Drawing on Multiple Data Sources to Improve Labour Market Statistics

Obtaining a detailed, accurate and timely understanding of the changes in the labour market helps policymakers, businesses and employees respond and make the better, strategic decisions for their respective needs. Official labour market statistics are based largely on surveys that, as well as being expensive, can be biased by non-response, subject to recall error and too small to support detailed analysis. Their advantage is that they are flexible in the data they can collect. Administrative and other new forms of data can address some of the shortcomings of survey data, providing an opportunity to observe at scale labour market behaviour on a consistent basis over a long period of time. However, such data record information for a specific purpose and this will not always be relevant more generally.  In this project, we draw on different forms of data to enhance labour market statistics and to understand aspects of the labour market impacts of COVID-19.

In this project we assess the potential for HM Revenue and Custom’s PAYE RTI (Real Time Information) data to provide new statistics, particularly on labour market flows. We also use Labour Force Survey (LFS) data to examine the impact of the COVID-19 pandemic on self-employment and on local labour markets, where we also supplement the analysis with online job vacancy data.  We conduct a case study of the creative workforce, since research suggests this has been badly impacted by COVID-19.  Assessing the scale of this impact will help inform policymakers with regard to the potential targeting of support measures for this sector.

To establish the potential for administrative data, we have been using HMRC’s PAYE RTI data to produce labour market statistics on transitions in and out of employment, job-to-job transitions and earnings mobility. These will then be compared against the corresponding (official) statistics derived from the LFS and a series of labour market statistics produced. Our research team is investigating the potential for econometric analysis of transitions using these data. We are exploring the impact of COVID-19 on the labour market, addressing two main questions. Firstly, what the impact has been on self-employment relative to paid employment and secondly, what has happened to local labour markets during this time and the extent to which they appear to rebound from the economic shock of COVID-19. We will explore these questions initially through descriptive analysis of the LFS and online job vacancy data before conducting more formal econometric modelling. Our case study of the creative workforce will focus on a subset of the population but will also examine whether the impact of COVID-19 is any different within geographically-specific creative clusters.

In partnership with the Creative Industries Policy and Evidence Centre (PEC) we have been analysing changes in the UK’s creative workforce since 2001 and in particular, the extent of transitions in and out of creative occupations and whether these dynamics differ in the UK’s creative clusters. Initial results show that during the pandemic there have been considerable variation within the creative workforce, both employed and self-employed. Those employed in IT, software and computer services appear to have fared better than the non-creative workforce, while other creative occupation groups such as Music, performing and visual arts have been harder hit with sustained falls in employment and reduced hours.