Metadata for Automating Universal Credit, 2022-2023

Automating Universal Credit studies the automated and digital aspects of Universal Credit (UC), a social security benefit in the UK. Recipients mainly interact with UC staff through an online account, and their monthly entitlement is calculated by an automated, means-testing system that factors their personal circumstances and monthly income if they work. The study used qualitative longitudinal methods, which allowed us to understand how UC claimants experience UC in near real-time, including the systems' unexpected behaviours and errors. The study has achieved two main high-level findings. First, we found that Universal Credit creates temporal mandates by applying a fixed, monthly assessment period of earnings for working claimants. We developed the concept of temporal punitiveness to describe how this monthly period creates problems for claimants who are paid their wages on a weekly/bi-weekly basis; due to the rules of calculation within the system, this misalignment may lead to the loss of entitlement. Likewise, parents have a very narrow timeframe to submit for childcare reimbursement; if parents do not submit within certain parameters, they may not be reimbursed. Second, we argue that administrative burdens may originate at the technical layer that citizens interact with to receive a service. To evidence this argument, we identify how the mechanism for reporting earnings data to UC creates administrative burdens for working UC claimants: we found that several claimants' earnings data reported to UC for the automatic calculation was wrong and thus, claimants needed to start a dispute process with the DWP. A FOI request we submitted to the DWP revealed that this happens to 5-6% of claimants each year. UC’s automated tools function at times as gatekeepers to social security entitlements, raising concerns about the current design of UC as a form of social security.