raneous transmission described in section 3 (see Eq. [6]) for the 28 three-digit ISIC manufacturing industries in which I have comovement data using information from the 1992 commodity-by-industry ( _USE_ ) and industry-by-commodity ( _MAKE_ ) matrices produced by the U.S. Bureau of Economic Analysis (BEA) and my own correspondence between the BEA industry classifications and ISIC. The appendix describes the construction of this matrix in detail. Using this matrix I compute the input-output and (generic) credit-chain distances across industry pairs as described in equation (10) of section 3.

Since the sectoral classification consists of 28 industries at three-digit ISIC level, the _C_ matrix of credit-chain distances contains 378 different distances (the distance between a sector and itself is normalized to 100). The distribution of the distances is significantly skewed with most of its mass close to zero (around 40 percent) because of the well-known sparsity of the input-output matrices (see Figure 2).<sup>11</sup> Table 6 shows the twenty industry pairs with the closest _credit-chain distances_ , which represent about five percent of the total number of pairs. The closest pairs are intuitive; for instance, the two pairs with the smaller distances are the ones formed by the Textiles and Wearing apparels industries and Transport Equipment and Fabricated Metal Products industries. Although pairs that are close according to _credit-chain_ distance also tend to have a small _input-output_ distance, there is some important degree of variation between these measures of similarity; only half of the pairs reported in Table 6 are also among the 20 with the closest input-output distance and the rank correlation between them is only 0.17. The overall correlation between both distance measures is 0.66, but is mainly driven by the difference between the group of industries with small linkages and the rest; in fact, the correlation drops to 0.5 when looking only to those industry-pairs with _input-output distances_ above the median. Thus, the overall pattern suggests that the measure of _credit-chain distances_ is partly determined by the input-output linkages among sectors, but the relative use of trade credit in the chain linking two sectors introduces non-trivial variation in the measure with respect to the standard input-output distance.

# 5 Results

This section presents the results of the test of the hypothesis that the intensity of use of trade credit along the chain linking two industries increases their correlation. Following the empirical approach outlined in section 3, I test this hypothesis by using the data to estimate the parameters of equation (11) and test whether the coefficient of the interaction of the generic credit chain distance and the use of trade credit in the country ( _a_ ) is significantly positive. The coefficients obtained by ordinary least squares (OLS) for various versions of equation (11) that include different sets of additional controls are presented in Table 7. In Panel A the dependent variable is the correlation of growth rates of real value-added between industry-pairs in different countries and in Panel B is

> 11According to Horvath (1998, 2000) this sparsity is crucial to understand why the law of large numbers does not necessarily apply to models where transmission of shocks is given by input-output relations.

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