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

TL;DR

This paper introduces a method to accurately rank sectors and countries in global value chains using only local and aggregate data, avoiding the need for complete input-output tables.

Contribution

It presents a novel rank-1 approximation approach that efficiently estimates upstreamness and downstreamness without full I-O table knowledge.

Findings

High accuracy in ranking achieved with limited data

Method performs well on empirical World Input-Output Database data

Spectral properties of I-O tables explain approximation effectiveness

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.