Model for the Probability of Having a Banking Relationships

First, the factors affecting the probability of having any banking relationship are examined. Second, for the firms that have any banking relationship, the factors affecting the probability of having a single or multiple banking relationships is analyzed. Two dummy variables are created for these estimations: RELATION and MULTIPLE. The dummy variable RELATION equals to one if a firm has any banking relationship and zero otherwise. The dummy variable MULTIPLE takes the value of one for firms with multiple banking relationships and zero for firms with a single banking relationship. The following models are estimated using the probit models:

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In these models, the dependent variable, banking relationship, is measured with the dummy variables, RELATION and MULTIPLE. The independent variables consist of several firm characteristics: performance of a firm, ROA, its age, AGE; its size, SIZE; innovativeness, INNOVA; leverage, LEVERAGE; obtaining funds external sources other than bank loans, NONFIN; belonging to a group or a holding, GROUP; having a related bank, BMEMBER; state-ownership of a firm, STATE; being a multinational company, MNC; and any incentives provided by government agencies to a firm, INCENT. The year and industry fixed effects, and , are controlled in the model. These variables are determined based on the models used in the literature.

Firm performance is used to express the overall results of any financial activity in a firm, namely firm profitability. ROA, net income divided by total assets, is used a measure of firm profitability. It is widely used in the literature as a proxy for firm performance. In the literature, there is no clear cut finding about the association between firm profitability and the probability of having any banking relationship. For example Detragiache et al. (2000) and Dietsch and Golitin-Boubakari (2002) find that firm performance decreases the probability of having any banking relationship in Italy and France. On the other hand, Tirri (2007) find a positive relationship between firm

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profitability and the probability of having any and multiple banking relationships in France.

The age of a firm, AGE, is included in the models to capture the length of the firm achievement. It is measured as the logarithm of firm’s age since its establishment. In general, since establishing a banking relationship takes time, the probability of having a banking relationship is expected to increase as firms get older. However, since older firms are better known in the market, they may face less adverse selection problems and thus have probably less need to maintain multiple banking relationships. Therefore the sign of the coefficient on AGE is expected to be positive in the first model but to be negative in the second one.

The size of a firm, SIZE, is measured by the natural logarithm of the market value of a firm, as it is widely used in the literature. I expect a positive relationship between firm size and both probabilities because of two reasons. First, larger firms may prefer to maintain multiple banking relationships in order to eliminate any risk coming from the liquidity shock to their banks. Second, larger firms conduct more businesses from different branches or a business in different regions. Thus, they may choose to maintain multiple banking relationships in order to finance such a complex business.

The innovativeness of a firm is calculated as R&D expenditures divided by sales, following Skinner (1993). In the literature, von Rheinbaben and Ruckes (2004) show a U-shaped relationship between the degree of firm innovativeness and the number of

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banking relationships. They show that as long as a firm decides to reveal private information, a higher degree of firm innovativeness makes fewer banking relationships optimal. On the other hand, when profits are highly sensitive to information leakage, the firm chooses not to reveal its confidential information in order not to impair output market success. In such cases, firms prefer to maintain multiple banking relationships to decrease borrowing rates by benefiting from competition among banks. Although I expect a positive relationship between firm innovativeness and having a banking relationship, the relationship between innovativeness and the probability of having multiple banking relationships can be both positive and negative, controlling for the industry that a firm operates.

The variable LEVERAGE is included in the models to control the debt level of a firm. It is measured by a debt ratio, total debt to total asset. In the literature, some studies suggest that, ceteris paribus, banks may decline to lend to highly levered firms because of the high probability of default. Thus, as firms become highly leveraged, the probability of having multiple banking relationships might decrease. On the other hand, some empirical studies indicate that there is a positive relationship between leverage of a firm and the probability of having multiple banking relationships. For example, Roberts and Siddiqi (2004) and Tirri (2007) find that highly leveraged firms are more likely to have multiple banking relationships in the U.S. and Italy, respectively. Therefore, while the sign of the coefficient of this variable is expected to be positive in the first model, it can be both positive and negative in the second model.

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The variable NONFIN is used to control the level of the firm’s liabilities other than bank loans. It is calculated as a percentage of non-bank liabilities in total liabilities of a firm. If a firm finds financing from other sources such as trade credits, rather than borrowing from banks, then the probabilities of having any and multiple banking relationships are expected to be lower and vice versa. Therefore, I expect a negative relationship between NONFIN and the probabilities of having any and multiple banking relationships.

I include the dummy variable of group membership, GROUP, to capture the effect of belonging to a group or a holding. It takes a value of one if a firm belongs to any group or a holding, and zero otherwise. If a firm is a member of a group of companies, it may easily find funds from the other firms in the group, and thus may rely less on borrowing from banks. Therefore, I expect a negative relationship between GROUP and the probabilities of having any and multiple banking relationships.

If a firm or its group owns a bank, this firm may get funding easily or get any other financial services from their banks at more favorable terms or conditions. Therefore, such firms may choose to maintain a banking relationship with only their bank. To capture this effect, I add a dummy variable BMEMBER in the models. This dummy variable equals to one if a firm is a part of a group that owns a bank, and equals to zero otherwise. This variable is expected to be negatively related to the probability of having multiple banking relationships.

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STATE is another dummy variable which equals to one if any government entity owns at least 5 percent of shares of a firm and equals to zero otherwise. If any government entity is a shareholder of a firm, it will be easier to establish a banking relationship with a state-owned bank. Thus, the variable STATE is expected to be negatively related to the probability of having multiple banking relationships.

I define a firm as a multinational company if a foreign owner holds more than 5percent of the company’s shares or equity and create the dummy variable MNC which equals to one if a firm is a multinational company and zero otherwise. Multinational firms may prefer to maintain more and different types of banking relationships to finance its businesses in different countries, like large firms. Therefore I expect MNC to be positively related to the probabilities of having any and multiple banking relationships.

Firms that obtain incentives from the government may rely less on bank loans. In this context, to capture the impact of incentives on banking relationship, I add a dummy variable, INCENT, in the models. It equals to one if a firm obtains any type of incentive from any government entity such as credits, grants, investment allowances, value-added- tax exemption certificate or remission of duty, and zero otherwise. I expect INCENT to be negatively related to the probabilities of having any and multiple banking relationships.

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The industry dummy variables, , are included into the equations to control for the possible effects of the sectors within which a firm operates. Industries are classified according to the grouping used by the Public Disclosure Platform (PDP). Firms in the sample are from eight main industries: education, health, sport and other social services; electricity, gas and water; manufacturing; construction and prosperity; mining; technology; wholesalers, retailers, hotels and restaurants; and transportation, communication and storage. In addition to these industry dummy variables, I control for

time effects by including year dummy variables, . There are nine calendar year

dummy variables for the sample period 2003-2011.

Table 2 summarizes the expected signs of the coefficients of the explanatory variables in the models explaining the probabilities of any and multiple banking relationships.