Personal Bankruptcy Systems in the EU – Measuring Leniency
Notice bibliographique
Résumé
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.
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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,004 | 0,011 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,007 | 0,006 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,005 | 0,002 |
| Science ouverte | 0,001 | 0,003 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 0,001 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».