Non-take-up of benefits at the start of the COVID-19 pandemic
Bibliographic record
Abstract
The benefits system – particularly Universal Credit (UC) – has played a major role in Britain’s COVID-19 response, and it is no surprise that there has been an emphasis on how well it has responded. \nMost experts so far have suggested that UC has performed well, even if historic weaknesses remain. Yet the situation of those who did not claim UC has been given little attention – particularly those who were eligible for UC but did not claim it. In this report, we present the findings of exploratory research into this group, funded by the Health Foundation. \nWe estimate there are around half a million people – our best estimate is 430,000–560,000 people – who were eligible for UC during the start of the COVID-19 pandemic but did not claim it. \nThis includes a quarter of a million (220,000) people who thought they were eligible for UC (mostly correctly) but didn’t want to claim it. One-third of those who didn’t want to claim said that this was because they did not need benefits. But more commonly, people hadn’t applied for UC because of the perceived hassle of applying (59%), including the challenge of figuring out if they were eligible, the claims process itself, or the threat of sanctions. (Indeed, an outright majority said that conditionality would put them off applying in future). A further sizeable minority (27%) didn’t claim UC because of benefits stigma. \nWe have also estimated survey respondents’ eligibility for UC — something that has never previously been done. Estimating eligibility for UC is complex and there are a number of caveats to the figure. Bearing this in mind, we estimate that 280,000–390,000 people wrongly thought they were ineligible for UC. Some people had actively considered applying for benefits and decided they weren’t eligible, but mostly people just had a ‘sense’ that they were not eligible for anything. \nSince the start of the COVID-19 pandemic, income had fallen amongst a majority of both of these groups of people not taking-up UC. To make ends meet, people relied on savings, friends/family (for more than a quarter) or borrowed from banks. Relatively small numbers had used emergency help like food banks. However, these strategies were often still not sufficient for those not taking-up UC to avoid financial strain.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.009 | 0.038 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.028 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".