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Record W4310215195 · doi:10.14267/phd.2022056

Personal Bankruptcy Systems in the EU – Measuring Leniency

2022· dissertation· en· W4310215195 on OpenAlexaboutno aff
Jens Valdemar Krenchel

Bibliographic record

Venuenot available
Typedissertation
Languageen
FieldBusiness, Management and Accounting
TopicFinancial Literacy, Pension, Retirement Analysis
Canadian institutionsnot available
Fundersnot available
KeywordsBankruptcyLegislatureOrder (exchange)BusinessActuarial scienceEconomicsAccountingPolitical scienceFinanceLaw

Abstract

fetched live from OpenAlex

Since the Second World War, there has been a massive credit expansion to consumers, also in Europe. With increased credit to consumers comes inevitably increased risk of a negative credit event, in many cases due to change of life events, such as loss of job, sickness, divorce, and death of an income earner in a family. This in turn, in Europe, has led to a regulatory wave to introduce personal bankruptcy regimes for consumers and entrepreneurs. Academic research on personal bankruptcy has distinguished between firstly discussions on personal bankruptcy regulations in themselves and are usually focused on the controversial impact thereof on society, economy, financial markets, entrepreneurship, and labour supply. Secondly ad distinctly, limited research has tried to comparatively analyse personal bankruptcy regimes across jurisdictions, in order to access their degree of leniency. Armour and Cummings (2008) evaluated the systems of various chosen countries (England, US, Germany, France, Canada) and White (2007) contrasted the bankruptcy policies based on the trade-off between providing insurance to debtors against punishing default. Walter, G. (2020) described key tenets between US and Austrian models, such as Austria and Hungary. The methodology of measuring leniency has been limited to one-time legislative changes or some elements of, in particular, the US personal bankruptcy system. • • The research carried out here, builds on previous studies, but expands both the number of countries in the study and the number of indicators to create a composite index of personal bankruptcy legislations. • • The aim of the research is to construct a composite index, which includes the characteristics and elements of EU personal bankruptcy systems in order to compare • their leniency, and to compare and rank the personal bankruptcy legislative systems of all EU countries from the leniency aspect, to analyse the differences and similarities.1 • • The result is a calculation of the composite index for 25 EU countries and the US as a benchmark, validated results, and a ranking of the countries according to the leniency of their personal bankruptcy systems. The analysis is revolving around four hypothesised explanatory factors by analysing the index scores by: grouping based on leniency characteristics, region, law origin, and the age of the regime. • • It is concluded that the systems show high heterogeneity and cannot be clustered by leniency characteristics, region or legal origin assumed based on former studies. However, there is a strong association between leniency and the age of legislation. • • Results indicate that personal bankruptcy policies in the EU are usually launched as creditor-friendly and are later shifted to a more lenient direction. • • The research underpins the more modern regulatory regime adopted by the EU in terms of the Fresh Start Directive that is currently being rolled out in member states, but would criticize the EU initiative for being insufficient in as far as it is only obligatory for an entrepreneurial fresh start, and hence insufficient as it only recommends extending the framework to consumers. • • The research also points to the need to keep revising national regulatory frameworks to account for the maturation of the personal bankruptcy process, in terms of making them more lenient, as seems to be the experience across Europe. • • Finally, the research underscores the need for further research in the area.

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 imitation

Not 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.

metaresearch head score (Codex)0.004
metaresearch head score (Gemma)0.011
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.007
Threshold uncertainty score0.022

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.011
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0070.006
Science and technology studies0.0010.001
Scholarly communication0.0050.002
Open science0.0010.003
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.001

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.

Opus teacher head0.023
GPT teacher head0.237
Teacher spread0.213 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

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Citations0
Published2022
Admission routes1
Has abstractyes

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