# TERMS OF LENDING FOR SMALL BUSINESS LINES OF CREDIT: THE ROLE OF LOAN GUARANTEES

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4 R. Posey, A. K. Reichert IJBFR Vol. 5 No Model There are four endogenous (hypothesis) variables included in this study: the presence of a loan guarantee, loan rate, the size of the LOC, and collateral requirements. These four variables are used to test a number of specific hypotheses relating to the lending process. Control variables are included to capture exogenous effects that have been previously reported in literature, such as, the effect as length of the borrowing relation and specific borrower characteristics including; leverage, cash, fixed assets, ownership structure, number of LOCs outstanding, number of lenders utilized by the borrower, whether the owner is an active manager, and the industry classification based on two-digit SIC codes. Four basic equations will be used to explore this topic. Their general form is shown below. LOCG = α 11 + β 11 RATE + β 12 LSIZEP + β 13 COLLAT + β 1n CV + ε 1 (1) RATE = α 21 + β 21 LOCG + β 22 LSIZEP + β 23 COLLAT + β 2n CV + ε 2 (2) LSIZEP = α 31 + β 31 RATE + β 32 LOCG + β 33 COLLAT + β 3n CV + ε 3 (3) COLLAT = α 31 + β 31 RATE + β 32 LOCG + β 33 LSIZEP + β 3n CV + ε 3 (4) where: LOCG is binary and indicates the presence of a loan guarantee RATE is the initial interest rate on the LOC LSIZEP is the size of the LOC as a proportion of firm assets COLLAT is binary and indicates the presence of collateral CV is the vector of control variables (see Table 1 for descriptions) Note that LOCG COLLAT are dichotomous variables, hence equations (1) and (3) are estimated using a logistic regression procedure. Ordinary least squares regressions will be used for the other two equations. In this study, the existence of simultaneity among the variables was confirmed using the Hausman test before proceeding to use a two stage least squares (2SLS) approach as employed by Brick and Palia (2007). Each of the four hypothesis variables will be regressed on all the exogenous variables in the model. In the second step, the predicted values from these first-stage regressions will be used as independent variables, replacing their respective original variables in the right hand side of equations (1)- (4). Variables not found to be consistently significant in the initial regressions are omitted from the second stage analysis. Data The data source is the 2003 Surveys of Small Business Finances (SSBF), available from the US Federal Reserve Board. A total of 4,240 firms and 1,972 variables are included in the survey. The data was collected over several months during the year. For this study, only firms whose most recent loan was a line of credit are included in the analysis. Thus term loans are excluded. Mach and Wolken (2006) report that 34.3% of firms in the 2003 survey have LOCs. The definitions of variables used in this study are provided in Table 1. Variables with missing, extreme, or illogical values (e.g. a negative cash balance) were excluded from the analysis. Table 2 provides descriptive statistics for the variables used. There are approximately 1,460 observations in the data set. Approximately 63.7% of all LOCs have loan guarantees. The average initial interest rate is 5.55%. The mean term to maturity is just under 31 months. The average size of the LOC scaled by total assets is 66.1%. The average age of the firms in the sample is 17+ years. On average, each borrowers does business with 3.9 lenders. Approximately 80% of the firms have some form of limited 94

5 The International Journal of Business and Finance Research Volume 5 Number liability, such as, subchapter S, C, or LLC s. Collateral was required in 51% of the loans, while compensating balances were required on less than 9% of the LOCs. Table 1: Variable Definitions Dependent Variables LOCG binary variable indicating a guarantee (1 = present) RATE nominal initial interest rate charged for line of credit LSIZEP dollar value of the line of credit divided by total assets COLLAT binary variable indicating collateral (1 = required) 2SLS Instrumental Variables LOCG_I Instrument for LOCG RATE_I Instrument for RATE LSIZEP_I Instrument for LSIZEP COLLAT_I Instrument for COLLAT Control Variables RATEOVRINDEX initial interest rate premium over index used FEES Fees imposed as % of loan FSIZE natural log of firm s assets EMPLOY number of full-time employees in survey year LEVERAGE ratio of debt to total assets CASH ratio of cash to total assets PPE ratio of net depreciable assets divided by total assets INC net income divided by sales OPINC operating profit divided by sales NUMLOC number of lines of credit for the firm TERM term of the line of credit in months FAGE age of the firm in years FIXED binary variable indicating fixed interest rate (1 = fixed) DISTANCE distance in miles between firm and lender RELATE length of the firm s relationship with lender in years LIMLIAB binary variable indicating limited liability legal form OWNMGR binary variable indicating presence of owner/manager NINST number of financial institutions used by the firm GRANTPCT ratio of amount granted divided by amount requested Industry 7 dummy variables for two digit SIC code groups (8 total) For 83% percent of the firms the owner and the manager of the firm are the same individual. Binary variables were used to capture the industry sector based on 2-digit SIC codes. Of the industries represented, 1% were in the mining industry, 8.2% in construction, 15.8% in manufacturing, 4.1% in transportation, 8.6% in wholesale, 16.4% in retail, 42.5% in insurance, and 3.4% in the general services sector. None of the firms had previously filed for bankruptcy and none had been delinquent on previous loans. The preponderance of the LOCs were established during 2003, with only a few exceptions. Small businesses, as defined by the U.S. government, are those having fewer than 500 employees. In the final sample, the average business has 52 employees and assets of \$6.2 million. EMPIRICAL RESULTS As discusses earlier, there are four dependent endogenous variables of interest: the presence of a loan guarantee (LOCG) and loan collateral (COLLAT), the size of the loan as a proportion of total firm assets (LSIZEP), and the initial interest rate (RATE) on the LOC. Employing a 2SLS approach, Tables 3-6 present the results of the four second-stage regressions where one endogenous variable serves as the dependent variable and three remaining endogenous variables are represented by instrumental variables generated in the three first-stage regressions. (Note that the instrumental variables are identified by the name of the endogenous variable followed by an underscore and the letter I. For example, the instrument for the collateral variable is indicated by COLLAT_I). 95

6 R. Posey, A. K. Reichert IJBFR Vol. 5 No Table 2: Summary Statistics Variable N Mean Standard Minimum Maximum Deviation LOCG RATE LSIZEP FSIZE COLLAT LEVERAGE FEES CASH INC TERM FAGE NINST GRANTPCT RELATE LIMLIAB FIXED DISTANCE COMPBAL EMPLOY RATEOVRINDEX PPE OWNMGR NUMLOC MINE CONST MANUF TRANS WHOLE RETAIL INSURE Assets ,243,037 21,430, ,741,345 trading liaboverassets ROA Quick cashovrassets Table 2 provides the mean, standard deviation, minimum, and maximum values for each of the variables. In Table 3, the dependent variable is LOCG. Independent variables include three instruments for collateral, rate, and size. None of the three endogenous instrumental variables are statistically significant. If collateral and loan guarantees serve as substitutes one would expect to see a statistically significant negative coefficient, and if they are complements a statistically significant positive coefficient would be expected. While the coefficient on COLLAT_i is positive it is not statistically significant suggesting the presence of collateral does not impact the probability for a loan guarantee. Thus, the empirical results fail to support the notion that collateral serves as either a close substitute or complement for a loan guarantee. On the other hand, the following factors significantly increase the probably that a loan will have a guarantee: 1) greater use of leverage (LEVERAGE), 2) an increase in the number of lending institutions a borrower utilizes (NINST), greater geographic distance from the lender (DISTANCE), the borrower has limited liability (LIMLIAB), and the loan carries a compensating balance requirement (COMBAL). Alternatively, the following factors reduce the likelihood of loan guarantees: 1) the larger the firm (FSIZE), the greater the firm s cash balances (CASH), the longer the lending relationship (RELATE), the LOC carries a fixed interest rate (FIXED), and the greater the level of fixed assets owned by the firm (PPE). In terms of prediction accuracy, the logistic regression produced a concordant ratio of 76.5%, a discordant ratio of 23.3% and concordant to discordant ratio of 3.3, with virtually no ties. 96

7 The International Journal of Business and Finance Research Volume 5 Number Table 3: Logistic Regression (Stage 2) with Collateral as Dependent Variable Expected Wald Parameter Sign Estimate Chi-Square Significance Intercept COLLAT_I RATE_I LSIZEP_I FSIZE *** LEVERAGE *** FEES CASH ** INC TERM FAGE *** NINST *** GRANTPCT ** RELATE *** LIMLIAB *** FIXED *** DISTANCE ** COMPBAL * EMPLOY *** RATEOVRINDEX PPE *** OWNMGR NUMLOC Concordant (%) 76.5 Somer's D Discordant (%) 23.3 Gamma Ties (%) 0.2 Tau - a Pairs c significance denoted by ***, **, * for the 1%, 5%, and 10% level, respectively. This table presents the results of a second stage logistic regression where LOCG (presence of a loan guarantee) is the dependent variable. The estimated equation is: : LOCG = α 11 + β 11RATE_I + β 12LSIZEP_I + β 13COLLAT_I + β 1nCV + ε 1 Where: RATE_I is the instrument for initial interest rate for the line of credit, LSIZEP_I is the instrument for the size of the line of credit as a proportion of firm assets, COLLAT_I is binary and is the instrument indicating the presence of a collateral requirement, and CV is the vector of control variables. In Table 4, COLLAT is regressed on the other three instrumental endogenous variables as well as the remaining independent variables. In this case all three endogenous variables are statistically significant. As mentioned above, in Table 3 the presence of collateral had no impact on the likelihood of the borrower posting a personal guarantee. In contrast, Table 4 indicates that the presence of a loan guarantee (LOCG_I) serves to reduce the likelihood of collateral being pledged against the loan. Thus, while theory would suggest that both collateral and guarantees potentially reduce the loss given default on a loan, and hence, one may substitute for the other, the evidence presented in Table 4 suggest that the relationship is asymmetric. That is, loan guarantees appear to serve as a substitute for collateral but collateral is not a substitute for a personal loan guarantee. As mentioned previously, with a guarantee the borrower is likely pledging his or her personal assets, while with collateral corporate assets are being pledged. Thus, the results suggest that in some sense, guarantees represent a higher form of security than collateral. The coefficient on the loan rate (RATE_I) is also negative suggesting collateral is implicitly priced since the borrower may reduce the loan rate by posting collateral. On the other hand, the size of the loan (LSIZEP) is positively related to the use of collateral suggesting that the lender is attempting to reduce loss given default as the size of the LOC increases. Among the remaining variables the following are positively related to the use of collateral: 1) greater firm profitability (INC), longer the loan maturity (TERM), the greater the age of the firm (FAGE), distance from borrower (DISTANCE), and the use of compensating balances (COMPBAL). The factors which tend to reduce the likelihood of using collateral are: 1) the excess of the loan over the requested loan amount (GRANTPCT), the length of the lending relationship (RELATE), the number of full-time employees (EMPLOY), the level of fixed assets (PPE), and the number of number of lines of credit outstanding (NUMLOC). In terms of accuracy, the percent of 97

8 R. Posey, A. K. Reichert IJBFR Vol. 5 No concordant observations is 79.5%, the percent of discordant observations is 20.4%, and a concordant to discordant ratio of 3.9, with virtually no ties. Table 4: Logistic Regression (Stage 2) with Collateral as Dependent Variable Expected Wald Parameter Sign Estimate Chi-Square Significance Intercept LOCG_I * RATE_I *** LSIZEP_I *** FSIZE ** LEVERAGE FEES CASH INC *** TERM *** FAGE * NINST GRANTPCT *** RELATE *** LIMLIAB FIXED DISTANCE *** COMPBAL *** EMPLOY ** RATEOVRINDEX PPE *** OWNMGR NUMLOC * Concordant (%) 79.5 Somer's D Discordant (%) 20.4 Gamma Ties (%) 0.1 Tau - a Pairs c This table presents the results of a second stage logistic regression where COLLAT (indicating presence of a collateral requirement) is the dependent variable. The estimated equation is: COLLAT = α 11 + β 11RATE_I + β 12LSIZEP_I + β 13LOCG_I + β 1nCV + ε 1 Where: RATE_I is the instrument for initial interest rate for the line of credit, LSIZEP_I is the instrument for the size of the line of credit as a proportion of firm assets, LOCG_I is binary and is the instrument indicating the presence of a loan guarantee, and CV is the vector of control variables. Significance denoted by ***, **, * for the 1%, 5%, and 10% level, respectively. In Table 5, the loan rate (RATE) is regressed on the other three endogenous variables and the remaining independent variables. Consistent with the asymmetric relationship between loan guarantees and collateral mentioned above, the loan guarantee (LOCG_i) is negatively related to the loan rate while the loan collateral is not. As mentioned earlier the presence of a loan guarantee reduces loss given default (LGD), which is a component in the risk premium a bank incorporates into the loan rate. Firm size (FSIZE) and the loan rate are negatively related suggesting that larger firms have greater bargaining power. Of the remaining explanatory variables the following have a positive and significant relationship with the loan rate: 1) loan fees (FEES), 2) length of the lending relationship (RELATE), fixed interest rate (FIXED), interest rate premium over the market rate index (RATEOVRINDEX), if the manger and owner are the same (OWNMGR), and the number of lending relationships used by the firm (NINST). The following variables are negatively related to the loan rate: 1) firm profitability (INC), 2) length of the loan (TERM), and 3) the total number of full-time employees (EMPLOY). While it may seem surprising that the longer the banking relationship the higher the interest rate, the hold-up theory of the lending relationship suggests that banks often attempt to extract economic rents from their long time customers. As the lending relationship matures both the borrower and especially the lender have invested considerable time and effort to develop the relationship. The durability of this relationship reflects a form of implicit capital both parties have invested. The lender in particular is interested in maximizing the 98

9 The International Journal of Business and Finance Research Volume 5 Number return on this investment, and hence attempts to extract economic rents in the form of higher loan rates. The adjusted R-square and F-value for the model are 0.41 and 22.9, respectively. Table 5: OLS Regression with Interest Rate as Dependent Variable Expected Parameter Variable Sign Estimate t Value Significance Intercept *** COLLAT_I LOCG_I ** LSIZEP_I? FSIZE *** LEVERAGE FEES *** CASH INC ** TERM *** FAGE RELATE *** LIMLIAB FIXED *** DISTANCE COMPBAL? EMPLOY ** RATEOVRINDEX *** PPE OWNMGR ** NUMLOC NINST *** GRANTPCT Adj. R-squared 0.41 F statistic *** This table presents the results of a second least squares (2SLS) regression where RATE (initial interest rate). The estimated equation is: : RATE = α 11 + β 11LOCG_I + β 12LSIZEP_I + β 13COLLAT_I + β 1nCV + ε 1 Where: LOCG_I is the instrument for the presence of a loan guarantee, LSIZEP_I is the instrument for the size of the line of credit as a proportion of firm assets, COLLAT_I is binary and is the instrument indicating the presence of a collateral requirement, and CV is the vector of control variables. Significance denoted by ***, **, * for the 1%, 5%, and 10% level, respectively. In Table 6, loan size as a percent of total firm assets (LSIZEP) is regressed on the other three endogenous variables. Both loan guarantee (LOCG_I) and loan rate (RATE_I) are negatively related to loan size. This may be explained by the fact that small borrowers requesting smaller loans are less diversified and hence potentially riskier, leading to both higher interest rates and a greater use of personal loan guarantees. Once again, it appears that there is an asymmetric relationship between loan guarantees and collateral, with guarantee having the stronger impact (Note that the coefficient collateral is negative but not statistically significant). Among the other explanatory variables the following have a positive relationship with loan size: 1) firm leverage (LEVERAGE), 2) loan fees (FEES), 3) firm profitability (INC), 4) limited liability (LIMLIAB), 5) number of employees (EMPLOY), and 6) the number of lending relationships (NINST). Among the explanatory variables with a negative relationship with loan size are: 1) the length of the lending relationship (RELATE), and 2) and the interest rate premium over the market rate index (RATEOVRINDEX). Once again there is evidence of a hold-up effect as demonstrated by the negative relationship between loan size and the durability of the lending relationship. One way for the lender to extract economic rents from the relationship is to make not only higher priced loans but to reduce the size of the loan. This limits the lenders risk at the expense of the borrowers financing needs. The adjusted R- square and F-value for the model are 0.32 and 17.2, respectively. Hypothesis H1 indicates that loan guarantees lower the interest rate charged. The results indicate that the presence of a loan guarantee is in fact associated with a lower rate of interest, however, when loan guarantee is the dependent variable, the loan rate is not significant. The follow-on statement in H1states that collateral lowers the interest rate is weakly supported. The presence of collateral does not 99

10 R. Posey, A. K. Reichert IJBFR Vol. 5 No significantly affect the interest rate when the loan rate is the dependent variable. However, higher interest rates are associated with a lower likelihood of collateral use when collateral is the dependent variable. Hypothesis H2, which states that lines of credit will be larger in the presence of a loan guarantee can be rejected. The coefficient on loan guarantees (LOCG_I) is negative and statistically significant where loan size is the dependent variable. In this case, loan guarantees appear to be associated with smaller, perhaps riskier loans. Consistent with this explanation, loan guarantees are associated with higher leverage. In a related fashion, hypothesis H2 also states that lines of credit will be larger when collateral is required. The insignificant empirical results suggest that the presence of collateral does not explain the size of the loan when loan size is the dependent variable. However, larger loans increase the probability that collateral will be required when COLLAT is the dependent variable. Consistent with Chakraborty and Hu (2006), the length of the banking relationship is significant is all the regressions. A longer relationship is associated with a lower probability of both a loan guarantee and collateral requirements, but is associated with higher interest rates and smaller lines of credit, which may indicate that banks are trying to earn economic rents from their long-term borrowers. However, the estimates suggest the practical effect is minimal as the coefficients are all quite small. Table 6: OLS Regression with Loan Size as the Dependent Variable Expected Parameter Variable Sign Estimate t Value Significance Intercept *** COLLAT_I LOCG_I *** RATE_I? *** FSIZE *** LEVERAGE *** FEES *** CASH INC ** TERM FAGE RELATE *** LIMLIAB *** FIXED * DISTANCE COMPBAL? EMPLOY *** RATEOVRINDEX ** PPE OWNMGR NUMLOC NINST * GRANTPCT Adj. R-squared 0.32 F statistic 17.2 *** This table presents the results of a second least squares (2SLS) regression where LSIZEP (size of line of credit over assets). The estimated equation is: LSIZEP = α 11 + β 11LOCG_I + β 12RATE_I + β 13COLLAT_I + β 1nCV + ε 1 Where: LOCG_I is the instrument for the presence of a loan guarantee, RATE_I is the instrument for the initial interest rate for the line of credit, COLLAT_I is binary and is the instrument indicating the presence of a collateral requirement, and CV is the vector of control variables. Significance denoted by ***, **, * for the 1%, 5%, and 10% level, respectively Furthermore, loan guarantees appear to be used more frequently by limited liability firms and are also used more frequently by more highly leveraged firms. On the other hand, longer banking relationships are associated with less frequent use of guarantees. The interest rate and size of the line of credit offer no significant explanation for the presence of a loan guarantee. The use of collateral also does not explain the use of loan guarantees. The interest rate charged is lower in the presence of a loan guarantee, but is not significantly affected by the use of collateral or compensating balances. Loan guarantees are associated with smaller loans, possibly an indication of lower credit quality. It appears that lower credit 100

11 The International Journal of Business and Finance Research Volume 5 Number quality is addressed though the use of loan guarantees and by limiting the size of lines of credit granted.. In this case, loan size and guarantees appear to be complementary. This provides evidence that credit rationing is taking place CONCLUSION In our research, loan guarantees are found to have a negative effect on the size of loans and also a negative effect on the interest rate of the loans. There is some evidence that loan guarantees and collateral are asymmetric substitutes as the presence of a loan guarantee lowers the likelihood of a collateral requirement but the opposite is not true. Collateral does not appear to be substitute for land guarantees. Furthermore, measures of liquidity and leverage affect the use of loan guarantees, while they do not significantly affect the use of collateral. The presence of more fixed assets lowers the likelihood of both loan guarantees and collateral. Both loan guarantees and collateral are explained, in part, by the ratio of the amount of credit granted to that applied for. However, the signs are different, so loan guarantees are more probable as the loan amount increases while collateral requirement are less likely. Perhaps this is once again a reflection that the two are substitutes. The variable GRANTPCT suggest that there is more room to bargain with collateral requirements and loan guarantees than interest rates or the final size of the line of credit. As reported by Brick & Palia (2007), there is some evidence of simultaneity among the terms of lending which if not accounted for may provide inconsistent results. Brick & Palia (2007) and others examine the effects of the strength of the lending relationships on the terms of lending. Various authors have found little or no significant effect. In this research, the length of the lending relationship is considered a proxy for the strength of the relationship. Furthermore, the model includes an additional variable, which reflects the number of lending relationships a firm relies upon. If both variables measure an important aspect of the lending relationship, their signs should be opposite. Holding all else constant, a longer relationship is presumed to indicate a stronger relationship, while a greater number of lending relationships might suggest a weaker relationship. The empirical evidence suggests that multiple banking relationships do in fact reflect a weaker lending relationship. Furthermore, for loan guarantees a longer (stronger) lending relationship is associated with a lower probability of a guarantee. For collateral, only the length of the relationship is significant, but it too indicates that a longer relationship is associated with a lower probability of collateral. In the case of the loan rate, the two variables have the same sign, while in the loan size equation a stronger relationship is associated with smaller loans, contrary to what might be expected. As mention in the beginning, the precise terms of any given loan reflect the results a complex set of negotiation between the borrower and the lender where various trade-off exist between the individual terms of lending. REFERENCES Brick, I. and Palia, D. (2007). Evidence of Jointness in the terms of relationship lending. Journal of Financial Intermediation. 16, Camino, D., and Cardone, C. (1999). The Valuation and Cost of Credit Insurance Schemes for SME: The Role of Loan Guarantees. International Small Business Journal, 17, Chakraborty, A. and Hu, C. (2006). Lending relationships in line-of-credit and non-line-of-credit loans; Evidence from collateral use in small business. Journal of Financial Intermediation, 15, Cowling, M., and Mitchell, P. (2003). Is the Small Firms Loan Guarantee Scheme Hazardous for Banks or Helpful to Small Business. Small Business Economics, 21,

12 R. Posey, A. K. Reichert IJBFR Vol. 5 No Doh, T., and Ryu, K. (2004). Analysis of Loan Guarantees among the Korean Chaebol Affiliates. International Economic Journal, 18, Glennon, D. and Nigro, P. (2005) Measuring the Default Risk of Small Business Loans: A Survival Analysis Approach. Journal of Money, Credit & Banking, 37, Mach, T. and Wolken,J. (2003). Financial Services Used by Small Businesses: Evidence from The 2003 Survey of Small Business Finances. Federal Reserve Bulletin, 92, A Petersen, M., and Rajan, R. (1994). The Benefits of Lending Relationships: Evidence from Small Business Data. Journal of Finance, 49, 3-37 Riding, A. and Haines Jr., G.(2001). Loan Guarantees: Cost of Default and Benefits to Small Firms. Journal of Business Venturing, 16, Stiglitz, J. and Weiss,A. (1981). Credit Rationing in Markets with Imperfect Information. American Economic Review, 71 Issue, BIOGRAPHY Dr. Alan K. Reichert is a Professor of Finance at Cleveland State University. He can be contacted at Department of Finance, College of Business, Cleveland State University, 2121 Euclid Avenue, Cleveland, Ohio Dr. Ray Posey is Department Chair and Professor of Business Administration, Mount Union College. He can be contacted at Department of Business Administration, Mount Union, Ohio, Alliance, Ohio

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