The measurement and regulation of shadow banking in Ireland
Bibliographic record
Abstract
Purpose The purpose of this paper is to study financial vehicle corporations (FVCs) and other special purpose vehicles (SPVs) in Ireland. Design/methodology/approach The paper is based on a database of FVCs that are a central part of the shadow banking sector in Ireland. The database is derived from a European Central Bank (ECB) list of securities and from filings in Company Registration Office, Dublin. Findings Tax concessions are very valuable and has resulted in zero or close-to-zero effective tax rates. Although described as “bankruptcy remote”, FVCs/ SPVs in Ireland are associated with several banks that failed. Central Bank data are inconsistent with revenue data and have resulted in regulatory gaps. The main economic benefit to Ireland consists of payments to certain service providers. Research limitations/implications A complete population of FVCs/SPVs has not been used. Ownership of FVCs/SPVs has not been identified with consequent implications for identifying risk to the sponsoring firm or guarantor. Practical implications The study indicates data deficiencies in Central Bank data, with consequent implications for regulation and measuring the size of the shadow banking sector, and failure of FVCs/SPVs described as bankruptcy remote. Social implications The shadow banking sector has been a key source of instability and risk transference in the recent past. Research and understanding is vital to prevent a future occurrence. Originality/value There are no publicly available databases of individual FVCs/SPVs in Ireland. Hence, research on granular data is limited. The study develops a database derived from lists of securities published by the ECB. The study also relies on a database derived from company house records.
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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 | 0.003 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".