中小企业融资的决定因素
——来自三个中欧国家的证据
原文作者 Ashiqur Rahman, M. Twyeafur Rahman, Jaroslav Belas 单位 莫斯特尼兹林托马斯巴塔大学企业经济系，斯特拉斯克莱德商学院经济系
摘要：本文在捷克、斯洛伐克和匈牙利这三个中欧国家的背景下，探讨了中小企业获得融资的决定因素。研究的数据来自世界银行和欧洲复兴开发银行进行的BEEPS调查。本文不仅从中小企业的角度对企业进行了实证分析，还分别对微型企业、小型企业和中型企业进行了实证分析。此外，我们还分析了各国中小企业获得融资的决定因素，以深入了解国家一级中小企业融资的差异。结果表明，微型企业和女性拥有和经营的企业面临着银行信贷短缺的问题。另一方面，我们发现抵押物质押与融资渠道之间存在正相关关系。对于中型企业，我们发现有证据表明创新型企业从银行获得了更多的信贷。实证结果还表明，尽管捷克共和国的利率与贷款规模呈负相关，贷款规模随着利率的增加而增加，特别是对于整体上的中小企业和微型企业。
关键词：融资渠道；中小企业；捷克；斯洛伐克；匈牙利
实证结果
我们提供不同公司规模和不同国家的评估结果。我们分离回归结果来理解微型、小型和中型企业的银行贷款行为。此外，本文给出了中小企业融资的跨国回归结果，以了解国家层面的差异。因此，本文的分析不仅从公司层面的差异角度，而且从国家层面，都有助于促进对中小企业融资行为的了解。
表5给出了全样本的回归结果，我们给出了企业层面分割的结果。对于中小企业，我们发现企业规模的系数在统计上显著为1%，并与我们的因变量贷款规模正相关。这表明随着企业规模的增加，贷款规模也随之增加。然而，当我们从企业规模的角度来看，这一结果并不适用于微型企业。贷款规模与微观企业之间的负相关系数表明，在我们考察的国家，微型企业从银行获得的信贷额度较低。Brancati(2015)在意大利市场的背景下也发现了类似的结果。这一结果可能表明，通过降低信息不透明度，大公司可以表现出更好的信贷质量，这有助于它们从银行获得更多贷款。因此，我们可以说，减少信息不对称可以改善企业在贷款市场的融资可能性。
在中小企业中，我们意外地发现企业年龄与贷款规模呈负相关，但在统计上并不显著。Petersen和Rajan(1995)也发现，在美国的背景下，贷款规模与企业年龄之间存在负相关关系。他们发现，成熟和老的企业需要从金融机构获得相对较少的债务，因为他们有用于投资的现金储备。而且，这一结果可以从企业资本结构理论中得到解释。这可能意味着企业已经成熟，已经进入市场很长一段时间，积累了更多的内部资产，可以将其留存收益进行投资(Myers and Majluf, 1984)。因此，年龄较大的企业从银行获得的贷款较少。当我们观察微型企业时，这一假设得到了支持。因此，我们可以说，随着微能够企业的成熟，它可以通过过去的业绩记录的形式向银行提供更多的信息，或者也可以通过与银行形成良好的关系来获得贷款(Brancati, 2015;Neuberger等人，2006)。
我们发现女性拥有公司与获得银行融资之间存在负相关关系。但在中小企业层面，这一结果在统计学上并不显著。在微型企业中发现了统计上显著的结果。因此，本研究提供了实证证据，表明女性拥有的公司从正规金融机构获得的信贷低于男性拥有的公司。我们的结果可以从不同的角度来解释。首先，这可能是由于银行刻板印象性别歧视导致女性业主获得较少的资金(Carter and Rosa, 1998)。同样，女性拥有的公司可能缺乏融资渠道，因为她们没有足够的资产可以向银行抵押(Lee等人，2015)。在我们的案例中，更相关的是，女性拥有的微型企业可能没有多少实物资产可以作为银行的抵押品，因此它们面临银行更高的信贷限制。
出乎意料的是，在中小企业或全样本中，我们没有发现创新和企业规模之间有统计学意义的结果。然而，我们发现，只有在中型企业的情况下，创新和获得融资之间在10%的水平上出现了统计上显著的积极结果。因此，我们可以推断，在我们的研究国家，创新企业没有受到商业银行的惩罚。创新和融资的积极信号表明，商业银行通过提供资金支持，确实重视企业的创新活动。也就是说，商业银行认为创新企业在市场上有更大的成长空间，因此向其提供资金。
我们发现犯罪/盗窃仅对微型企业具有统计学意义。结果表明，商业银行认为，如果微型企业因抢劫、盗窃或纵火而遭受损失，它们的风险更大，基于这一点，微型企业可以被拒绝获得更大的贷款。有理由认为，微型企业资源有限，如果它们因犯罪活动而面临额外的损失，就会严重妨碍它们的生存可能性。因此，这可能意味着，当银行对报告犯罪/盗窃影响其业务的微型公司进行评级时，会更加严格，因为这增加了他们的贷款违约概率。
研究发现，在所有企业规模中，抵押品都具有积极的信号，并且在统计上具有显著性。根据研究结果，本研究提供了额外的支持，即抵押品的可用性可以缓解中小企业融资的可能性。抵押品可能表明受检查国家的信贷质量较好，借款人对偿还贷款的能力有信心(Bester, 1987; Chan和kanatas, 1985; Besanko和Thakor, 1987)。另一方面，这可能意味着抵押品具有约束作用，因为银行愿意向中小企业放贷(Chakraborty和Hu, 2006;Menkhoff等人，2012; Brick和Palia, 2007)。因此，研究结果表明，抵押品是我国中小企业融资的重要决定因素。
表5企业规模回归结果:因变量:贷款规模
|
变量 |
中小企业 |
微型公司 |
小型企业 |
中型企业 |
|
企业规模 |
0.0127*** |
-0.4001*** |
0.052 |
0.002 |
|
(0.0042) |
(0.1362) |
(0.0336) |
(0.0079) |
|
|
企业年龄 |
-0.0535 |
0.5954** |
-0.0222 |
0.0349 |
|
(0.0693) |
(0.02698) |
(0.2183) |
(0.044) |
|
|
企业年龄的平方 |
0.0009 |
-0.0236 |
-0.0009 |
0.0084 |
|
(0.0014) |
(0.0089) |
(0.0061) |
(0.0045) |
|
|
女性(是=1) |
-0.176 |
-1.1648** |
0.5372 |
-1.042 |
|
(0.3921) |
(0.5203)) |
(0.6178) |
(0.7621) |
|
|
创新(是=1) |
0.5737 |
0.1907 |
0.387 |
1.4462* |
|
(0.405) |
(0.5607) |
(0.6525) |
(0.8079) |
|
|
犯罪/盗窃(是=1) |
0.4334 |
-1.3635* |
1.0898 |
0.0698 |
|
(0.4509) |
(0.7406) |
(0.7042) |
(0.8138) |
|
|
抵押品(是=1) |
1.6145*** |
2.5885*** |
1.4740** |
0.1084* |
|
(0.4654) |
(0.6085) |
(0.7057) |
(1.165) |
|
|
利率 |
0.0943* |
0.1052* |
0.0956 |
0.025 |
|
(0.0527) |
(0.0582) |
(0.088) |
(0.1245) |
|
|
常数 |
9.3543*** |
8.8984*** |
8.1000*** |
11.3294*** |
|
(0.9243) |
(2.0198) |
(1.9721) |
(1.913) |
|
|
企业数量 |
195 |
48 |
104 |
43 |
|
r平方(%) |
14.7 |
54.6 |
12.2 |
13.9 |
注:本表报告了整个样本公司(SMEs)和公司级细分的OLS回归模型结果。因变量为贷款金额(贷款规模)的自然对数。企业规模是指全职员工的数量(公司规模)，企业年龄是指公司运营的年限(公司年龄)。利率是对贷款收取的适当利率(利率)。其他解释变量是虚拟变量(女性、创新、犯罪/盗窃和抵押品)。显著性水平:*** plt;0.01，
** plt;0.05，* plt;0.1。资料来源:作者自己的估计
我们发现，在10%的水平上，中小企业和微型企业的利率在统计上是显著的。这说明随着利率的提高，贷款规模也在增加。这可能意味着贷款金额越高，风险越大。因此，随着贷款规模的增加，银行可能会提高利率。此外，巨额贷款可能会增加道德风险问题，因此，银行可能会收取更高的利率，以尽快获得补偿。人们可能会问，为什么银行明知有很大风险还会向借款人提供信贷?我们认为，银行间的竞争可能会影响银行向风险借款人提供信贷的决定，而高利率是贷款人增加利润率的激励。
表6国家一级回归结果:因变量:贷款规模
|
变量 |
捷克共和国 |
斯洛伐克 |
匈牙利 |
|
公司规模 |
0.0166*** |
0.0157 |
0.0106 |
|
(0.0039) |
(0.0135) |
(0.0048) |
|
|
企业年龄 |
-0.1608 |
-0.2201 |
-0.0881 |
|
(0.189) 剩余内容已隐藏，支付完成后下载完整资料 Determinants of SME Finance: Evidence from Three Central European Countries Ashiqur Rahman, M. Twyeafur Rahman, Jaroslav Belas Empirical Results We present estimation results across firm size and across countries. We separate regression results to understand bank lending behaviour for micro, small and medium firms. Moreover, the paper presents cross-country regression results for SME financing to understand the differences in country level. Therefore, the analyses of the paper have valuable attributes to foster knowledge about SME financing behaviour not only from firm-level differences perspective but also on country level. Table 5 presents the regression results for full sample and we show results from firm level segmentation perspective. With respect to the SMEs, we found that the coefficient of FIRM SIZE is statistically significant at 1 per cent and positively associated with our dependent variable which is LOAN SIZE. This indicates that as the firm size increases the loan size also increases. However, this result is not true for the micro firms when we look at it from the firm size perspective. The negative coefficient of the relationship between loan size and micro firms suggests that micro firms get lower amount of credits from banks in our examined countries. Brancati (2015) found similar results in the context of Italian market. The result may suggest that larger firms can show better credit quality by reducing information opacity and that helps them to get more loans from banks. Thus, we may say that reduction of information asymmetry can improve the financing possibilities of firms in the loan markets. In the segment of SMEs, unexpectedly, we found that FIRM AGE is negatively related to LOAN SIZE but it is not statistically significant. Petersen and Rajan (1995) also found a negative relationship between the loan size and firm age in the context of USA. They found that mature and older firms need a relatively lower amount of debt from financial institutions since they have reserve cash for investment. Moreover, this result can be interpreted from the capital structure theory of firms. It could mean that the firms which are mature and already in the markets for a long time have accumulated more internal assets and can invest their retained earnings (Myers and Majluf, 1984). As a result, firms which are older require smaller amounts of loans from banks. The hypothesis is supported when we look at the micro firms. Therefore, we can say that as a micro firm matures, it can provide more information to banks in the form of past track record or it is also able to get loans by forming a good relationship with banks (Brancati, 2015; Neuberger et al., 2006). We found a negative relationship between FEMALE ownership of firms and access to bank finance. However, the result is not statistically significant on the SMEs level. A statistically significant result is found for the micro firms. Hence, this study provides empirical evidence that women-owned firms get a lower amount of credit from the formal financial institutions than the male-owned firms do. Our results can be interpreted from different perspectives. Firstly, it might be caused by female owners receiving lower amount of finance due to the bank stereotype gender discrimination(Carter and Rosa, 1998). Similarly, women-owned firms may lack access to finance because they do not have enough assets to pledge as collateral to banks (Lee et al.,2015). In our case, it is more relevant that women-owned micro firms may have less physical assets to pledge as collateral to the bank and thus they face higher credit restrictions from banks. Unexpectedly we did not find statistically significant results between INNOVATION and LOAN SIZE in the segment of SMEs or full sample. However, we found statistically significant positive result at 10 per cent level between INNOVATION and access to finance only in the case of medium sized firms. Thus, we can infer that innovative firms are not penalized by commercial banks in our examined countries. The positive sign of innovation and access to finance signals that commercial banks do value the innovation activities of the firms by providing financial support. It could mean that commercial banks provide funds to innovative firms by assuming that innovative firms have more growth prospects in the market. We show that CRIME/THEFT is only statistically significant for micro enterprises. The result suggests that commercial banks perceive micro firms as riskier if they incur any losses due to robbery, theft or arson and based on that micro firms can be denied a larger loan. It is legitimate to argue that micro firms have limited resources and if they face additional losses because of criminal activities, it can seriously hamper their possibility of survival. Hence, it could mean that banks are stricter when rating the micro firms which reported that CRIME/THEFT had affected their business because it increases their probability of loan default. The paper found that COLLATERAL has a positive sign and the results are statistically significant across all firm sizes. According to the results, the current study provides additional support that availability of collateral can ease the financing possibility for SMEs. It is possible that collateral signals better credit quality and confidence of the borrower in loan repayment capacity in the examined countries (Bester, 1987; Chan and kanatas, 1985; Besanko and Thakor, 1987). On the other hand, it could mean that collateral has a disciplinary role and because of that banks are willing to lend to SMEs (Chakraborty and Hu, 2006; Menkhoff et al., 2012; Brick and Palia, 2007). Hence, the result suggests collateral is a significant determinant of SME finance in our examined countries. Table 5 Results of the Regressions Across Firmsrsquo; Size: D 剩余内容已隐藏，支付完成后下载完整资料 资料编号：[597872]，资料为PDF文档或Word文档，PDF文档可免费转换为Word |
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