ‘typical’ LSOA can reach. Finally, the standard deviation measures the variability in jobs accessibility among LSOAs.

The results suggest that the percentage of reachable jobs is consistently low in Birmingham, Hereford and Worcester and Kidderminster. In contrast, for Dudley, Telford and Coventry this indicator is generally high.

The percentage of reachable jobs varies the least in Wolverhampton and Walsall and the most in Leamington Spa. This means that in Leamington Spa the decision on where to live can have a much greater impact on job access than it does in Wolverhampton and Walsall.

Table 9. Distribution of the percentage of reachable jobs across LSOAs in TTWAs in the West Midlands.

TTWA Minimum Median Maximum Standard deviation

Birmingham 0.00 36.43 78.18 13.20

Coventry 11.81 70.97 95.67 22.25

Dudley 24.22 83.83 100.00 17.99

Hereford 3.30 53.13 82.51 23.00

Leamington Spa 0.00 69.47 96.66 26.28

Shrewsbury 0.00 52.49 100.00 13.47

Stafford 3.34 86.41 100.00 24.99

Stoke-on-Trent 0.00 79.36 100.00 31.50

Telford 0.00 100.00 100.00 26.17

Wolverhampton and Walsall 13.83 47.68 84.56 13.94 Worcester and Kidderminster 0.00 45.46 79.08 17.24

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Figure 14. Accessibility maps for 7 out of 11 TTWAs in the West Midlands. The scale bar is the same for all TTWAs.

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Figure 15. Accessibility maps for 4 out of 11 TTWAs in the West Midlands. The scale bar is the same for all TTWAs.

Job Accessibility Index

The Job Accessibility Index (JA Index) provides a summary measure of accessibility and captures the average proportion of the population in the TTWA that can reach a job within 27 minutes of car travel. It varies between 0 and 1, with 1 representing perfect accessibility. For example, a value of 0.85 means that, on average, a job can be reached by 85% of area residents.

Figure 16 shows JA index values for all TTWAs in the West Midlands. Confirming the observations made above, jobs in Telford, Dudley and Stafford appear to be the most accessible while those in Birmingham appear to be the least accessible.

Birmingham is likely disadvantaged by a more uneven distribution of job destinations among its LSOAs. The difference in JA indices between Birmingham and Telford could also be explained by Telford being a much younger city than Birmingham. As such, it is likely that Telford benefited from centralised transport infrastructure planning. In contrast, Birmingham is a much older city that has been growing over the course of hundreds of years.

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Figure 16. JA index values for all TTWAs in the West Midlands.

Figure 17a shows the inverse relationship between the JA Index and the average distance between pairs of LSOAs. Generally, larger TTWAs are more likely to have lower job accessibility. While this relationship is not surprising the figure also serves to show those regions that have a Job Accessibility index which is higher (e.g. Telford) or lower (e.g. Birmingham) than what would be expected given their size.

Figure 17b compares the JA index and the average commuting distance. There does not seem to be a strong relationship between the two variables. Interestingly, while Dudley and Telford both have high Job Accessibility indices, their average commuting distance differs by almost two kilometers.

One could expect that a better transport infrastructure would enable people to travel more easily and effectively to jobs that are located further away. Therefore, higher jobs accessibility in a TTWA would translate into longer average distances travelled by workers in that area. However, the weak relationship between the two measures (Figure 17b) seem to challenge this expectation. Therefore, the analysis suggests that on its own distance travelled is insufficient for measuring job accessibility.

Figure 17a (left). JA index and the average geographical distance12 between LSOAs. Figure 17b (right). JA index and the average commuting distance. Shown are all TTWAs in the West Midlands.

12 Distances are computed as the straight line distance between LSOA centroids.

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Accessibility Curve

The accessibility curve shows the percentage of jobs accessible by different percentages of the population in a given area (Figure 18). The curve disaggregates the information contained in the JA index by showing job accessibility for population subsets of different sizes. The ideal curve is when all jobs can be reached by the entire population (indicated by the dashed line at the top of the chart). More accessible TTWAs follow the ideal curve for longer. In general, a steeper descent means a larger gap between well-connected LSOAs and poorly-connected LSOAs. Furthermore, the more a curve is shifted upwards and rightwards, the more jobs in the corresponding TTWA are accessible by more of the population.

The chart shows that Telford has considerable overlap with the ideal curve, followed by a very rapid drop once the curve passes 80% of the population. Such a drop is likely explained by the presence of the M54 motorway, which cuts horizontally across the Telford TTWA. LSOAs located more than 27 minutes away from the motorway are likely to be the areas with reduced Job Accessibility.

Figure 18. Job Accessibility curves for all TTWAs in the West Midlands. The dashed black line represents the ideal curve.

Conclusion

This research has developed a novel methodology for measuring skill mismatch using big data. The methodology can provide a comprehensive picture of skill demands and skills supplied at a regional level and for a fine-grained set of skills. The research has also produced a range of measures for estimating access to jobs in a region, based on highly granular multimodal transport data.

The analysis showed that there are noticeable variations in skill composition across Great Britain. In particular, there appear to be large differences between the skill mismatches in the largest cities and those that persist in the rest of Great Britain. In cities, the largest apparent skill shortages are in Management and HR, Accounting, Software Engineering, Finance, Marketing and BI and IT Systems Design. The diversity of skills in each region also varied considerably with larger and more urban areas demonstrating greater diversity of skills supply.

The analysis of access to jobs showed that regions around London have the longest commuting distances. However, the subsequent examination of duration times suggests that

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distance travelled may be a poor proxy for job accessibility. A number of alternative measures were presented, including a Job Accessibility index, accessibility maps and an accessibility curve. These measures were calculated for the West Midlands and it was shown that Telford had slightly better access to jobs (given the typical commuting distances in the region), while access to jobs in Birmingham was slightly worse than what would be expected. While the analysis was limited to the West Midlands, the infrastructure has been built to enable the collection of information for the whole of Great Britain.

Several aspects of the research may be useful for policy makers and planners. The approach could be used to guide decisions on funding for skills provision. It could also enable skill shortages to be spotted earlier. The job accessibility metrics could be used in regional planning. Specifically, planners could identify the one-off changes to transport infrastructure that would have the greatest effect on the job accessibility curves in their regions.

There are a number of directions in which to expand the research. One avenue is to move from relative indicators of demand and supply to absolute indicators, which would enable a skill shortage to be expressed as a number of vacancies or workers. This would require reconciling the stock of supply with the flow of demand. Another direction would be to link skills to qualifications. This could be achieved by using information from job adverts and even course descriptions, and it would enable skill shortage indicators to be converted into detailed recommendations on course provision. In regards to measuring job accessibility, the 20 ‘job destinations’ used in the calculations could be replaced by modelling the distribution of employment across a region and including travel times for public transport. The final step would be to compare regional skill mismatches to the accessibility of jobs across regions, and thereby capture the contribution of transport infrastructure to skill shortages across Great Britain.

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References

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Appendices

Appendix 1. Exploration of commuting distances

This section explores the variables that might explain the variation in average commuting distances. Data was retrieved from official statistics (Prothero, 2016, Office for National Statistics, 2016b). There is little correlation between the land area of a region and the average distance commuted (Pearson correlation coefficient: -0.1168). After examining a range of socio-economic indicators, higher levels of economic activity, such as higher employment rates, appear to be weakly associated with longer commuting distances.

However, the best correlate for average commuting distance is the level of self-containment in a region. Supply-side self-containment is the percentage of residents who also work in the area, and demand-side self-containment is the percentage of the workforce that also live in the area (Prothero, 2016). Higher self-containment rates in an area indicate that fewer people are commuting outside that area.

Figure A1 shows how the average commuting distance in an area varies with the level of self- containment. Generally, the higher percentage of people who both live and work in a TTWA, the shorter the average distance to work. Indeed, darker blue points (that is, shorter commuting distances) are more prevalent with highly self-contained TTWAs (upper left corner), while the opposite is true for longer commuting distances (light green points cluster in the lower left corner).

Figure A1. Relationship between average commuting distance and the self-containment level of each TTWA. Different symbols represent different regions or countries in Great Britain.

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Appendix 2. Jobs accessibility measures: equations This section details the equations used to compute the job accessibility measures.

For each TTWA, let �denote the number of job opportunities in LSOA � and � the number of

residents in LSOA �. The proportion of jobs (�) and the proportion of residents (�) is then:

� = � / ∑ � (1)

� = � / ∑ � (2)

In Equation 1 the sum over � indicates a sum over all the LSOAs selected as destinations. In Equation 2 the sum over � indicates a sum over all the LSOAs in the TTWA. The same convention is used for all the equations that follow.

Let � represent the time it takes to travel from LSOA � to LSOA �, and � the time threshold for the journey (equal to 27 minutes in the main text). Journeys that can be made within the threshold are considered feasible journeys.

Finally, let �(�) define the Heaviside step function, that is: �(�) = 1 �� > 0, �� 0 ��ℎ��.

Job accessibility maps

For the job accessibility maps, the metric shown for each LSOA � is calculated by:

� = 100 ∑ �� − � � (3)

Job accessibility index

First, the following metric is calculated:

� = ∑ �� − � � (4)

Then, the Job Accessibility index (JA index) is given by:

�� = ∑ �� (5)

It is worth noting that, while the JA index varies between 0 and 1, its lower bound is strictly higher than 0. The minimum value for the index is given by:

� = ∑ �� (6)

Indeed, the jobs in each of the LSOAs selected as destinations can always be reached by the residents of the same LSOA within the time threshold set. Hence, the JA index can never be exactly 0. Different TTWAs will have different lower bounds for their JA indices. The lower

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bound is higher if the LSOAs with the largest number of jobs are also the most populated. It is worth remarking that this lower bound is generally very close to 0. For TTWAs in the West Midlands, it is always smaller than 0.012.

To normalise the index so that it covers the full range from 0 to 1, one would need to compute the following:

1 �� = (�� − �) (7) 1

Job accessibility curves

Finally, using the quantities � defined above (Equation 4), the job accessibility curves are calculated from the equation:

�� (�) = 100 ∑ � � � > (8)

Here � represents a population percentage, and therefore its range is between 0 and 100. The different values obtained from Equation 8 for different values of � constitute the job accessibility curve.

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Appendix 3. Composition of skill demands nationwide

Composition of skill demands by category in Great Britain between 2012-2018 (percentage)

Granular skill Broad skill category 2012 2013 2014 2015 2016 2017 2018 category (second (top level of the level of the skills skills taxonomy) taxonomy)

Accounting Business Administration 3.75 3.89 4.25 4.02 4.16 4.38 4.48

Administration and Law Business Administration 8.85 8.40 8.62 8.79 8.68 8.49 9.42

Finance Business Administration 2.54 2.52 2.57 2.38 2.28 2.42 2.67

Logistics Business Administration 5.72 5.78 5.78 5.48 5.75 5.80 6.01

Management and HR Business Administration 10.93 11.40 11.39 11.17 11.48 11.49 11.14

Design Education, Sales and 2.79 2.84 2.32 2.31 2.27 2.26 2.36 Marketing

Education, Languages Education, Sales and 4.64 4.92 4.92 4.73 4.51 4.57 4.22 and Art Marketing

Marketing Education, Sales and 2.45 2.42 2.22 2.66 2.24 2.51 2.36 Marketing

PR and Journalism Education, Sales and 1.03 0.85 0.81 0.81 0.81 0.77 0.80 Marketing

Sales Education, Sales and 15.28 14.71 14.20 13.98 14.36 13.98 13.04 Marketing

Civil Engineering and Engineering, 1.45 1.46 1.66 1.57 1.58 1.55 1.49 Design Construction and Transport

Construction, Engineering, 12.03 12.48 12.75 13.22 13.14 12.68 12.75 Maintenance and Construction and Transport Transport

Energy and Engineering, 1.30 1.33 1.29 1.21 1.11 1.05 1.10 Environmental Construction and Management Transport

Mechanical and Engineering, 5.02 4.72 4.55 4.50 4.30 4.33 4.49 Electrical Engineering Construction and Transport

Cardiovascular and Health and Social Care 0.48 0.55 0.60 0.59 0.63 0.62 0.57 Respiratory Healthcare

Caregiving and Health and Social Care 8.57 9.16 9.39 9.76 9.64 10.21 10.30 Rehabilitation

Dentistry Health and Social Care 0.41 0.38 0.37 0.36 0.43 0.32 0.34

Healthcare Health and Social Care 0.71 0.92 1.04 1.02 1.12 1.09 1.21 Administration

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Ophthalmology and Health and Social Care 0.25 0.24 0.22 0.21 0.23 0.24 0.26 Dermatology

Primary Care Health and Social Care 1.45 1.63 2.11 2.22 2.37 2.34 2.43

Surgery and Internal Health and Social Care 1.39 1.51 1.43 1.32 1.38 1.41 1.42 Medicine

Business Intelligence IT 2.11 2.10 2.04 2.06 1.86 1.73 1.61 and IT Systems Design

IT Security IT 0.14 0.10 0.13 0.14 0.18 0.19 0.19

IT Systems and Support IT 2.50 1.76 1.62 1.55 1.47 1.44 1.45

Software Engineering IT 3.06 2.88 2.76 2.98 3.04 3.13 2.86

Chemistry and Science and Research 0.51 0.46 0.42 0.41 0.45 0.43 0.45 Laboratory Techniques

General Biology Science and Research 0.03 0.03 0.04 0.04 0.04 0.04 0.04

Physics and Math Science and Research 0.21 0.19 0.17 0.15 0.15 0.18 0.19

Research Methods Science and Research 0.41 0.41 0.35 0.34 0.33 0.34 0.37

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Appendix 4. Composition of skills supply nationwide

Composition of skills supply by category in Great Britain between 2012-2018 (percentage)

Granular skill Broad skill category 2012 2013 2014 2015 2016 2017 2018 category (second (top level of the level of the skills skills taxonomy) taxonomy)

Accounting Business Administration 3.72 3.85 4.16 4.13 4.21 4.51 4.48

Administration and Law Business Administration 8.94 8.34 8.42 8.36 8.09 7.80 8.57

Finance Business Administration 2.55 2.53 2.57 2.47 2.49 2.73 2.94

Logistics Business Administration 5.50 5.65 5.64 5.38 5.69 5.74 5.91

Management and HR Business Administration 9.62 10.04 10.16 9.84 10.25 10.23 10.02

Design Education, Sales and 2.59 2.68 2.32 2.35 2.28 2.43 2.38 Marketing

Education, Languages Education, Sales and 6.16 6.34 6.44 6.33 6.32 6.42 6.09 and Art Marketing

Marketing Education, Sales and 2.32 2.31 2.22 2.76 2.40 2.78 2.52 Marketing

PR and Journalism Education, Sales and 0.96 0.81 0.81 0.82 0.85 0.87 0.86 Marketing

Sales Education, Sales and 13.41 12.86 12.46 12.50 12.74 12.31 11.66 Marketing

Civil Engineering and Engineering, 1.65 1.74 1.96 1.86 1.91 1.87 1.77 Design Construction and Transport

Construction, Engineering, 12.93 13.53 13.54 13.75 13.61 12.39 13.08 Maintenance and Construction and Transport Transport

Energy and Engineering, 1.53 1.59 1.56 1.51 1.42 1.29 1.38 Environmental Construction and Management Transport

Mechanical and Engineering, 5.25 5.29 5.11 5.10 4.95 4.84 5.10 Electrical Engineering Construction and Transport

Cardiovascular and Health and Social Care 0.55 0.58 0.60 0.59 0.61 0.66 0.59 Respiratory Healthcare

Caregiving and Health and Social Care 8.32 8.54 8.52 8.52 8.22 8.74 8.92 Rehabilitation

Dentistry Health and Social Care 0.45 0.38 0.37 0.35 0.41 0.33 0.33

Healthcare Health and Social Care 0.71 0.89 0.97 0.93 1.01 1.02 1.10 Administration

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Ophthalmology and Health and Social Care 0.26 0.25 0.21 0.21 0.21 0.24 0.25 Dermatology

Primary Care Health and Social Care 1.55 1.66 2.05 2.09 2.18 2.18 2.24

Surgery and Internal Health and Social Care 1.53 1.60 1.42 1.30 1.31 1.51 1.42 Medicine

Business Intelligence IT 2.11 2.14 2.13 2.20 2.07 2.01 1.80 and IT Systems Design

IT Security IT 0.26 0.15 0.19 0.21 0.26 0.26 0.25

IT Systems and Support IT 2.66 1.90 1.80 1.75 1.67 1.67 1.65

Software Engineering IT 3.10 3.04 3.11 3.50 3.58 3.78 3.32

Chemistry and Science and Research 0.64 0.58 0.56 0.54 0.62 0.62 0.62 Laboratory Techniques

General Biology Science and Research 0.03 0.04 0.05 0.05 0.05 0.05 0.05

Physics and Math Science and Research 0.27 0.24 0.22 0.21 0.22 0.26 0.27

Research Methods Science and Research 0.44 0.45 0.39 0.39 0.39 0.43 0.44

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Appendix 5. Composition of skill demands in regions

Composition of skill demands by category in regions and countries of Great Britain in 2018 (percentage)

East Midlands (seven largest TTWAs in the region)

Granular skill Leicester Nottingham Derby Northampton Peterborough Lincoln Mansfield category

Accounting 4.05 4.36 3.95 4.73 4.45 3.51 4.31

Administration 9.64 9.74 9.08 10.81 8.73 9.51 9.57 and Law

Finance 2.21 2.47 2.29 2.51 2.41 2.00 1.95

Logistics 6.78 5.92 6.66 7.03 6.39 5.55 6.17

Management 9.57 10.41 10.40 9.40 10.70 9.96 8.58 and HR

Design 1.96 2.03 1.80 2.05 2.19 1.49 1.54

Education, 3.74 4.26 4.26 3.32 3.21 4.36 3.68 Languages and Art

Marketing 1.94 2.22 1.86 2.11 2.30 1.54 1.59

PR and 0.63 0.71 0.60 0.67 0.78 0.54 0.52 Journalism

Sales 13.12 12.84 12.46 12.73 13.53 11.54 12.56

Civil Engineering 1.60 1.48 1.82 1.54 1.60 1.40 1.41 and Design

Construction, 16.60 12.55 15.60 14.85 13.50 15.69 17.28 Maintenance and Transport

Energy and 1.12 1.08 1.27 1.09 1.09 1.19 0.99 Environmental Management

Mechanical and 6.09 4.82 5.98 4.88 5.07 4.71 6.32 Electrical Engineering

Cardiovascular 0.47 0.65 0.53 0.59 0.62 0.80 0.63 and Respiratory Healthcare

Caregiving and 8.91 10.74 9.27 8.98 10.69 13.29 10.77 Rehabilitation

Dentistry 0.31 0.39 0.31 0.35 0.39 0.53 0.41

Healthcare 1.14 1.29 1.17 1.20 1.32 1.56 1.44 Administration

Ophthalmology 0.22 0.28 0.24 0.26 0.26 0.31 0.27 and Dermatology

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Primary Care 2.49 2.69 2.45 2.62 2.62 3.37 3.15

Surgery and 1.16 1.62 1.30 1.45 1.52 2.03 1.60 Internal Medicine

Business 1.24 1.56 1.33 1.51 1.41 0.99 1.08 Intelligence and IT Systems Design

IT Security 0.18 0.21 0.17 0.22 0.23 0.22 0.12

IT Systems and 1.36 1.51 1.39 1.52 1.55 1.13 1.17 Support

Software 2.55 2.95 2.80 2.73 2.57 1.91 2.15 Engineering

Chemistry and 0.42 0.55 0.46 0.36 0.38 0.40 0.33 Laboratory Techniques

General Biology 0.03 0.05 0.03 0.03 0.03 0.03 0.02

Physics and 0.17 0.22 0.19 0.15 0.16 0.15 0.13 Math

Research 0.42 0.32 0.32 0.32 0.32 0.27 0.28 Methods

East of England (eight largest TTWAs in the region)

Granular skill Cambridge Luton Southend Norwich Chelmsford Stevenage Ipswich Bedford category and Welwyn Garden City

Accounting 4.46 5.00 4.19 4.30 3.98 4.89 4.10 3.85

Administration 9.82 8.93 8.89 9.45 9.27 8.53 9.74 8.35 and Law

Finance 2.56 2.66 2.13 2.25 2.35 2.52 2.45 2.44

Logistics 5.96 6.65 6.69 5.78 6.10 6.60 6.82 6.06

Management 11.12 11.67 9.39 10.34 10.17 11.48 10.33 9.77 and HR

Design 2.20 1.90 1.60 1.81 1.68 1.90 1.55 1.66

Education, 3.53 3.74 4.39 2.65 4.24 2.86 3.53 7.06 Languages and Art

Marketing 2.35 2.27 1.60 1.88 1.77 2.22 1.96 1.92

PR and 0.76 0.70 0.53 0.62 0.59 0.71 0.67 0.65 Journalism

Sales 11.91 13.90 11.74 12.37 12.43 12.93 13.06 11.11

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Civil 1.60 1.52 1.48 1.58 1.52 1.58 1.78 1.70 Engineering and Design

Construction, 12.81 12.99 16.42 15.02 16.40 13.16 14.15 13.57 Maintenance and Transport

Energy and 1.27 1.03 1.06 1.27 1.17 1.24 1.16 1.15 Environmental Management

Mechanical 5.17 4.54 4.90 4.65 4.90 5.08 4.53 5.49 and Electrical Engineering

Cardiovascula 0.57 0.52 0.75 0.69 0.55 0.60 0.60 0.67 r and Respiratory Healthcare

Caregiving 9.38 9.72 11.22 12.28 11.56 10.09 11.14 11.56 and Rehabilitation

Dentistry 0.32 0.31 0.45 0.49 0.38 0.38 0.41 0.42

Healthcare 1.14 1.11 1.37 1.46 1.25 1.22 1.33 1.27 Administration

Ophthalmolog 0.23 0.24 0.31 0.28 0.25 0.24 0.25 0.26 y and Dermatology

Primary Care 2.18 2.32 3.06 2.85 2.79 2.47 2.62 2.75

Surgery and 1.36 1.30 1.85 1.74 1.38 1.48 1.50 1.62 Internal Medicine

Business 1.83 1.55 1.15 1.33 1.13 1.63 1.33 1.24 Intelligence and IT Systems Design

IT Security 0.18 0.21 0.18 0.15 0.20 0.22 0.18 0.19

IT Systems 1.73 1.48 1.35 1.31 1.21 1.63 1.36 1.43 and Support

Software 3.68 2.86 2.38 2.52 1.89 3.12 2.61 2.39 Engineering

Chemistry and 0.89 0.36 0.39 0.38 0.38 0.55 0.35 0.68 Laboratory Techniques

General 0.10 0.03 0.03 0.04 0.03 0.05 0.03 0.05 Biology

Physics and 0.33 0.16 0.16 0.14 0.15 0.21 0.15 0.29 Math

Research 0.57 0.31 0.32 0.35 0.28 0.41 0.30 0.39 Methods

London TTWA

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Granular skill category London

Accounting 5.16

Administration and Law 9.21

Finance 3.50

Logistics 5.83

Management and HR 11.80

Design 4.01

Education, Languages 4.98 and Art

Marketing 3.50

PR and Journalism 1.26

Sales 12.35

Civil Engineering and 1.51 Design

Construction, 8.66 Maintenance and Transport

Energy and 1.07 Environmental Management

Mechanical and 3.71 Electrical Engineering

Cardiovascular and 0.57 Respiratory Healthcare

Caregiving and 8.78 Rehabilitation

Dentistry 0.27

Healthcare 1.08 Administration

Ophthalmology and 0.26 Dermatology

Primary Care 1.96

Surgery and Internal 1.37 Medicine

Business Intelligence 2.30 and IT Systems Design

IT Security 0.21

IT Systems and Support 1.67

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Software Engineering 3.76

Chemistry and 0.46 Laboratory Techniques

General Biology 0.04

Physics and Math 0.24

Research Methods 0.49

North East (seven largest TTWAs in the region)

Granular skill Newcastle Middlesbrough Sunderland Durham and Blyth and Darlington Hartlepool category and Stockton Bishop Ashington Auckland

Accounting 4.29 3.42 3.06 3.40 2.72 4.28 3.02

Administration 9.40 7.85 8.13 7.66 7.98 8.47 7.75 and Law

Finance 2.54 2.22 2.02 2.27 1.67 2.27 2.15

Logistics 5.70 5.30 5.22 5.01 5.37 5.44 5.01

Management 10.42 10.25 9.93 9.96 10.10 10.81 10.70 and HR

Design 2.14 1.58 1.80 1.73 1.51 1.44 1.65

Education, 4.88 6.95 5.87 6.54 5.55 5.22 8.71 Languages and Art

Marketing 2.20 1.63 1.76 1.63 1.39 1.63 1.39

PR and 0.72 0.61 0.65 0.58 0.47 0.59 0.57 Journalism

Sales 13.76 12.97 12.44 11.18 14.20 11.95 12.94

Civil 1.42 1.60 1.33 1.64 1.44 1.45 1.33 Engineering and Design

Construction, 12.87 13.73 15.21 16.23 15.19 13.24 12.75 Maintenance and Transport

Energy and 1.05 1.12 1.10 1.14 1.00 1.04 0.88 Environmental Management

Mechanical 4.67 4.78 5.01 5.94 5.29 4.31 4.36 and Electrical Engineering

Cardiovascula 0.56 0.71 0.74 0.66 0.78 0.94 0.76 r and Respiratory Healthcare

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Caregiving 10.48 12.79 13.05 12.11 13.06 13.74 13.96 and Rehabilitation

Dentistry 0.36 0.44 0.46 0.40 0.49 0.49 0.46

Healthcare 1.24 1.35 1.44 1.27 1.53 1.51 1.42 Administration

Ophthalmolog 0.27 0.31 0.33 0.27 0.32 0.35 0.30 y and Dermatology

Primary Care 2.42 2.71 3.10 2.68 3.17 3.07 2.90

Surgery and 1.40 1.74 1.85 1.61 1.89 2.30 1.90 Internal Medicine

Business 1.51 1.10 1.03 1.15 0.86 1.18 0.84 Intelligence and IT Systems Design

IT Security 0.20 0.23 0.23 0.21 0.11 0.27 0.26

IT Systems 1.54 1.23 1.31 1.25 1.09 1.19 1.17 and Support

Software 2.86 2.08 1.91 2.15 1.65 1.71 1.61 Engineering

Chemistry and 0.50 0.63 0.47 0.66 0.60 0.48 0.59 Laboratory Techniques

General 0.04 0.05 0.03 0.05 0.04 0.04 0.03 Biology

Physics and 0.22 0.25 0.19 0.26 0.21 0.19 0.26 Math

Research 0.36 0.37 0.32 0.37 0.32 0.40 0.32 Methods

North West (seven largest TTWAs in the region)

Granular skill Manchester Liverpool Warrington Preston Chester Blackburn Crewe category and Wigan

Accounting 5.02 4.04 4.51 4.50 4.32 4.16 4.65

Administration 9.93 9.75 8.74 9.24 8.50 8.33 8.84 and Law

Finance 2.91 2.48 2.36 2.48 2.29 2.33 2.45

Logistics 5.96 5.90 6.63 5.90 5.87 5.49 6.31

Management 10.17 10.67 9.75 9.73 11.34 9.15 10.38 and HR

Design 2.48 1.96 1.59 1.77 1.88 1.71 1.59

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Education, 5.28 5.35 5.28 6.02 3.74 5.89 3.20 Languages and Art

Marketing 2.54 1.87 1.85 1.87 1.94 1.85 1.94

PR and 0.86 0.66 0.61 0.66 0.61 0.62 0.59 Journalism

Sales 13.99 12.20 12.81 11.82 13.85 12.42 13.27

Civil 1.53 1.41 1.65 1.33 1.47 1.49 1.47 Engineering and Design

Construction, 11.04 12.41 14.22 13.01 15.01 16.02 14.82 Maintenance and Transport

Energy and 1.03 0.97 1.08 1.16 1.10 1.11 1.05 Environmental Management

Mechanical 4.27 4.08 5.00 3.97 4.85 5.88 4.96 and Electrical Engineering

Cardiovascula 0.55 0.79 0.59 0.73 0.58 0.66 0.58 r and Respiratory Healthcare

Caregiving 9.73 12.48 10.62 12.73 10.75 10.88 10.95 and Rehabilitation

Dentistry 0.31 0.46 0.37 0.45 0.34 0.38 0.38

Healthcare 1.22 1.44 1.28 1.52 1.26 1.27 1.23 Administration

Ophthalmolog 0.25 0.32 0.26 0.29 0.26 0.29 0.26 y and Dermatology

Primary Care 2.25 2.93 2.85 3.10 2.65 2.64 2.52

Surgery and 1.36 1.93 1.48 1.82 1.46 1.64 1.43 Internal Medicine

Business 1.67 1.26 1.33 1.33 1.30 1.22 1.50 Intelligence and IT Systems Design

IT Security 0.21 0.23 0.18 0.18 0.18 0.17 0.19

IT Systems 1.51 1.23 1.39 1.20 1.24 1.28 1.53 and Support

Software 2.85 2.07 2.59 2.20 2.23 2.15 3.15 Engineering

Chemistry and 0.45 0.49 0.44 0.42 0.44 0.43 0.32 Laboratory Techniques

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General 0.04 0.04 0.03 0.03 0.04 0.03 0.03 Biology

Physics and 0.21 0.20 0.19 0.18 0.17 0.19 0.15 Math

Research 0.37 0.39 0.31 0.34 0.32 0.31 0.28 Methods

Scotland (seven largest TTWAs in the country)

Granular skill Glasgow Edinburgh Aberdeen Motherwell Falkirk and Dunfermlin Dundee category and Airdrie Stirling e and Kirkcaldy

Accounting 4.95 4.84 3.65 3.45 3.63 3.30 3.30

Administration 9.59 9.90 8.70 9.20 8.36 8.27 8.82 and Law

Finance 2.64 2.90 2.15 1.89 2.00 1.83 1.83

Logistics 6.04 5.66 5.62 6.65 5.94 5.19 5.62

Management 11.15 13.29 11.46 9.73 11.49 11.32 12.12 and HR

Design 2.50 2.48 2.19 1.18 1.78 1.17 1.87

Education, 2.93 2.85 3.54 2.94 3.60 5.04 2.89 Languages and Art

Marketing 2.41 2.39 1.74 1.52 1.78 1.39 1.87

PR and 0.82 0.80 0.62 0.50 0.65 0.48 0.66 Journalism

Sales 14.73 13.95 12.50 15.43 14.89 14.29 14.40

Civil 1.42 1.25 1.64 1.35 1.27 1.11 1.26 Engineering and Design

Construction, 13.02 12.37 14.23 18.31 16.70 16.15 15.77 Maintenance and Transport

Energy and 1.07 1.08 1.28 1.06 1.17 0.92 0.97 Environmental Management

Mechanical 4.14 3.20 4.87 4.50 3.81 3.53 3.59 and Electrical Engineering

Cardiovascula 0.48 0.40 0.88 0.42 0.45 0.53 0.64 r and Respiratory Healthcare

Caregiving 9.28 10.10 11.31 10.67 12.06 14.68 12.53 and Rehabilitation

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Dentistry 0.30 0.27 0.44 0.32 0.35 0.40 0.40

Healthcare 1.22 1.10 1.54 1.21 1.24 1.35 1.38 Administration

Ophthalmolog 0.23 0.21 0.35 0.22 0.23 0.24 0.27 y and Dermatology

Primary Care 2.35 2.02 2.99 3.17 2.59 2.86 2.88

Surgery and 1.20 1.00 2.14 1.12 1.12 1.36 1.59 Internal Medicine

Business 1.77 1.97 1.22 0.87 1.02 0.86 1.00 Intelligence and IT Systems Design

IT Security 0.18 0.20 0.20 0.27 0.12 0.21 0.18

IT Systems 1.54 1.48 1.28 1.23 1.00 1.07 1.16 and Support

Software 3.08 3.22 2.20 2.06 1.73 1.70 2.03 Engineering

Chemistry and 0.41 0.44 0.57 0.35 0.49 0.33 0.43 Laboratory Techniques

General 0.04 0.05 0.05 0.03 0.04 0.02 0.04 Biology

Physics and 0.18 0.19 0.21 0.12 0.17 0.14 0.15 Math

Research 0.36 0.39 0.44 0.23 0.30 0.23 0.34 Methods

South East (seven largest TTWAs in the region)

Granular skill Slough and Southampton Crawley Guildford Oxford Reading Medway category Heathrow and Aldershot

Accounting 4.68 3.81 5.00 4.29 3.97 4.68 4.19

Administration 9.16 9.74 9.41 9.93 9.22 9.32 9.97 and Law

Finance 2.64 2.40 2.51 2.31 2.28 2.85 2.40

Logistics 6.37 5.59 6.51 5.79 6.04 6.14 6.63

Management 12.27 11.42 11.71 11.92 12.79 11.85 10.15 and HR

Design 2.09 1.93 1.79 1.96 1.88 2.06 1.55

Education, 4.54 4.15 2.88 3.53 3.09 3.50 4.98 Languages

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and Art

Marketing 2.43 1.90 2.07 2.11 2.26 2.66 1.94

PR and 0.77 0.62 0.66 0.68 0.74 0.78 0.61 Journalism

Sales 13.85 12.94 13.03 13.79 13.98 13.94 13.50

Civil 1.38 1.50 1.36 1.27 1.44 1.55 1.54 Engineering and Design

Construction, 12.31 12.93 14.09 12.68 12.66 11.99 14.79 Maintenance and Transport

Energy and 1.02 1.11 1.02 1.02 1.09 1.12 1.07 Environmental Management

Mechanical 3.83 4.19 3.96 3.64 4.30 4.40 4.53 and Electrical Engineering

Cardiovascula 0.43 0.71 0.46 0.59 0.61 0.47 0.61 r and Respiratory Healthcare

Caregiving 10.32 11.61 11.24 12.13 10.53 9.48 9.45 and Rehabilitation

Dentistry 0.27 0.42 0.32 0.37 0.35 0.28 0.34

Healthcare 1.10 1.40 1.21 1.30 1.25 1.05 1.23 Administration

Ophthalmolog 0.22 0.30 0.24 0.28 0.26 0.24 0.27 y and Dermatology

Primary Care 2.18 2.79 2.57 2.66 2.49 2.12 2.71

Surgery and 1.09 1.74 1.20 1.48 1.50 1.16 1.52 Internal Medicine

Business 1.59 1.38 1.49 1.34 1.48 1.89 1.24 Intelligence and IT Systems Design

IT Security 0.19 0.24 0.16 0.17 0.18 0.19 0.21

IT Systems 1.53 1.43 1.42 1.33 1.40 1.77 1.37 and Support

Software 2.75 2.61 2.97 2.51 2.81 3.46 2.30 Engineering

Chemistry and 0.43 0.50 0.29 0.39 0.63 0.44 0.42 Laboratory Techniques

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General 0.04 0.05 0.03 0.04 0.07 0.04 0.03 Biology

Physics and 0.19 0.20 0.13 0.16 0.23 0.20 0.18 Math

Research 0.34 0.39 0.28 0.33 0.45 0.37 0.30 Methods

South West (seven largest TTWAs in the region)

Granular skill Bristol Exeter Swindon Plymouth Bournemouth Gloucester Trowbridge category

Accounting 4.64 4.09 4.37 3.56 3.93 4.23 3.93

Administration 10.28 11.02 9.41 8.68 9.69 9.26 9.87 and Law

Finance 2.90 2.30 2.49 2.12 2.34 2.28 2.14

Logistics 6.63 6.07 6.41 5.50 5.36 6.23 6.04

Management 11.37 10.57 10.67 9.85 11.17 11.09 10.53 and HR

Design 1.92 1.50 1.91 1.95 2.33 1.79 1.45

Education, 3.09 4.00 3.05 4.40 3.22 2.75 2.64 Languages and Art

Marketing 2.32 1.83 2.15 1.75 2.09 1.87 1.72

PR and 0.74 0.62 0.66 0.60 0.71 0.64 0.56 Journalism

Sales 12.96 13.36 13.82 12.16 13.83 12.73 13.49

Civil 1.78 1.48 1.64 1.63 1.22 1.58 1.46 Engineering and Design

Construction, 12.25 15.50 14.21 16.54 12.83 15.18 15.25 Maintenance and Transport

Energy and 1.20 1.19 1.17 1.23 0.91 1.24 1.05 Environmental Management

Mechanical 4.76 4.35 5.13 5.50 3.81 5.73 4.99 and Electrical Engineering

Cardiovascula 0.53 0.57 0.52 0.81 0.60 0.51 0.56 r and Respiratory Healthcare

Caregiving 9.27 10.06 9.76 10.77 13.35 10.84 12.10 and Rehabilitation

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Dentistry 0.31 0.36 0.30 0.42 0.45 0.35 0.43

Healthcare 1.15 1.23 1.11 1.33 1.43 1.21 1.32 Administration

Ophthalmolog 0.23 0.26 0.26 0.34 0.29 0.23 0.25 y and Dermatology

Primary Care 2.37 2.58 2.41 2.85 2.68 2.52 2.77

Surgery and 1.31 1.41 1.30 1.99 1.51 1.30 1.43 Internal Medicine

Business 1.79 1.20 1.61 1.16 1.34 1.34 1.23 Intelligence and IT Systems Design

IT Security 0.20 0.15 0.21 0.15 0.17 0.20 0.16

IT Systems 1.59 1.25 1.54 1.30 1.27 1.43 1.40 and Support

Software 3.36 2.17 2.97 2.32 2.66 2.67 2.48 Engineering

Chemistry and 0.44 0.41 0.39 0.48 0.30 0.35 0.33 Laboratory Techniques

General 0.04 0.03 0.03 0.05 0.03 0.03 0.03 Biology

Physics and 0.19 0.15 0.17 0.18 0.14 0.14 0.13 Math

Research 0.38 0.30 0.32 0.38 0.30 0.29 0.26 Methods

Wales (seven largest TTWAs in the country)

Granular skill Cardiff Swansea Newport Merthyr Llanelli Bridgend Wrexham category Tydfil

Accounting 4.29 4.33 3.97 3.80 2.95 4.30 3.67

Administration 10.36 8.61 8.60 9.14 8.02 8.87 8.07 and Law

Finance 2.73 2.30 2.28 2.24 1.70 2.07 1.95

Logistics 5.57 5.88 6.46 5.52 5.27 5.91 5.58

Management 11.34 10.99 10.21 9.80 11.01 9.72 9.33 and HR

Design 2.19 1.63 1.45 1.31 1.08 1.51 1.62

Education, 5.03 4.16 4.20 6.47 6.07 4.09 3.71 Languages and Art

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Marketing 2.12 1.73 1.68 1.40 1.32 1.56 1.68

PR and 0.75 0.58 0.54 0.47 0.47 0.54 0.61 Journalism

Sales 13.14 13.51 12.75 12.33 14.30 12.91 10.57

Civil 1.44 1.31 1.51 1.36 1.06 1.48 1.50 Engineering and Design

Construction, 11.41 13.80 15.22 13.80 13.05 15.87 15.41 Maintenance and Transport

Energy and 1.07 0.97 1.06 0.95 0.82 1.06 1.09 Environmental Management

Mechanical 4.10 4.67 5.60 5.52 3.63 5.23 6.19 and Electrical Engineering

Cardiovascula 0.61 0.74 0.61 0.80 1.07 0.72 0.95 r and Respiratory Healthcare

Caregiving 11.02 11.81 11.05 12.21 14.98 11.63 14.06 and Rehabilitation

Dentistry 0.34 0.41 0.37 0.42 0.58 0.41 0.51

Healthcare 1.27 1.41 1.24 1.53 1.81 1.33 1.61 Administration

Ophthalmolog 0.28 0.33 0.27 0.31 0.43 0.31 0.33 y and Dermatology

Primary Care 2.49 2.92 2.74 3.43 3.65 2.83 3.45

Surgery and 1.51 1.83 1.50 1.98 2.67 1.70 2.36 Internal Medicine

Business 1.54 1.26 1.36 1.04 0.72 1.21 1.10 Intelligence and IT Systems Design

IT Security 0.19 0.20 0.18 0.16 0.09 0.17 0.22

IT Systems 1.37 1.24 1.39 1.11 0.87 1.34 1.25 and Support

Software 2.67 2.32 2.84 1.74 1.43 2.19 2.11 Engineering

Chemistry and 0.52 0.47 0.41 0.57 0.40 0.50 0.49 Laboratory Techniques

General 0.05 0.04 0.03 0.03 0.03 0.04 0.04 Biology

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Physics and 0.21 0.19 0.17 0.22 0.15 0.17 0.17 Math

Research 0.40 0.38 0.30 0.36 0.36 0.34 0.39 Methods

West Midlands (seven largest TTWAs in the region)

Granular skill Birmingham Wolverhampton Coventry Dudley Stoke-on- Worcester and Leamington category and Walsall Trent Kidderminster Spa

Accounting 4.81 4.00 4.51 3.47 3.53 3.98 4.73

Administration 10.01 9.10 9.67 7.97 8.58 9.50 10.98 and Law

Finance 2.96 2.19 2.47 2.14 2.07 2.05 2.50

Logistics 6.60 6.22 6.81 6.06 6.34 5.80 5.88

Management 10.90 9.39 9.77 8.54 9.40 10.53 11.78 and HR

Design 2.23 1.48 1.71 1.51 1.84 1.45 2.09

Education, 4.59 5.14 4.52 5.27 4.07 2.79 2.86 Languages and Art

Marketing 2.53 1.74 2.05 1.71 1.84 1.70 2.13

PR and 0.81 0.55 0.64 0.55 0.60 0.56 0.70 Journalism

Sales 13.34 11.72 11.99 10.48 12.31 11.59 12.73

Civil 1.77 1.53 1.68 1.77 1.49 1.38 1.66 Engineering and Design

Construction, 12.00 17.29 15.75 19.79 17.01 16.99 14.56 Maintenance and Transport

Energy and 1.24 1.11 1.18 1.28 1.01 1.08 1.15 Environmental Management

Mechanical 5.23 6.22 6.31 8.25 5.75 5.73 5.25 and Electrical Engineering

Cardiovascula 0.46 0.52 0.44 0.42 0.58 0.55 0.39 r and Respiratory Healthcare

Caregiving 8.34 10.24 8.09 9.85 11.28 12.20 8.75 and Rehabilitation

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Dentistry 0.26 0.34 0.27 0.29 0.40 0.41 0.26

Healthcare 1.05 1.14 1.01 0.99 1.28 1.30 1.03 Administration

Ophthalmolog 0.21 0.23 0.19 0.18 0.23 0.23 0.19 y and Dermatology

Primary Care 2.05 2.69 2.42 2.30 2.79 2.88 2.10

Surgery and 1.12 1.27 1.11 1.05 1.45 1.42 1.00 Internal Medicine

Business 1.75 1.17 1.55 1.18 1.16 1.19 1.63 Intelligence and IT Systems Design

IT Security 0.19 0.15 0.23 0.16 0.18 0.16 0.22

IT Systems 1.53 1.30 1.56 1.40 1.39 1.28 1.51 and Support

Software 2.91 2.41 3.01 2.43 2.59 2.46 2.92 Engineering

Chemistry and 0.48 0.41 0.48 0.44 0.38 0.35 0.44 Laboratory Techniques

General 0.04 0.03 0.04 0.02 0.03 0.03 0.04 Biology

Physics and 0.22 0.18 0.21 0.19 0.16 0.13 0.19 Math

Research 0.36 0.27 0.33 0.26 0.28 0.28 0.33 Methods

Yorkshire and the Humber (seven largest TTWAs in the region)

Granular skill Leeds Sheffield Hull Bradfor York Wakefield and Huddersfiel category d Castleford d

Accounting 5.49 4.47 4.51 4.19 4.02 4.46 3.45

Administration 11.03 9.73 9.12 8.71 9.72 9.68 8.08 and Law

Finance 3.23 2.48 2.33 2.41 2.36 2.33 2.09

Logistics 6.29 6.11 6.07 5.92 5.79 6.44 5.55

Management 10.64 10.12 10.58 10.07 12.67 9.84 11.07 and HR

Design 2.45 1.93 1.95 1.76 2.09 1.81 1.71

Education, 4.15 4.92 5.13 5.41 4.18 4.75 5.26 Languages and Art

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Marketing 2.78 2.06 1.82 1.96 2.07 1.78 1.68

PR and 0.92 0.68 0.62 0.66 0.70 0.62 0.57 Journalism

Sales 13.40 12.88 12.59 12.89 13.87 11.95 13.54

Civil 1.61 1.59 1.66 1.36 1.25 1.48 1.41 Engineering and Design

Construction, 10.17 14.25 14.17 14.21 13.32 14.31 15.79 Maintenance and Transport

Energy and 1.14 1.13 1.15 1.05 1.16 1.03 1.04 Environmental Management

Mechanical 4.71 6.02 5.39 5.37 3.96 5.27 5.61 and Electrical Engineering

Cardiovascula 0.45 0.52 0.66 0.66 0.52 0.66 0.51 r and Respiratory Healthcare

Caregiving 8.04 9.20 10.12 10.30 10.89 11.33 11.52 and Rehabilitation

Dentistry 0.25 0.30 0.36 0.34 0.30 0.41 0.37

Healthcare 1.07 1.07 1.21 1.36 1.19 1.34 1.20 Administration

Ophthalmolog 0.20 0.24 0.29 0.26 0.24 0.27 0.26 y and Dermatology

Primary Care 1.98 2.28 2.43 2.66 2.48 2.58 2.48

Surgery and 1.09 1.25 1.62 1.62 1.32 1.59 1.27 Internal Medicine

Business 2.08 1.43 1.27 1.44 1.34 1.29 1.06 Intelligence and IT Systems Design

IT Security 0.19 0.18 0.19 0.18 0.18 0.20 0.16

IT Systems 1.78 1.44 1.35 1.46 1.18 1.38 1.24 and Support

Software 3.70 2.63 2.20 2.73 2.15 2.22 2.15 Engineering

Chemistry and 0.49 0.51 0.54 0.47 0.47 0.42 0.45 Laboratory Techniques

General 0.04 0.04 0.04 0.04 0.04 0.03 0.03 Biology

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Physics and 0.23 0.21 0.21 0.21 0.18 0.18 0.19 Math

Research 0.41 0.34 0.38 0.34 0.35 0.33 0.28 Methods

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Appendix 6. Average skill mismatch ratio by skill category and by region

The tables below show the average skill mismatch ratios by skill category across regions of Great Britain. The average skill mismatch ratios for a given region are calculated as an average of skill mismatch ratios for TTWAs in that region weighted by workplace population. The tables only show those third-level skill categories that account for more than 0.5% of skill mentions in all job adverts.

North East, North West and Yorkshire and the Humber

Most granular Granular skill Broad skill North East North West Yorkshire and skill category category category (top The Humber (third level of (second level level of the the skills of the skills skills taxonomy) taxonomy) taxonomy)

Accounting Accounting Business 1.03 0.91 0.84 Admin Administration

Accounting and Accounting Business 1.02 0.90 0.82 Financial Administration Management

Payroll and Tax Accounting Business 1.00 0.83 0.76 Accounting Administration

Claims Administration Business 1.05 0.93 0.93 Administration and Law Administration

Legal Services Administration Business 1.86 1.00 0.92 and Law Administration

Office Administration Business 1.06 0.97 0.91 Administration and Law Administration

Audit and Finance Business 1.05 0.96 0.97 Compliance Administration

Financial Asset Finance Business 0.94 0.83 0.84 Management Administration

Securities Finance Business 0.93 0.84 0.83 Trading Administration

Logistics Logistics Business 0.95 0.92 0.91 Administration Administration

Procurement Logistics Business 0.97 0.93 0.90 Administration

Shipping and Logistics Business 1.01 0.99 0.96 Warehouse Administration Operations

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Supply Chain Logistics Business 0.98 0.97 0.93 Management Administration

Business Management Business 0.95 0.94 0.93 Management and HR Administration

Hr Management Management Business 0.93 0.86 0.83 and HR Administration

Retail Management Business 0.91 0.89 0.88 Management and HR Administration

Graphic and Design Education, 0.98 0.95 0.92 Digital Design Sales and Marketing

Languages Education, Education, 1.00 0.98 0.98 Languages and Sales and Art Marketing

Teaching Education, Education, 1.18 1.44 1.66 Languages and Sales and Art Marketing

Digital Marketing Education, 0.88 0.85 0.85 Marketing Sales and Marketing

Marketing Marketing Education, 0.85 0.85 0.83 Strategy and Sales and Branding Marketing

Event Planning PR and Education, 0.93 0.90 0.90 Journalism Sales and Marketing

Complex Sales Sales Education, 0.87 0.80 0.81 Sales and Marketing

General Sales Sales Education, 0.86 0.81 0.83 Sales and Marketing

Retail Sales Education, 0.88 0.85 0.87 Sales and Marketing

Civil Civil Engineering, 1.25 1.25 1.23 Engineering Engineering and Construction Design and Transport

Construction Civil Engineering, 1.23 1.23 1.18 Engineering Engineering and Construction Design and Transport

Construction Construction, Engineering, 1.69 1.84 1.74 Maintenance Construction

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and Transport and Transport

Driving and Construction, Engineering, 0.77 0.82 0.85 Automotive Maintenance Construction Maintenance and Transport and Transport

Heating, Construction, Engineering, 1.65 1.82 1.72 Ventilation and Maintenance Construction Plumbing and Transport and Transport

Welding and Construction, Engineering, 0.95 1.14 0.93 Machining Maintenance Construction and Transport and Transport

Health, Safety Energy and Engineering, 1.04 1.08 1.09 and Environmental Construction Environment Management and Transport

Design and Mechanical and Engineering, 0.96 1.10 0.95 Process Electrical Construction Engineering Engineering and Transport

Electrical Mechanical and Engineering, 1.03 1.13 1.01 Engineering Electrical Construction Engineering and Transport

Electronics Mechanical and Engineering, 1.00 1.14 0.99 Electrical Construction Engineering and Transport

Manufacturing Mechanical and Engineering, 1.00 1.12 1.03 Methods Electrical Construction Engineering and Transport

Mental Health Caregiving and Health and 0.95 0.99 1.10 Rehabilitation Social Care

Social Work and Caregiving and Health and 0.81 0.86 0.88 Caregiving Rehabilitation Social Care

Patient Healthcare Health and 0.88 0.93 0.96 Assistance and Administration Social Care Care

Oncology Surgery and Health and 0.92 0.94 1.08 Internal Social Care Medicine

Surgery Surgery and Health and 0.91 0.95 1.04 Internal Social Care Medicine

Bi and Data BI and IT IT 0.99 0.94 0.87 Warehousing Systems Design

Business BI and IT IT 1.01 0.98 0.92 Analysis and It Systems Design

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Projects

It Support IT Systems and IT 1.00 0.99 0.92 Support

Networks IT Systems and IT 1.07 1.04 1.00 Support

System IT Systems and IT 1.10 1.09 1.02 Administration Support

Data Software IT 0.97 1.00 0.91 Engineering Engineering

Software Software IT 1.05 1.05 0.93 Development Engineering

Web Software IT 1.00 1.04 0.91 Development Engineering

East Midlands, East of England and West Midlands

Most granular Granular skill Broad skill East Midlands West Midlands East of skill category category category (top England (third level of (second level level of the the skills of the skills skills taxonomy) taxonomy) taxonomy)

Accounting Accounting Business 0.88 0.94 0.91 Admin Administration

Accounting and Accounting Business 0.90 0.93 0.98 Financial Administration Management

Payroll and Tax Accounting Business 0.81 0.87 0.90 Accounting Administration

Claims Administration Business 0.97 0.95 0.97 Administration and Law Administration

Legal Services Administration Business 1.05 1.06 1.12 and Law Administration

Office Administration Business 0.93 0.95 0.93 Administration and Law Administration

Audit and Finance Business 1.03 0.99 1.09 Compliance Administration

Financial Asset Finance Business 0.93 0.88 1.02 Management Administration

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Securities Finance Business 0.91 0.86 0.95 Trading Administration

Logistics Logistics Business 0.90 0.91 0.79 Administration Administration

Procurement Logistics Business 0.94 0.90 0.98 Administration

Shipping and Logistics Business 0.86 0.88 0.80 Warehouse Administration Operations

Supply Chain Logistics Business 0.96 0.93 0.92 Management Administration

Business Management Business 0.97 0.94 0.98 Management and HR Administration

Hr Management Management Business 0.90 0.84 0.97 and HR Administration

Retail Management Business 0.89 0.86 0.81 Management and HR Administration

Graphic and Design Education, 0.99 0.92 1.19 Digital Design Sales and Marketing

Languages Education, Education, 1.07 0.96 1.13 Languages and Sales and Art Marketing

Teaching Education, Education, 1.72 1.62 2.09 Languages and Sales and Art Marketing

Digital Marketing Education, 0.92 0.87 1.01 Marketing Sales and Marketing

Marketing Marketing Education, 0.90 0.86 0.96 Strategy and Sales and Branding Marketing

Event Planning PR and Education, 0.95 0.93 1.02 Journalism Sales and Marketing

Complex Sales Sales Education, 0.87 0.86 0.91 Sales and Marketing

General Sales Sales Education, 0.85 0.88 0.85 Sales and Marketing

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Retail Sales Education, 0.89 0.91 0.87 Sales and Marketing

Civil Civil Engineering, 1.18 1.16 1.20 Engineering Engineering and Construction Design and Transport

Construction Civil Engineering, 1.20 1.14 1.28 Engineering Engineering and Construction Design and Transport

Construction Construction, Engineering, 1.64 1.74 1.55 Maintenance Construction and Transport and Transport

Driving and Construction, Engineering, 0.78 0.80 0.65 Automotive Maintenance Construction Maintenance and Transport and Transport

Heating, Construction, Engineering, 1.62 1.71 1.55 Ventilation and Maintenance Construction Plumbing and Transport and Transport

Welding and Construction, Engineering, 0.94 0.93 0.85 Machining Maintenance Construction and Transport and Transport

Health, Safety Energy and Engineering, 1.11 1.03 1.12 and Environmental Construction Environment Management and Transport

Design and Mechanical and Engineering, 0.97 0.88 1.04 Process Electrical Construction Engineering Engineering and Transport

Electrical Mechanical and Engineering, 0.98 0.94 0.99 Engineering Electrical Construction Engineering and Transport

Electronics Mechanical and Engineering, 1.00 0.94 1.04 Electrical Construction Engineering and Transport

Manufacturing Mechanical and Engineering, 1.04 0.98 1.02 Methods Electrical Construction Engineering and Transport

Mental Health Caregiving and Health and 1.00 1.06 0.99 Rehabilitation Social Care

Social Work and Caregiving and Health and 0.80 0.86 0.70 Caregiving Rehabilitation Social Care

Patient Healthcare Health and 0.83 0.91 0.77 Assistance and Administration Social Care Care

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Oncology Surgery and Health and 0.94 0.99 0.91 Internal Social Care Medicine

Surgery Surgery and Health and 0.91 1.01 0.88 Internal Social Care Medicine

Bi and Data BI and IT IT 0.98 0.91 1.06 Warehousing Systems Design

Business BI and IT IT 1.03 0.93 1.14 Analysis and It Systems Design Projects

It Support IT Systems and IT 0.94 0.93 0.97 Support

Networks IT Systems and IT 1.01 0.98 1.07 Support

System IT Systems and IT 1.07 1.03 1.13 Administration Support

Data Software IT 1.02 0.93 1.09 Engineering Engineering

Software Software IT 1.00 0.96 1.03 Development Engineering

Web Software IT 1.09 0.96 1.19 Development Engineering

Scotland and Wales

Most granular Granular skill Broad skill Scotland Wales skill category category category (top (third level of (second level level of the the skills of the skills skills taxonomy) taxonomy) taxonomy)

Accounting Accounting Business 0.75 0.94 Admin Administration

Accounting and Accounting Business 0.71 0.99 Financial Administration Management

Payroll and Tax Accounting Business 0.58 0.94 Accounting Administration

Claims Administration Business 0.82 0.99 Administration and Law Administration

Legal Services Administration Business 1.24 1.70 and Law Administration

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Office Administration Business 0.98 0.94 Administration and Law Administration

Audit and Finance Business 0.81 1.03 Compliance Administration

Financial Asset Finance Business 0.83 0.95 Management Administration

Securities Finance Business 0.75 0.89 Trading Administration

Logistics Logistics Business 0.77 0.84 Administration Administration

Procurement Logistics Business 0.87 0.91 Administration

Shipping and Logistics Business 0.90 0.89 Warehouse Administration Operations

Supply Chain Logistics Business 0.83 0.91 Management Administration

Business Management Business 0.85 0.93 Management and HR Administration

Hr Management Management Business 0.53 0.93 and HR Administration

Retail Management Business 0.76 0.82 Management and HR Administration

Graphic and Design Education, 1.26 1.16 Digital Design Sales and Marketing

Languages Education, Education, 1.20 1.09 Languages and Sales and Art Marketing

Teaching Education, Education, 2.48 1.75 Languages and Sales and Art Marketing

Digital Marketing Education, 1.02 0.91 Marketing Sales and Marketing

Marketing Marketing Education, 1.00 0.86 Strategy and Sales and Branding Marketing

Event Planning PR and Education, 0.99 0.95 Journalism Sales and Marketing

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Complex Sales Sales Education, 0.89 0.84 Sales and Marketing

General Sales Sales Education, 0.86 0.81 Sales and Marketing

Retail Sales Education, 0.80 0.80 Sales and Marketing

Civil Civil Engineering, 1.13 1.27 Engineering Engineering and Construction Design and Transport

Construction Civil Engineering, 1.14 1.25 Engineering Engineering and Construction Design and Transport

Construction Construction, Engineering, 1.33 1.86 Maintenance Construction and Transport and Transport

Driving and Construction, Engineering, 0.85 0.76 Automotive Maintenance Construction Maintenance and Transport and Transport

Heating, Construction, Engineering, 1.18 1.87 Ventilation and Maintenance Construction Plumbing and Transport and Transport

Welding and Construction, Engineering, 0.87 1.16 Machining Maintenance Construction and Transport and Transport

Health, Safety Energy and Engineering, 1.10 1.12 and Environmental Construction Environment Management and Transport

Design and Mechanical and Engineering, 1.22 1.05 Process Electrical Construction Engineering Engineering and Transport

Electrical Mechanical and Engineering, 1.01 1.12 Engineering Electrical Construction Engineering and Transport

Electronics Mechanical and Engineering, 1.14 1.06 Electrical Construction Engineering and Transport

Manufacturing Mechanical and Engineering, 0.88 1.06 Methods Electrical Construction Engineering and Transport

Mental Health Caregiving and Health and 0.99 1.02 Rehabilitation Social Care

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Social Work and Caregiving and Health and 0.81 0.86 Caregiving Rehabilitation Social Care

Patient Healthcare Health and 1.09 0.92 Assistance and Administration Social Care Care

Oncology Surgery and Health and 1.22 0.95 Internal Social Care Medicine

Surgery Surgery and Health and 1.50 0.93 Internal Social Care Medicine

Bi and Data BI and IT IT 0.94 0.93 Warehousing Systems Design

Business BI and IT IT 1.00 0.97 Analysis and It Systems Design Projects

It Support IT Systems and IT 0.85 0.93 Support

Networks IT Systems and IT 0.86 1.01 Support

System IT Systems and IT 0.83 1.03 Administration Support

Data Software IT 1.21 0.95 Engineering Engineering

Software Software IT 1.13 0.97 Development Engineering

Web Software IT 1.26 0.96 Development Engineering

London, South East and South West

Most granular Granular skill Broad skill London South East South West skill category category category (top (third level of (second level level of the the skills of the skills skills taxonomy) taxonomy) taxonomy)

Accounting Accounting Business 0.83 0.90 0.88 Admin Administration

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Accounting and Accounting Business 0.66 0.97 0.96 Financial Administration Management

Payroll and Tax Accounting Business 0.68 0.87 0.85 Accounting Administration

Claims Administration Business 0.95 0.99 0.96 Administration and Law Administration

Legal Services Administration Business 0.85 1.05 1.01 and Law Administration

Office Administration Business 0.96 0.90 0.87 Administration and Law Administration

Audit and Finance Business 0.81 1.08 1.06 Compliance Administration

Financial Asset Finance Business 0.63 1.01 0.97 Management Administration

Securities Finance Business 0.59 0.95 0.91 Trading Administration

Logistics Logistics Business 0.94 0.76 0.81 Administration Administration

Procurement Logistics Business 0.79 0.99 0.96 Administration

Shipping and Logistics Business 1.08 0.89 0.85 Warehouse Administration Operations

Supply Chain Logistics Business 0.84 0.95 0.92 Management Administration

Business Management Business 0.84 0.97 0.97 Management and HR Administration

Hr Management Management Business 0.64 0.98 0.96 and HR Administration

Retail Management Business 0.85 0.73 0.79 Management and HR Administration

Graphic and Design Education, 0.80 1.23 1.16 Digital Design Sales and Marketing

Languages Education, Education, 0.93 1.06 1.05 Languages and Sales and Art Marketing

Teaching Education, Education, 1.69 1.89 2.12 Languages and Sales and

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Art Marketing

Digital Marketing Education, 0.76 1.00 0.98 Marketing Sales and Marketing

Marketing Marketing Education, 0.69 0.96 0.94 Strategy and Sales and Branding Marketing

Event Planning PR and Education, 0.85 1.02 0.97 Journalism Sales and Marketing

Complex Sales Sales Education, 0.73 0.90 0.88 Sales and Marketing

General Sales Sales Education, 0.92 0.82 0.85 Sales and Marketing

Retail Sales Education, 0.91 0.80 0.85 Sales and Marketing

Civil Civil Engineering, 1.25 1.30 1.25 Engineering Engineering and Construction Design and Transport

Construction Civil Engineering, 1.02 1.32 1.27 Engineering Engineering and Construction Design and Transport

Construction Construction, Engineering, 2.11 1.58 1.61 Maintenance Construction and Transport and Transport

Driving and Construction, Engineering, 0.95 0.64 0.70 Automotive Maintenance Construction Maintenance and Transport and Transport

Heating, Construction, Engineering, 2.00 1.61 1.66 Ventilation and Maintenance Construction Plumbing and Transport and Transport

Welding and Construction, Engineering, 1.08 0.92 1.01 Machining Maintenance Construction and Transport and Transport

Health, Safety Energy and Engineering, 0.96 1.12 1.17 and Environmental Construction Environment Management and Transport

Design and Mechanical and Engineering, 0.91 1.15 1.13 Process Electrical Construction Engineering Engineering and Transport

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Electrical Mechanical and Engineering, 1.01 1.05 1.08 Engineering Electrical Construction Engineering and Transport

Electronics Mechanical and Engineering, 0.89 1.13 1.16 Electrical Construction Engineering and Transport

Manufacturing Mechanical and Engineering, 1.05 1.10 1.08 Methods Electrical Construction Engineering and Transport

Mental Health Caregiving and Health and 1.23 1.08 1.03 Rehabilitation Social Care

Social Work and Caregiving and Health and 1.03 0.72 0.79 Caregiving Rehabilitation Social Care

Patient Healthcare Health and 1.15 0.86 0.83 Assistance and Administration Social Care Care

Oncology Surgery and Health and 1.12 1.05 0.96 Internal Social Care Medicine

Surgery Surgery and Health and 1.15 1.01 0.93 Internal Social Care Medicine

Bi and Data BI and IT IT 0.62 1.05 1.04 Warehousing Systems Design

Business BI and IT IT 0.65 1.14 1.13 Analysis and It Systems Design Projects

It Support IT Systems and IT 0.81 0.98 1.00 Support

Networks IT Systems and IT 0.84 1.07 1.11 Support

System IT Systems and IT 0.77 1.12 1.15 Administration Support

Data Software IT 0.63 1.13 1.11 Engineering Engineering

Software Software IT 0.67 1.08 1.05 Development Engineering

Web Software IT 0.62 1.22 1.18 Development Engineering