Relationship Lending and Credit Quality

In his study, Wagner (2010) extend the model of Boyd and De Nicolò (2005) and suggest that the possibility for banks to select among different types of borrowers alterate the bank behavior and reverses the stability effect of the lending channel.

See ISTAT “Struttura e competitività del sistema delle imprese industriali e dei servizi”, published on 27th October 2011.

More precisely, the data used here are derived exclusively from the borrower-bank relationship level, which means that we collect some information about the relationship that firm i (i = 1..43,338) has with bank j (j = 1..8). According to our data, the loans are then aggregated at the borrower level. The analysis with an aggregated measure of credit granted (a sum of line-of-credit loans and non-line-of-credit loans for each borrower) represents one of the limitations of the survey data that prevent us from examining the role of the technical form of loans in determining loans’ default probability. Other important limitations refer to the lack of firm-specific variables except for yearly sales, size class, industry and province.

We normalize to 10 the number of internal rating classes to preserve the privacy disclaimer of our data provider. The true number of internal rating classes is not much different from 10.

The variables LENGHT and BANKS measure the strength of bank-firm relationships. However, having no information about the number of financial services provided by the lenders, we cannot follow the general definition of relationship banking advanced by Boot (2000): <<the provision of financial services by a financial intermediary that: i. invests in obtaining customer-specific information, often proprietary in nature; and ii. evaluates the profitability of these investments through multiple interactions with the same customer over time and/or across products>>, pp. 10.

In more formal terms, the following proxy of distance was used: DISTANCE _ij  = ln(1 + KM_ij), where KM _ij is the distance in kilometers between the province of firm i (in Italy, there are 110 provinces) and the headquarter of bank j (j = 1..8).

A credit line is understood here as a contract that allows a borrower to take advantage of a predetermined line limit and repay the loan at the borrower’s discretion with an interest rate periodically set by the bank. Whenever the drawn credit exceeds the line limit, the bank charges a penalty interest rate.

Except for sales, the data of this study cannot be matched with firms’ balance-sheet data, for example, because the banking group altered the borrower identities before providing us the data for confidentiality purposes.

However, we omit to run this analysis because it is beyond the scope of this paper.

Thus, the positive sign for the marginal effect of RATINGt explains the obvious consideration that borrowers with a high rating in 2008 (bad firms) have a higher likelihood to fail in 2010 than better borrowers, as also exploited by the rating transition matrix (we omitted for brevity the matrix).

It should be noted that the mean value of DISTANCE for the parent bank is lower than for subsidiary banks (we omit to separate descriptive statistics of main variables for each bank). This circumstance may explain why the coefficient of DISTANCE is statistically significant (at 1 %) in column (2) but not in column (1).

We omitted to detail the frequency of firm size classes for each lender for reason of brevity.

Because D (default) corresponds to the fourteenth class of the rating bank system.