Notice bibliographique
Résumé
The thesis profiles SME access to finance for more than 31,000 firms in high-income and emerging markets, with financial accounting data for more than 15,000 emerging markets firms.SMEs account for nearly 40% of the total sample.The author finds (1) bank credit allocation favoring large-scale businesses versus SMEs is rational due to large firms' positive financial performance indicators, fees paid, disclosure practices and market power (demand side) as well as creditor operational efficiency and cost effectiveness from lending economies of scale (supply side); (2) despite this, large-scale lenders have an interest in financing SMEs for portfolio diversification and management of concentration risk, while mid-sized lenders often provide credit to SMEs due to the lender's more limited capital and/or business model orientation as a "stakeholder" institution; (3) multivariate statistical tests reveal less consistently positive correlation between firm size and credit access than expected, although this partly reflects testing issues and narrow bounds of SME classifications; by contrast, (4) univariate indicators show a very high level of credit access by large-scale firms that dwarf credit access of SMEs, and support arguments of firm size bias in favor of large-scale firms in credit access and "low leverage puzzle" theory.Weakness in moveable property registries makes it harder for firms to value and pledge machinery and equipment as assets for secured transactions.Correspondingly, SMEs are constrained in their access to credit, particularly LTD, because the legal and institutional environment works against them due to their dependence on machinery and equipment for operations and because these are the predominant fixed assets they have to pledge as collateral.Despite this, positive correlation of markets with high credit access and strong legal and institutional variables shows firms in stronger environments for credit information, minority shareholder protection, regulatory effectiveness, property registration, contract enforcement and insolvency resolution have better chances of accessing credit (consistent with institutional theory).More generally, legal and institutional variables are weak descriptors in relation to dependent variables, whereas financial indicators are stronger.Category of interest dummy variables for income levels, listed status and sector also showed good results, whereas regional indicators were less reliable.similar to separate research that reports lines of credit account for about 15% of total corporate assets (Lins, Servaes, & Tufano, 2010).This research claims lines of credit are used not only for working capital purposes but also to (a) exploit business opportunities as they emerge (e.g., capital investment needs, undervalued properties or securities purchases) and (b) hedge liquidity and market risks, particularly when external credit markets are poorly developed.Therefore, depending on levels of external credit market development and the specific access and preferences of firms, there are overlapping uses of lines of credit in both working capital and fixed asset investment.As for long-term debt, the sample of firms in the research reporting relevant financial accounting information shows LTD (including CMLTD) to approximate 90% of total debt when all figures are summed.For emerging markets (EM), the figures are lower at 78% of total EM debt, but higher than the 43% average long-term debt maturity profile of 24 publicly-listed
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
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 | 0,002 | 0,012 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,001 |
| Bibliométrie | 0,002 | 0,003 |
| Études des sciences et des technologies | 0,000 | 0,001 |
| Communication savante | 0,002 | 0,002 |
| Science ouverte | 0,001 | 0,002 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,017 | 0,002 |
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 ».