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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.
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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).
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.
Producer households are partnerships or sole proprietorships with a number of employees less than or equal to five.
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).
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
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.
In our data we do not have enough information to distinguish between different types of loans.
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).
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.
This threshold is used in the statistics of European Central Bank and in several Bank of Italy papers.
All bank level variables in model ( 3) are exogenous. Therefore, we rule out bank-level endogeneity and only take into account firm level endogeneity.
The latter is constructed by taking into account the best linear unbiased prediction of the random part of the baseline model.
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).
Albright JJ, Marinova DM (2010) Estimating multilevel models using SPSS, Stata, SAS, and R. Mimeo
Antweiler W (2001) Nested random effects estimation in unbalanced panel data. J Econ 101:295–313
Baltagi BH (2002) Econometrics. Springer, New York
Bank of Italy (2008) Annual report. Rome
Bank of Italy (2010) Statistical bulletin 4/2010
Berger AN, Udell GF (1990) Collateral, loan quality, and bank risk. J Monet Econ 25:21–42 CrossRef
Berger AN, Udell GF (1995) Relationship lending and lines of credit in small firms finance. J Bus 68(3):351–381 CrossRef
Berger AN, Udell GF (1998) The economics of small business finance: the roles of private equity and debt markets in the financial growth cycle. J Bank Finance 22:613–673 CrossRef
Berger AN, Udell GF (2005) Small business and debt finance. In: Zoltan JA, Audretsch DB (eds) Handbook of entrepreneurship research. Springer, New York
Besanko D, Thakor AV (1987) Collateral and rationing: sorting equilibria in monopolistic and competitive credit markets. Int Econ Rev 28(3):671–689 CrossRef
Bester H (1985) Screening vs rationing in credit markets with imperfect information. Am Econ Rev 57:850–855
Bester H (1987) The role of collateral in credit markets with imperfect information. Eur Econ Rev 31:887–899 CrossRef
Bonaccorsi di Patti E (2006) La diffusione delle garanzie reali e personali nel credito alle imprese. Bank of Italy
Boot AWA, Thakor AV (1994) Moral hazard and secured lending in an infinitely repeated credit market game. Int Econ Rev 35(4):899–920 CrossRef
Boot AWA, Thakor AV, Udell GF (1991) Secured lending and default risk: equilibrium analysis, policy implications, and empirical results. Econ J 101(406):458–472 CrossRef
Casolaro L, Focarelli D, Pozzolo AF (2008) The pricing effect of certification on syndicated loans. J Monet Econ 5:335–349
Calcagnini G., Farabullini F., and G. Giombini (2012). The impact of the recent financial crisis on bank loan interest rates and guarantees. MPRA Paper 36682, University Library of Munich, Germany
Chakravarty S, Yilmazer T (2009) A multistage model of loans and the role of relationship. Financ Manage 38(4):781–816 CrossRef
Cowling M (2010) The role of guarantee schemes in alleviating credit rationing in the UK. J Financ Stab 6:36–44 CrossRef
Ebbes P, Bockenholt U, Wedel M (2004) Regressor and random-effects dependencies in multilevel models. Statistica Neerlandica 58:161–178 CrossRef
Harhoff D, Körting T (1998) Lending relationships in Germany: empirical results from survey data. J Bank Finance 22:1317–1353 CrossRef
Hester DD (1967) An empirical examination of a commercial bank loan offer function. In: Hester DD, Tobin J (eds) Studies of portfolio behavior. Wiley, New York, pp 118–170
Jimenez G, Salas V, Saurina J (2006) Determinants of collateral. J Financ Econ 81:255–281 CrossRef
John K, Lynch AW, Puri M (2003) Credit ratings, collateral and loan characteristics: implications for yield. J Bus 76:371–409 CrossRef
Lewbel A (1997) Constructing instruments for regressions with measurement error when no additional data are available, with an application to patents and R&D. Econometrica 65:1201–1213 CrossRef
Manove M, Padilla AJ (1999) Banking (conservatively) with optimists. Rand J Econ 30(2):324–350 CrossRef
Manove M, Padilla AJ, Pagano M (2001) Collateral versus project screening: a model of lazy banks. Rand J Econ 32(4):726–744 CrossRef
Ogawa K, Sterken E, Tokutsu I (2010) Multiple bank relationship and the main bank system. In: Calcagnini G, Saltari E (eds) The economics of imperfect markets, series contribution to economics. Physica/Springer, Berlin/Heidelberg, pp 73–90 CrossRef
Ono A, Uesugi I (2009) The role of collateral and personal guarantees in relationship lending: evidence from Japan’s SME loan market. J Money Credit Bank 41(5):935–960 CrossRef
Panetta F, Signoretti FM (2010) Domanda e Offerta di Credito in Italia durante la Crisi Finanziaria. Occasional Papers, 63 Bank of Italy
Petersen MA, Rajan RG (1994) The benefits of lending relationship: evidence from small business data. J Finance 49(1):3–37 CrossRef
Pozzolo AF (2004) The role of guarantees in bank lending. Discussion papers, 528. Bank of Italy
Rabe-Hesketh S, Skrondal A, Pickles A (2004) Generalized multilevel structural equation modeling. Psychometrika 69(2):167–190 CrossRef
Spencer NH, Fielding A (2000) An instrumental variable consistent estimation procedure to overcome the problem of endogenous variables in multilevel models. Multilevel Model News 12(1):4–7
Stiglitz J. E., and Weiss A. (1981). Credit Rationing in Markets with Imperfect Information, American Economic Review, 71(3):393–410.
- Guarantees and Bank Loan Interest Rates in Italian Small-Sized Firms
- Physica-Verlag HD
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