Abstract
The paper analyzes the role of guarantees on interest rates before and during the recent financial crisis in small-sized firm financing. The novelty of this work is the distinction between real and personal guarantees, and the potential different role they could have played in the bank-borrower relationship during the recent financial crisis.
This paper draws from individual Italian bank and producer households data taken from the Central Credit Register at the Bank of Italy over the period 2006–2009.
Our analysis demonstrates that collateral affects the cost of credit of small business by systematically reducing the spread of secured loans, once we control for borrower and loan risk. Personal guarantees reduce the loan interest rate only during the crisis.
The opinions presented are solely those of the authors and do not necessarily reflect the positions and views of the Bank of Italy.
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Notes
- 1.
Panetta and Signoretti (2010) show that, during recent economic and financial turmoil, the decrease of Italian bank loans has been affected both by demand and supply factors. As for firms, they show that loan demand declined following the investment contraction, while lower levels of bank loan supply were the joint result of the increase in borrowers’ risk and in the degree of bank risk aversion.
- 2.
Producer households are partnerships or sole proprietorships with a number of employees less than or equal to five.
- 3.
The Central Credit Register is regulated by the resolution adopted by the Credit Committee on 29 March 1994 pursuant to Articles 53, 67 and 107 of the Banking Law. The following participate in this centralized service:
– Banks entered in the register referred to in Article 13 of the Banking Law;
– Financial intermediaries entered in the register of banking groups and/or the special register referred to in, respectively, Articles 64 and 107 of the Banking Law that engage exclusively or primarily in financing activity. Financial intermediaries more than 50% of whose financing activity consists of consumer credit are exempted. Consequently, the group of financial intermediaries reporting to the Central Credit Register is not identical to the group that transmits supervisory returns.
Participating intermediaries also report the exposures of foreign branches to borrowers resident in Italy. All the statistical distributions take such loans into account.
Once a month intermediaries are required to report each customer’s debtor position, comprising both individual and joint liabilities (joint accounts and partnerships).
The whole position relative to a given customer must be reported where even one of the following conditions applies: the sum of credit granted or used for all loans and guarantees granted to the customer is at least 30,000 euros; the total value of personal guarantees provided by the customer is at least 30,000 euros; the customer’s position is classified among bad debts or is written off during the reference month, regardless of the amount; the face value of factoring claims the intermediary has acquired from the customer is at least 30,000 euros; the value of the transactions carried out by the intermediary on behalf of third parties is at least 30,000 euros.
Where a report is made because one of the above conditions applies, it must cover all the outstanding positions of the customer in question (Bank of Italy 2010, p. 117).
- 4.
- 5.
The large decrease in the number of contracts in the database could be partially due to statistical changes which occurred in personal or firm data, and industry reclassifications which occurred in 2009. However, variables’ trend in our database are consistent with those observed in the data referred to the whole population of banks and households businesses. Indeed, from 2008 to 2009, the total number of lending contracts in Italy decreased by 20.47%. See http://bip.bancaditalia.it/4972unix/homebipita.htm
- 6.
In empirical literature the interest rate charged on the loan is often used as the measure of customer riskiness. The use of an observed measure of customer riskiness such as RISK allows controlling for the endogeneity issue concerning guarantees.
- 7.
In our data we do not have enough information to distinguish between different types of loans.
- 8.
To determine if our interest rate model can be correctly identified as a supply function, we should assume that the variance of the stochastic term in the loan offer function is smaller than the corresponding variance in the loan asking function. This assumption seems acceptable given that, for instance, a given bank forces its lending officers to follow certain common techniques of credit analysis that may result in more precision in processing lending application (i.e. in a lower stochastic variance). Diversely, borrowers are subjected to industry specific seasonal and cyclical shocks; moreover, firm treasurers are not compelled to behave similarly when they apply for loans. Both reasons imply a larger stochastic variance for the loan asking function (see Hester (1967), p. 132).
- 9.
The panel data is not a pure nested model, as we have some firms that have loans from different banks in each period. Therefore, for robustness checks purposes, we estimate model (3) considering only firms that do not have multiple bank relationships (NUM_REL = 1) in each year. Estimates confirm the findings of Tables 8.
- 10.
This threshold is used in the statistics of European Central Bank and in several Bank of Italy papers.
- 11.
All bank level variables in model (3) are exogenous. Therefore, we rule out bank-level endogeneity and only take into account firm level endogeneity.
- 12.
The latter is constructed by taking into account the best linear unbiased prediction of the random part of the baseline model.
- 13.
The result is consistent with previous studies according to which partnerships have a lower probability of posting personal guarantees than limited liabilities firms (Bonaccorsi di Patti 2006).
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Data Appendix
Data Appendix
SPD is the spread between the interest rate applied on loan by each bank and the interest rate on overnight interbank deposits. Both interest rates are averages of each year’s fourth quarter values.
COLL is the share of each loan guaranteed by collateral. Loans are mainly mortgages granted by banks to the borrower. This variable is a proxy for inside collateral.
PERS is the share of each loan guaranteed by personal guarantees. Personal guarantees are granted by third parties in favour of borrowers. This variable acts as outside collateral.
DOUBLEG is a binary dummy variable that takes a value of 1 when both personal and real guarantees are posted and 0 otherwise.
LOAN_S is the ratio between the amount of loan granted to the firm by each bank in the database and the average size of loan granted to firms of the same sector. It represents a proxy for loan size.
FIRM_S is a binary dummy variable which takes a value of 1 when the amount of loan is equal or greater €1,000,000 and 0 when the value of loan is less than €1,000,000. Alternatively, we use the dummy variable LARGE. The latter is a binary dummy variable which takes a value of 1 when the amount of loan is equal or greater € 250,000 and 0 when the value of loan is less than € 250,000.
RISK is a dummy variable that takes value equal to one if the firm has substandard loans, i.e. the firm is in temporary difficulty. This variable is a measure of ex ante (observed) credit risk of the firm.
LEND_REL is a binary dummy variable that takes a value of 1 in the case of a firm-bank relationship three or more years long and a value of 0 in the other cases.
NUM_REL is the number of lending relationships for each firm in each year.
CENTRAL is a binary geographical dummy variable that has a value of 1 for customers with headquarter in Central Italy and 0 otherwise.
SOUTH is a binary geographical dummy variable that has a value of 1 for customers with headquarter in Southern Italy and 0 otherwise.
BANK_S is a binary dummy variable that has a value of 1 for banks which are classified as “major” or “large” according to the classification of Bank of Italy (2008).
CRISIS*COLL represents the interaction between collateral (COLL) and a dummy that is equal to1 in every year of the financial crisis period (2008–2009); it is 0 in the pre-crisis years (2006 and 2007).
CRISIS*PERS represents the interaction between personal guarantees (PERS) and a dummy that is equal to1 in every year of the financial crisis period (2008–2009); it is 0 in the pre-crisis years (2006 and 2007).
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Calcagnini, G., Farabullini, F., Giombini, G. (2012). Guarantees and Bank Loan Interest Rates in Italian Small-Sized Firms. In: Calcagnini, G., Favaretto, I. (eds) Small Businesses in the Aftermath of the Crisis. Contributions to Economics. Physica, Heidelberg. https://doi.org/10.1007/978-3-7908-2852-8_12
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