only industries with at least 15 observations for the growth rates is to avoid having a short rank correlation matrix.<sup>8</sup> I compute several other measures of comovement for robustness checks. One potential problem with the baseline measure is the use of a common deflator; if there were important heterogeneity in the evolution of prices across industries, the correlations computed with a common deflator may be driven by the correlation of relative inflation rates instead of the correlation of output growth. To address this concern I also compute the correlation of the growth rates of the index of industrial production reported in UNIDO in a similar way as described above. Although results obtained using this measure should not be affected by the relative price problem, the production index data is of lower quality and smaller coverage than the value added data, so I keep the later as benchmark.<sup>9</sup> Nevertheless, this choice will not affect the results. Finally, I also computed the correlations of the HP filtered series of real value added and industrial production to address possible concerns regarding the de-trending procedure implicitly used when calculating the correlation in growth rates.

The average correlations across manufacturing sectors during the period for the 43 countries in the sample, as well as the number of industry pairs with data are reported in Table 3. The average correlation of real value added and industrial production growth across sectors are always positive and around 0.25 on average. The inter-quartile range situates the average correlations between 0.2 and 0.36 for value added growth and 0.22 and 0.43 for the index of industrial production. This is consistent with the extensive evidence of positive comovement across sectors during the business cycle. The summary statistics are similar for the two measures, whose averages, reported in the table, are also positively and significantly correlated (correlation of 0.39 significant at the 2 percent level). As mentioned above, the coverage is better for the measure based in value added, for which there are 13,182 industry pairs compared with 12,548 for the measure based in industrial production. Both measures of correlation are positively but not significantly correlated with the average level of GDP per capita during 1980-2000. The positive correlation may indicate that idiosyncratic sectoral shocks are relatively more important in poorer countries. I will address this issue in the empirical analysis.

Finally, Table 4 provides information on the variation of the correlation measures in the two dimensions of the data: industry-pairs and countries. The first two columns report information for the raw correlations, and the last two for the residual correlations after partialling-out country and industry-pair fixed effects. Each column (which corresponds to one measure of correlation), first reports the overall variation (as measured by the standard deviation) and its decomposition in the within– and between–countries dimensions. In columns (3) and (4) the between countries variation is zero by construction. The last two rows of each column show the average standard deviations of

> 8With _N_ sectors and _T_ observations, there are _N_ ( _N −_ 1) _/_ 2 correlation coefficients to be estimated from _NT_ observations. The order condition, therefore, requires that _T ≥_ ( _N −_ 1) _/_ 2 for a full rank matrix. With 28 sectors, this requires 14 observations as a minimum. I allowed for one more than that.

> 9Yamada (2005), points out that reported indexes are in many cases not consistent over time and across industries in terms of ISIC aggregation. Also, most developing countries tend to report indexes based on the fixed weight Laspeyres formula that are not appropriate for the computation of growth rates (chain-indexes being the correct ones).

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