www.productivityinsightsnetwork.co.uk COVER PAGE [Please fill in the details below and a cover will be created for you] Report/Paper Title: Measuring Regional Skill Mismatches and Access to Jobs. Author (s): Jyldyz Djumalieva, Stef Garasto and Cath Sleeman. Thank you! 1 www.productivityinsightsnetwork.co.uk 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. 2 www.productivityinsightsnetwork.co.uk 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 3 www.productivityinsightsnetwork.co.uk 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 4 www.productivityinsightsnetwork.co.uk 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. 5 www.productivityinsightsnetwork.co.uk 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. 6 www.productivityinsightsnetwork.co.uk 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