# 6.2 Measurement

The regressions reported so far have used the correlation in the growth rates of real value added and industrial production index as measures of comovement. The exercises presented in Table 10 consider three alternatives. Column (1) addresses potential concerns with the use of first differences as a de-trending procedure and reports the coefficients obtained when using as dependent variable the correlation of the series of real value added de-trended using the Hodrik-Prescott filter. Column (2) uses a transformation of the correlation of the growth rate of real value added aimed to tackle the potential heteroskedasticity of the residuals resulting from the correlations taking values between -1 and 1. The transformed correlations are _r_ ̃ _ik_<sup>_c_= ln(</sup><sup>_r_</sup> _ik_<sup>_c_+ 1)</sup><sup>_−_ln(1</sup><sup>_−r_</sup> _ik_<sup>_c_)</sup><sup>_,_whichassumesaparametric</sup> non-linear relation between the correlations _rik_<sup>_c_andthevariablesofthemodel(seeOttoetal.,</sup> 2001, who introduced this). In both cases, the coefficient for the _credit-chain distance_ is negatively significant. Finally, Column (3) addresses concerns about the impact of outliers in the measures of correlations by replacing the standard measure described in equation (12) by a robust measure. The results are again largely unaffected.<sup>15</sup> The last two regressions presented in Table 10 check the robustness of the results to changes in the measure of credit chain distance. Column (4) reports the results obtained after relaxing as much as possible the assumption of fixed relative use of trade credit across industries that introduced in section 4.1 and using the available country-specific measures of sectoral use of trade credit that can be obtained from Worldscope. The measure of credit chain distance applied in this column uses the country-industry value of payables financing, _Pic_ , measured directly from Worldscope for all country-industries with data on more than five firms (395 cases in the sample), and the product of the U.S. relative and the corresponding country median ( _Pic_ = _Pi,US × Pc_ ) in the remaining cases (1305 cases). The results show that using as much country level information as possible does not affect the main conclusion of the paper; the coefficient obtained for the credit chain distance is similar to that previously reported. Column (5) explores the sensitivity of the main coefficient to the particular choice of the U.S. as baseline country for measuring the input-output relations across industries and uses instead _credit-chain distances_ computed using input-output matrices from the U.K. The rest of the procedure to construct the distance is the same as in the baseline case. Again, the coefficient is of the right sign and significance and are only marginally smaller.

# 6.3 Are industries with small credit chain distances similar in other dimensions?

Industries that are closer in terms of credit-chain distance may also be similar in other dimensions such as technologies, degrees of external financial requirements, liquidity needs, capital intensity, and stage of production. To the extent that these other dimensions of similarity are emphasized by country characteristics related to the use of trade credit, the results could be spuriously related to the omission of these potential determinants of comovement. The regressions presented in Table 11 explore several of these possibilities.

> 15The specific measure is the weighed correlation between two industries, where the weights are obtained from a robust regression between the two growth rate series.

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