Rhiannon Williams, one of our Data Impact Fellows, discusses how she seeks to build practical policy into data analysis in her work.
Data analysis is a powerful tool for understanding policy and how it impacts people. When starting a research project that explores a policy, we might first think about the broad population or place that the policy targets. However, in practice, the implementation of policy doesn’t always happen to everyone all at once.
Practical elements such as local conditions or the capacity for practitioners to implement a change quickly sometimes mean that policy changes happen bit by bit, affecting different places or groups of people at different times. By building the practicalities of a policy rollout into data analysis, we can often capture its effects more accurately. A more accurate reflection of the policy’s effect in turn leads to more relevant and meaningful research impacts. As a UK Data Service Impact Fellow, impact and the potential for real change drive my research process.
The research context: Universal Credit and housing problems
My recent research focuses on the relationship between the Universal Credit system and claimants’ problems meeting housing costs. The reshaping of the UK welfare system into the new Universal Credit system is a monumental change, affecting the lives of several million benefit claimants across different places, populations, and circumstances. The policy change replaces six legacy benefits with one integrated benefit, Universal Credit, to create one centralised welfare system. Research shows that Universal Credit is at times linked to increased financial hardship, with some claimants struggling to pay their rent or turning to food banks for help. The introduction of Universal Credit is having very real and often harmful effects on claimants, making research on how it works urgent and potentially very impactful.
Universal Credit’s complex timeline
A timeline of the rollout of Universal Credit
Universal Credit’s rollout has been complex, with different levels of provision coming to different local authorities at different times between 2013 and 2018. As of 2023, the rollout of Universal Credit is not fully complete. The ‘Managed Migration’ programme, through which remaining legacy benefit claimants will be moved to the Universal Credit system, is projected to take place over the next few years. This stage will particularly affect a large number of vulnerable claimants, including many claimants with disabilities. To better understand different claimants’ experiences and hopefully produce impacts that would help vulnerable groups, I wanted to build the different stages of Universal Credit into my research.
An impact-focused research approach
To achieve this, I prioritised integrating the rollout of Universal Credit in my research design. The staggered nature of the rollout provided an unusual and exciting opportunity to directly compare claimants in both the Universal Credit and legacy benefit systems. I used data from Understanding Society, a nationally representative survey of around 40,000 households covering a wide range of topics. By capturing the everyday details of UK residents’ lives and how they change over time, Understanding Society allowed me to draw on claimants’ real experiences of policy change.
The comparison groups were created by combining Understanding Society data with DWP data, enabling year-by-year insights into what benefits respondents claimed and the availability of Universal Credit in their area at that time. By building the practical policy implementation into the research design in this way, I was able to gather robust insights into how the experiences of Universal Credit claimants compared to their legacy benefit counterparts.
Research impact and implications
The findings demonstrated a higher likelihood of experiencing housing payment problems among Universal Credit claimants, particularly for people with disabilities and claimants moving from Housing Benefit to Universal Credit. As the Universal Credit system’s reach widens and its outcomes become increasingly entrenched in the lives of claimants, these vulnerable claimants will potentially encounter increased and compounding housing difficulties. By identifying and evidencing the disproportionate risk among vulnerable groups, the research findings can now support housing policymakers and practitioners shape future research and policy change to better meet the needs of claimants.
The example of Universal Credit highlights the value of building the practicalities of policy into research that looks at policy and its effect. By being specific in our research design about how a policy is carried out, we can produce more accurate and robust findings on its effects. This means that our findings will be more aligned to the real experiences of people affected by policy and are more likely to have meaningful and relevant research impacts.
You can read Rhiannon’s research on Universal Credit in the paper published in Housing Studies.
About the author
Rhiannon is a Research Associate at the University of Sheffield, working in CaCHE and the Department of Urban Studies and Planning. Her PhD was on housing insecurity at the University of Sheffield as part of the Data Analytics and Society CDT. Her research explores housing insecurity in the UK in relation to changes in welfare policy, with a particular focus on the association between Universal Credit and housing. She uses quantitative data analysis methods, including logistic regression and multilevel modelling.