Who is overlooked in employment survey data? An exploration of multiple employment with administrative data

In this blog, Dr Darja Reuschke, Associate Professor at the University of Birmingham and ADR UK Research Fellow, describes how she explores different forms of multiple employment with administrative data – and why this is important for understanding today’s employment patterns.

Labour market statistics often do not fully capture the reality of modern work. Many people juggle multiple jobs, whether by combining employee roles or mixing employment with self-employment. With the rise of the gig economy, this phenomenon has become more common yet remains underexplored.

Understanding multiple employment is also key to addressing overlooked labour market trends and gender disparities. For example, part-time employment statistics may underestimate women’s working hours when multiple jobs are considered.

By using linked administrative datasets, this research will uncover forgotten labour market experiences.

The journey behind my research

My research has long focused on changes in work and employment within spatial and social contexts. A key area of interest has been self-employment, and how decisions to become self-employed are related to where people live and their household and family situation. I have primarily drawn on longitudinal survey data, which allows the study of employment changes over time and the factors shaping these outcomes. Discovering and exploring new datasets fascinates me.

A pivotal moment came when I read the paper ‘The Migration-commuting nexus in rural England. A longitudinal analysis’ (Brown et al.). It introduced me to the potential of the Annual Survey of Hours and Earnings (ASHE) dataset. This is widely used for estimations of earnings and for policies concerning the national minimum wage. As a longitudinal dataset, it allows me to study employment changes in a spatial context. From then on, ASHE was on my ‘mental list’ of datasets to explore in the future.

Before applying for an ADR UK fellowship, I had not worked with administrative datasets. When the Annual Survey of Hours and Earnings linked to Pay As You Earn (PAYE) and Self-Assessment dataset became ADR UK’s new flagship dataset, I saw an opportunity to investigate multiple employment in a new way.

My approach to using linked administrative datasets

Self-assessment tax return data includes income from self-employment, whereas ASHE, while a high-quality dataset on earnings and hours worked, does not hold information about self-employment. Through linking these sources, I can explore whether employees also receive income from self-employed work.

This type of multiple employment (whereby individuals have more than one employment) has been largely overlooked, despite its growing relevance in the gig economy. Previous research on self-employment has focused on individuals for whom self-employment is their primary source of income.  However, studying those who engage in self-employment while also working as employees, provides new insights into who participates in self-employment and why.

I will also compare this phenomenon with multiple jobholding, where people have more than one employee job. By linking ASHE records with real-time payslip data, I can explore different methods to identify multiple jobholding. This allows me to assess who is generally excluded in mainstream employment research, which typically focuses on individuals’ ‘main’ jobs.

Relevance and impact

Having more than one job or labour income stream means that people work more hours than established labour market statistics suggest. This is important in particular for understanding women’s employment. For example, if hours in multiple jobs are added up, what is counted as ‘part-time’ employment will change.

Through the lens of multiple employment, this project will therefore address gender gaps in paid work. Working with ASHE linked with PAYE (payroll) and Self-Assessment (all income sources) data will enable better understanding of shifting labour markets. Working together with the UK Women’s Budget Group as a partner on the project will also help to draw out the gender implications of my research.

Read more about Darja’s project.