Common use of Distance Clause in Contracts

Distance. We now turn to a detailed discussion of the coefficients on the distance variables to identify which theoretical predictions can be rejected by the data. We take for each of our distance measures the log of (one plus) the distance, as we conjecture the marginal impact on the loan rate to decrease with distance.54 We will use a robustness exercise to investigate the impact of this choice of functional form. The negative and significant coefficients on ln(1+Distance to Lender) in Models I to IV suggest that borrowers located farther away from the lender pay a lower loan rate at the lending bank. These results are consistent with spatial price discrimination, as the lending bank charges a higher loan rate to borrowers with greater proximity. In addition, the lender’s market power increases with the distance between the borrower and the closest competitors, as indicated by the positive and significant coefficient on the variable ln(1+Distance to Closest Competitors). Our proxy for the distance between the borrower and the closest competitor may identify strategic behavior between banks that our other competition variables did not (or only partly) pick up. Indeed, even after controlling for the number of competitors, branch concentration, postal zone, and bank branch effects, the lending bank seems to enjoy substantial market power, which increases with the distance to the closest competitors. These results thus reject uniform pricing and monitoring cost theories without discriminatory pricing.55 The price discrimination models based on linear transportation costs and/or monitoring costs discussed in section 2 further provide precise theoretical predictions concerning the sum of the coefficients on both distance measures (this prediction is not present in the asymmetric information models we discussed). In particular, given the location of bank branches, a marginal shift in the location of the borrower implies that the sum of the coefficients on both distance measures should equal zero. Therefore, in line with this theoretical prediction emanating from simple location models, we restrict the sum of the coefficients on both distance measures to equal zero in Model II (these coefficients are mostly easily interpretable). We test the restriction and report the results in Model V. The F-statistic equals 8.6; hence, we cannot reject the equality restriction. Both distance effects are not only statistically but also economically relevant (obviously, our distance measures are just one set of all the factors explaining the variation in loan rates). An increase of one standard deviation in the distance between borrower and lender (i.e., the traveling time increasing from 0 to 7.3 minutes), decreases the loan rate by 18 basis points in Model V.56 An increase of one standard deviation in the distance between borrower and the closest competitors (from 0 to 2.3 minutes) increases the loan rate by about 10 basis points.57 For the median loan of BEF 300,000 (USD 7,500), annual outlays for the borrower decrease by BEF 72 (USD 1.8) per extra minute of traveling to the lender.58 Belgian entrepreneurs and (bank) managers made around BEF 20 / minute in 1995,59 while the operating costs for a car (gas, maintenance, tires) may have amounted to around BEF 3 / minute of driving.60 According to a linear transportation cost model, thus, the median borrower is expected to make one-and-a-half additional round-trips to his bank branch as a direct result of the new loan.61 Alternatively, according to a linear monitoring cost model, loan officers are expected to make three round-trip visits to their median borrowers. Hence, we find our spatial discrimination estimates economically interesting on the margin, but also reasonable (given that, for instance, loan repayment can be organized by mail). On the basis of the estimates we can also assess the magnitude of possible bank rents. Borrowers located very close to the lender (say in the same housing block) will be charged 14 basis points more, on average, than a borrower located right between the lender and the quartile closest competitor.62 Hence, “location rents” extracted from the closest borrowers are around 4% (and can be as high as 9%) of the bank’s marginal cost of funding, which we take to be the interest rate on a Belgian government security with equal repayment duration as the loan given to the firm. Location rents extracted from the average observed borrower amount to around 0.5% of the marginal cost. To put these location rents in perspective, note that the loan rate increases by 62 basis points over 26 years (the period the longest observed relationship lasts). This maximum increase implies annualized “information rents” of less than 7% of the marginal cost of funding.63 Information rents extracted from the average observed borrower amount to 1.5% of the marginal cost.