Retail company voluntary arrangements: A dubious remedy?
Bibliographic record
Abstract
Abstract Despite much debate on Company Voluntary Arrangements (CVA) among UK retailers, understanding of retail CVAs remains limited. There is continuing uncertainty about the uptake of CVAs, what aspects lead to successful outcomes and whether CVAs can be viewed as a remedy for struggling UK retailers. To address these questions, we developed and analysed a novel and detailed dataset of Companies House records for the population of retailers' CVAs between mid‐2012 and early 2021. We find that CVAs, despite detrimental impacts on other actors (landlords and suppliers), can be a useful tool for some retailers in adjusting to the new market conditions. The uptake of CVAs among retailers is stable, though not among large retailers. Retail CVAs help to avoid immediate business failure, but we found limited evidence of the success and efficient longer term outcome of the procedure, suggesting that alternative methods could be considered. The success and efficiency of CVA do not seem to depend on the size of the business, but there are variations in both the uptake and efficiency of CVAs across retail sub‐sectors. This suggests that a range of mechanisms are required to cater to the different needs across retail categories. Despite the market challenges, CVAs are not prolonged on average. However, longer duration CVAs seem to have a lower chance of succeeding and of being efficient implying that CVA cannot remedy fundamental business issues. Finally, we observed differences related to who oversees the procedure, suggesting that greater emphasis should be put on upskilling and selection of insolvency practitioners.
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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 | 0.022 | 0.118 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.011 | 0.001 |
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".