ADR UK Research Fellows: RAPID: Population interactions with employment, benefits and pensions – UK

This project aims to explore the following research questions:

1. What proportion of adults aged 50–64 leave employment and which non-employment destinations do they enter (unemployment, inactivity, or retirement)? How do employment exit rates and destinations vary by age, gender and earnings within and across local authorities?
2. How long do adults aged 50–64 remain out of work after leaving employment and how does the duration of non-employment vary by age, gender, earnings, type of non-employment and local authority?
3. To what extent do adults aged 50–64 return to employment after a period out of work? How quickly do they return and how sustainable are these returns? How do return-to-work rates and post-return employment retention vary by age, gender, earnings and local authority?
4. How do caring responsibilities, health problems, disability and low-paid or unstable employment influence older workers' risks of leaving employment, the duration of time spent out of work and their likelihood of returning to and remaining in employment? How do these relationships differ by age and gender within and across local authorities and to what extent are they shaped by local labour-market conditions, occupational structure, social-care capacity and local health burdens?

The methodology used in this study:

• In the first stage, the study will use RAPID's monthly employment, benefit and pension records to develop a set of derived monthly labour-market measures that classify individuals into employment, unemployment, retirement and different forms of inactivity, including caring-related and health-related inactivity. These derived measures will then be used to analyse older workers' labour-market transitions, including employment exit rates, transitions into different forms of non-employment, the duration of periods spent out of work and rates of return to and retention in employment. Examining how these transitions differ by age and gender and how such differences vary within and across local authorities, will provide the first comprehensive picture of older workers' labour-market dynamics across local areas of the UK. 
• In the second stage, the project will use statistical methods to identify how individual circumstances shape employment exits, the duration of non-employment, and return-to-work and employment-retention outcomes. These individual circumstances include: 
  • caring responsibilities 
  • health problems and disability 
  • employment characteristics, such as low pay and employment instability 
  • local conditions including labour-market demand, occupational structure, social-care capacity and population health. 

Local-authority fixed effects will be used to account for persistent differences between areas, while multilevel modelling and interaction analysis will examine how individual circumstances and local contexts combine to shape labour-market outcomes. Together, these analyses will generate new evidence on how older workers' labour-market dynamics vary within and across local authorities and identify where retention and return-to-work interventions may be most needed.

This project aims to explore the following research questions:

1. What happens to people’s employment, earnings and income before, during and after they claim a health-related benefit, and how has this changed over time?
2. How do local job market conditions affect how many people start and stop claiming health-related benefits?
3. Do cuts to other, non-health-related benefits lead to more people claiming health-related benefits?

The methodology used in this study:

• To answer the first research question, this project will track large groups of people to see how their work and income change in the years before and after a benefit claim. It will use statistical models to explore which groups are most at risk of leaving work because of ill health. 
• To answer the second question, the project will study what happens when an area suffers a sudden loss of jobs (for example, the closure of the Redcar steelworks) by comparing the affected area with similar areas that were not affected. It will also explore how differences in exposure to declining industries affect economic outcomes and benefit claims in different local areas. 
• To answer the third question, the project will look at a 2011 cut to housing benefit that affected some people and areas more than others, and compare what happened to those groups to understand whether cutting one benefit pushes people towards claiming a health-related benefit. 
• Throughout, the research will combine RAPID with publicly available information on prescriptions from family doctors, hospital activity, and people’s own reports of their health from the 2011 and 2021 Censuses, to understand whether these patterns are driven by genuinely worsening health or by other factors.

This project aims to explore the following research questions:

1. How does economic inactivity vary across different local authorities and regions in the UK?
2.  What are the main reasons people become economically inactive – for example, long-term sickness, caring responsibilities, or other factors?
3.  How do individual characteristics (such as age, gender, disability, and benefit history) and local conditions (such as deprivation, job availability, or local health burdens) shape the likelihood of being economically inactive?
4.  What are the pathways out of economic inactivity? 
5.  How do these pathways vary across different local labour market contexts?

The methodology used in this study:

• The project will use the RAPID dataset to illustrate patterns of economic inactivity across different local areas through descriptive analysis. It will look at how inactivity rates vary by place and how these patterns relate to individual characteristics, such as age, disability, and caring responsibilities.
• The project will then use regression analysis and a decomposition method to explore which personal characteristics and local conditions are most strongly linked to being economically inactive, and to assess the extent to which factors contribute to differences in inactivity between areas.
• To understand the dynamics of economic inactivity – not just who is inactive, but how people move in and out of work over a time period – the project will analyse the timing and sequence of these transitions.