as the absolute value of the difference in their Gini coefficients (obtained from Cowan and Neut, 2002). The regression presented in column (3) adds to the main specification the interaction of this variable with the overall volatility of a country measured by the standard deviation of the growth rate of real GDP per capita. Although, consistently with this mechanism, sectors producing goods of similar complexity are indeed more correlated in more volatile countries, the result regarding the role of credit-chains remains unaltered.

The credit-chain mechanism emphasized throughout the paper focuses mainly on the transmission of shocks through backward linkages but forward linkages may also be important, not only for technological reasons (productivity shocks upstream translating into an increase in production downstream because of an expansion of the supply of intermediates) but also for credit reasons because an upstream shock could affect the supply of trade credit for downstream industries. The regressions presented in columns (4) to (6) check for this possibility. Column (4) starts by adding the interaction between the input-output distance between two industries derived from the _COST_ matrix of transmission of upstream shocks described in Shea (2002) and a country’s payables financing to check if the main result could be simply related to upstream transmission being more important in countries with higher use of trade credit.<sup>18</sup> The results suggest that industries with stronger forward linkages tend to co-move more in countries with higher payables financing, but this channel does not importantly affect the findings regarding the role of credit chain distance. The regressions reported in columns (5) and (6) go one step forward and ask whether the forward linkages may matter in the transmission of credit default. The regression in column (5) adds to the baseline specification a measure analogous to the credit chain distance built using the _COST_ matrix (instead of _D_ ) and payables financing to check for the possibility that upstream shocks affect relatively more downstream industries that rely more on trade credit. The regression in column (6) instead focuses on the constraints to the supply of credit and adds a measure of distance built using the _COST_ matrix and the upstream industry’s level of accounts receivables to sales as a measure of the amount of credit offered to downstream industries. The results indicate that only the measure based on the use of credit downstream is related to the degree of comovement in countries with higher aggregate use of trade credit, which suggest that upstream shocks benefit relatively more downstream industries that use relatively more trade credit, but do not provide evidence consistent with contractions on trade credit supply.

The derivation of equation (10) assumed that sectoral shocks were independent and identically distributed. Relaxing this assumption would result in additional terms on the linear approximation that will also be a function of the elements of the _D_ matrix, raising, therefore, the possibility that the credit chain distance could be picking the effect of one of these omitted variables. One possible deviation from this assumption comes from differences in the variance of the shocks across industries. In this case, the linear approximation (10) would include a term that is a function of the of the _D_ matrix and the relative variances of the sectoral shocks. This term

> 18The input-output distance based in this matrix is computed analogously to the distance based on the _D_ matrix (see Eq. [10])

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