The financing of small firms in Beijing, China: exploring the extent of credit constraints
Notice bibliographique
Résumé
Purpose – The purpose of this paper is to utilise a sample of 384 small and medium-sized enterprises (SMEs) who applied for external finance in the Beijing area of China to investigate the characteristics of firms against: the amount of external finance sought, the amount received, and the proportion of external finance which was received from the sought finance. Design/methodology/approach – The authors use a survey of SMEs in Beijing, China, undertaken between July and December 2007 where a response rate of 37.67 per cent was obtained. The survey was translated from English to Chinese, and then back translated from Chinese to English by academics with input from businesses. The sample of 384 firms is robust. Findings – Overall, there is little evidence in the sample of Chinese SMEs that innovative firms face discrimination from providers of credit. However, where innovation is measured by inputs (specifically R & D), providers of credit appear less comfortable. Three other factors were more important and were statistically significant at the 5 per cent level. For example, exporters were less likely to receive a greater proportion of their sought finance; and manufacturing firms were more likely than service sector firms, and limited liability companies were more likely than extended sole proprietorship firms to obtain a greater proportion of the external finance which they sought. Research limitations/implications – The sample for the research is from Beijing. Researchers may extent and role out the research to other parts of China. Practical implications – Practically, the authors explore variations in firm-level characteristics by: the amount of external finance sought, the amount of external finance received, and the ratio of “sought” to “received” external finance. In this way, the research questions are concerned with understanding which “types” of firms seek most bank finance, and which are most successful. This information is of benefit to SMEs, policy makers and those who work in the finance industry. Social implications – Access to finance is a cause of stress and anxiety to many SMEs. A greater understanding of the accessing of finance in Beijing China will allow entrepreneurs to be better placed to reflect upon their businesses and their suitability to pursue finance. This can help the economic and social well-being of entrepreneurs and their employees. Originality/value – There are comparatively few large scale surveys which have been undertaken of access to finance by SMEs in China, and within this field there is very little research which has been undertaken to look at innovators and non-innovators. The results allow us to have a better understanding of how much finance SMEs in Beijing are seeking, obtaining, and the proportion of finance received from that sought, and the extent to which innovation and other business and owner-manager characteristics are influential.
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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 | 0,001 | 0,005 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,002 | 0,004 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 0,000 |
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 ».