Firm Location, Loan Characteristics, and Distance Sample Clauses

Firm Location, Loan Characteristics, and Distance. Both XxxXxxxx.xxx and Mappoint account for road categorization when calculating traveling times. Traffic congestion, however, is not taken into account. As congestion is more common in cities than in the countryside, we introduce a dummy variable Urban and interaction terms with our two distance measures. Urban equals one when the borrower is located in an agglomeration with more than 250,000 inhabitants,65 and zero otherwise. The coefficients on the interaction terms in Model VII in Table 7 indicate that urban borrowers experience discrimination twice as harshly, possibly suggesting that traffic congestion substantially increases actual traveling times in urban areas (the other coefficients are broadly unaffected).66 In addition, rural borrowers pay on average 35 basis points less than urban dwellers. The latter result raises the troubling possibility that farms (located in rural areas) and manufacturing companies (located on the outskirts of towns) pay a lower loan rate than service-type companies (located downtown and possibly close to bank branches) - not because of location, but because of, say, the tangibility of their assets. The firm variables employed so far, including the 49 industry dummies, may not fully absorb such differences in firm characteristics across location, resulting in a “spuriously” estimated effect of distance on the loan rates. We therefore also split the sample by sector. We identify 247 Agricultural, Fishing, and Mining firms, 900 Manufacturing firms, and 13,897 Service firms. The average Agricultural, Fishing, and Mining firm in the sample is indeed located around 10% farther from the lending branch and closest competitor than the average Manufacturing firm (we choose not to tabulate these comparisons). On the other hand, Manufacturing and Service firms do not differ statistically in their location vis-à-vis the lending or the closest competitor’s bank branch. Table 8 reports the coefficients for the three main regressions split along sector (we retain the model numbering introduced in Table 7 throughout the paper). The distance coefficients for the three sectors are surprisingly similar in magnitude, although fewer observations in the Agriculture, Fishing, and Mining and Manufacturing sectors prevent most coefficients from being statistically significant. Hence, differences in firm characteristics (such as asset tangibility) that may be correlated with location seemingly do not drive our results. Nevertheless, we use augmente...