Upstreamness and downstreamness in input-output analysis from local and aggregate information

Abstract

Ranking sectors and countries within global value chains is of paramount importance to estimate risks and forecast growth in large economies. However, this task is often non-trivial due to the lack of complete and accurate information on the flows of money and goods between sectors and countries, which are encoded in Input-Output (I-O) tables. In this work, we show that an accurate estimation of the role played by sectors and countries in supply chain networks can be achieved without full knowledge of the I-O tables, but only relying on local and aggregate information, e.g., the total intermediate demand per sector. Our method, based on a rank-$1$ approximation to the I-O table, shows consistently good performance in reconstructing rankings (i.e., upstreamness and downstreamness measures for countries and sectors) when tested on empirical data from the World Input-Output Database. Moreover, we connect the accuracy of our approximate framework with the spectral properties of the I-O tables, which ordinarily exhibit relatively large spectral gaps. Our approach provides a fast and analytical tractable framework to rank constituents of a complex economy without the need of matrix inversions and the knowledge of finer intersectorial details.

22 pages, 5 figures. Title changed, major restructuring of the paper

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Cited by in corpus (2)

• Correlation between upstreamness and downstreamness in random global value chains
• Statistics of the non-zero eigenvalues and singular values of low-rank random matrices with non-negative entries