There is a fairly wide consensus in the finance literature that MGS guarantees do increase availability of funds (Cardone, 1995; Craig et al., 2005; Boocock and Shariff, 2005; Riding et al., 2007), even if this dependent on how much the lender trusts the solvency of the system (Ortega Galán, 1990). MGS may, however, also limit their guarantees to operations with high information asymmetry (Reig Pérez and Ramírez Comeig, 1998).

The empirical studies consulted confirm increased credit availability for MGS guaranteed SMEs (Beck et al., 2010). This may even occur in times of crisis (Uesugi et al., 2010), as in the Japanese market (Nitani and Riding, 2005), while, Zecchini and Ventura (2009) report a 12.4% increase in credit availability due to MGS for the Italian market, and Bartoli et al. (2013) note that guaranteed SMEs presented lower financial strain during the crisis years 2007-2009. More recently, in contrast, D’Ignazio and Menon (2013) find no significant impact on the volume of bank debt for a sample of Italian in 2008, the year of the credit crunch, although they do detect a significant increase in these firms’ long-term debt.

On the basis of the financial literature consulted, we would expect that when MGS provide guarantees this will increase SMEs Access to bank credit. While the information asymmetry faced by MGS may lead to their rationing their concession of guarantees in Spain, this is offset by their being business cooperatives which mutually guarantee each other, which therefore facilitates the assessment and monitoring of investment projects.

Various studies of the European market support the idea that MGS guaranteed credit operations lead to better conditions (IDEA, 2003; Pombo et al. 2006). This reduction in costs comes not only from the reduction in information asymmetry, but also because MGS favor stable banking relations (Peterson and Rajan 1994). However, along with the credit costs (interest rates, charges, etc.) the firm must also bear the costs incurred by the MGS guarantee, so SMEs should only resort to these guarantees when the savings on the debt interests outweigh the costs of the guarantee (Camino and Cardone, 1999). Elsewhere, Pozzolo (2004) concludes that lenders with higher probability of non-payment will demand greater guarantees even if these suppose cutting financial costs. The cost of extra financial products for SMEs may also be lower thanks to the improved banking relations fostered and consolidated by the MGS between the lenders and the firms (Berger and Udell, 1995).

Empirically, Italy is one of the most studied markets ³ . Columba et al. (2010) show that the Italian firms guaranteed by MGS obtained lower interest rates (20 basis points). Similarly, Zecchini and Ventura (2009) state that MGS can obtain credit for Italian SMEs at an interest rate of 1.5% less than similar firms not guaranteed by MGS. This improvement in financing costs has also occurred in times of crisis (D’Ignazio and Menon, 2013, and Mistrulli et al., 2011, for Italy, and Uesugi et al., 2010, for Japan).

The increase or decrease in financing costs also depends on factors like the risk of non-payment by the MGS (Cardone Riportella and Trujillo Ponce, 2007), or the firm size. In Spain, the reduction on the interest rate charged by the lender is higher than the cost of the guarantee in micro and small enterprises while the reverse holds for medium size firms (Cardone, 1995). Lower risk borrowers accept loans with high collateral and low interest rates, while riskier firms are financed without collateral but at high rates (Comeig at al., 2014). So, the findings on the effects of guarantees on financing costs seem to tip in favor cheaper costs, leading one to expect better conditions when providing a MSG guarantee. However, it should be borne in mind that in Spain the receivers of these loans (entrepreneurs, SME management and micro SMEs), who are accustomed to dealing with banks and seeking finance in one way or another, are of the opinión that MGS guaranteed operations incur high costs (García Tabuenca and Crecente Romero, 2009).

Other factors, related to a firm’s principal characteristics, need to be considered when analyzing the relation between obtaining a MSG guarantee and the availability and cost of financing for the firm. The first is Size, since smaller firms encounter greater problems in financing their investments, and at higher cost due to information asymmetry and credit rationing (Berger and Udell, 1998). We therefore expect smaller firms to have lower credit availability and at the higher cost.

The second factor is the Age of the firm. Firms mature over time and reduce their information asymmetry, either on account of the maturing of the financial cycle itself or through sustained relations with a bank, and this aids the evolution of their sources of finance (Berger and Udell, 1998). We therefore expect more mature firms to have greater credit availability and at cheaper costs.

A third factor in accessing finance and the cost of this is the Guarantees that the SME can put up. Borrowers with higher probability of providing guarantees are those with higher risk, larger loans, longer debt maturity structure, or fewer banking relationships. In the final analysis, asymmetric information and lack of guarantees will together make financing almost unobtainable (Stiglitz and Weiss, 1981). It is therefore expected that firms with greater guarantees will have more credit available and at lower cost.

We also include Leverage to measure firms’ cost of accessing financing (Hernández Cánovas and Martínez Solano, 2006). More indebted firms are expected to bear higher costs due to their higher risk of bankruptcy.

Firms’ profitability is a further factor of importance when accessing debt. More profitable firms may see their lower risk rewarded by lower interest rates on loans. As regards credit availability, more profitable firms will probably require less debt as they are better able to self-finance (García-Teruel et al., 2014).

Elsewhere, Growth opportunities reflect the need to make investments to maintain growth. De Andrés Alonso et al. (2005) show that firms with greater growth opportunities resort less to bank financing. This may be because firms prioritize maintaining the flexibility necessary to take advantage of growth opportunities. Another explanation, though, is that firms with greater growth opportunities are likely to be younger and smaller and, therefore, less likely to be able to obtain finance at an assumable cost. So, lack of financing may mean profitable investment opportunities may go begging (Petersen and Rajan, 1994), since greater growth opportunities are associated with greater risk.

Lastly, Sector is another fundamental control variable when analyzing debt availability and cost, since there are costs and benefits of debt that are typical to each branch of an activity which affect the level and cost of business indebtedness (Grinblatt and Titman, 2003: 483- 484).

The study uses information from a sample of firms that had received MGS guarantees. The only publicly available information refers to guarantees granted by the OINARRI MGS ⁴ , whose website lists the firms guaranteed in 2010. Of these firms, which are located in the Basque Autonomous Community (Spain), we selected all the limited and publicly limited companies in the industry, construction and services sectors which provided the necessary information for the study on the SABI (Sistema de Análisis de Balances Ibéricos) database for the years 2009 and 2010. The SABI database was searched for SMEs of the same characteristics within the Basque Autonomous Community. After removing firms that were inactive, those with negative own funds (technical bankruptcy), those with assets or sales equal to or less than zero, and outliers, we were left with a final sample of 3,738 firms, of which 264 had been guaranteed by the MGS.

The impact of an SME having a MGS guarantee on its credit availability is measured by the following model:

where Credit availability represents the proportion of bank debt over total assets; MGS is a dummy variable taking the value 1 if the firm has finance through a MSG guarantee and 0 otherwise; Size measures the size of the firm by calculating the logarithm of sales; Age is the number of years since the firm was established; Guarantees is a proxy for guarantees put up by the firm, calculated as the ratio of fixed tangible assets over total assets; ROA is the firm’s profitability calculated as the quotient between earnings before interest and taxes and total assets; Growth opportunities is approximated using the variation in sales, (salest-salest-1)/salest-1; finally, the three dummy variables, Industry, Construction and Services, indicate to which sector a firm belongs.

The effect of being granted a MSG guarantee on the cost of debt for SMEs is studied in the following model:

where Cost is measured as the ratio of financing expenses over bank debt; MGS is a dummy variable taking the value 1 if the firm has financing guaranteed by a MGS and 0 otherwise; Leverage is the ratio of short and long term creditors over total assets; the rest of the variables are the same as in model 1. The variable cost includes both the interest rate with the bank borne by the firm and the cost of the MGS guarantee where relevant. The estimation of models (1) ad (2) was performed with ordinary least squares and the errors are robust to heteroskedasticity.

Table 1 shows the descriptive statistics of the variables. The bank debt of the firms represents on average 25.5% of total liabilities with a cost of 6.3%, with total leverage at 59.3%. Only 7% of firms in the sample were guaranteed by a MGS. The SMEs had a mean age of 20 years and mean sales of 4,833,062 euro. Profitability was 2.5%, and the rate of change of sales was 5.9%. This low profitability can be explained by the complicated economic situation faced by firms in 2010, the year of the study. The correlations between the independent variables and the models were below 0.26, so there are no problems of multicollinearity.

Table 1.

Credit availability measures the proportion of bank debt over total assets; Cost is the ratio of financing costs over bank debt; MGS is a dummy variable that takes the value 1 when the firm has obtained financing with a MGS guarantee; Sales is the annual turnover of the firm in thousands of euro; Number of employees is the number of workers; Age is the number of years since the firm was set up; Leverage is the ratio of total debt over total assets; Guarantees is the ratio of fixed tangible assets over total assets; ROA is the firm’s profitability; Growth opportunities measures the annual variation in sales; Industry is a dummy variable that takes the value 1 when the firm belongs to the industry sector and 0 otherwise; Construction is a dummy variable that takes the value 1 when the firm belongs to the construction sector and 0 otherwise; Services is a dummy variable that takes the value 1 when the firm belongs to the services sector and 0 otherwise.

The effects of MGS guarantees on debt conditions for SMEs were examined initially through univariate analysis. Panel A in Table 2 shows the mean values for credit cost and availability for firms guaranteed by MGS and for firms not guaranteed by MGS. For greater robustness, Panel B in the same table gives the results when comparing the sample of guaranteed firms with a control sample made by matching non MGS guaranteed firms of similar characteristics with respect to the sector, assets size, level of sales and number of employees. Statistically significant differences were checked for with a means difference test based on the Student t test. When analyzing the whole sample, the results indicate, as expected, that guaranteed firms have greater access to credit, with 35.6% bank debt of their total liability, versus 24.7% for non guaranteed firms. This finding is significant at 1%. They also bear lower financing costs, 5.5% versus 6.4%, and this difference is statistically significant at 5%. So, the results seem to show better and cheaper access to finance.

Table 2.

Credit availability is the proportion of bank debt over total assets, and Cost is the ratio of financing costs over bank debt

This section analyzes the effects of the MSG guarantee on cost and availability of bank debt while controlling for firm heterogeneity. Table 3 presents the results of the ordinary least squares estimation of models 1 and 2. Columns 1 and 3 present the effects on access to bank debt with the total sample and the control sample respectively. Likewise, columns 2 and 4 show the effects of the financing cost for both samples.

The results are consistent with those of the univariate analysis. The guarantee of the MGS facilitates firms’ access to bank financing as is shown by the positive coefficient significant at 1% of the variable MGS in columns 1 and 3 Table 3, confirming the importance of the role of the MGS.

As regards cost, the results are not so clear Column 2 of Table 3 shows that SMEs with guarantees enjoyed lower financing costs (negative coefficient significant at 5%). However, the estimation in column 4 for the subsample of paired firms does not return significant results. This may be indicating that although guaranteed firms enjoy lower interest rates from banks (since the guarantee supposes less risk for the creditor), this is off set by the cost to the firms of the guarantee.

Table 3.

Credit availability (dependent variable) measures the proportion of bank debt over total assets; Cost (dependent variable) is the ratio of financing costs over bank debt; MGS is a dummy variable that takes the value 1 when the firm has obtained financing guaranteed by the MGS; Size is the logarithm of the sales; Age is the number of years since the firm was established; Leverage is the ratio of the total debt over total assets; Guarantees is the ratio of fixed tangible assets over total assets; ROA is the firm’s profitability; Growth opportunities measures the annual variation in sales; Industry is a dummy variable that takes the value 1 when the firm belongs to the industrial sector and 0 otherwise; Construction is a dummy variable that takes the value 1 when the firm belongs to the construction sector and 0 otherwise. All estimations are performed by ordinary least squares. Observations is the number of cases included in the estimation. F is the p-value of the overall significance test. R2 is the coefficient of determination. The stand error consistent with the White heteroskedacity appears in brackets.