The financing of small firms in Beijing, China: exploring the extent of credit constraints
Bibliographic record
Abstract
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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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".