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Journal Article

Regional and sectoral disaggregation of multi-regional input-output tables: A flexible algorithm

Authors
/persons/resource/Leonie.Wenz

Wenz,  Leonie
Potsdam Institute for Climate Impact Research;

/persons/resource/sven.willner

Willner,  Sven
Potsdam Institute for Climate Impact Research;

/persons/resource/alexander.radebach

Radebach,  Alexander
Potsdam Institute for Climate Impact Research;

/persons/resource/robert.bierkandt

Bierkandt,  Robert
Potsdam Institute for Climate Impact Research;

/persons/resource/Jan.Steckel

Steckel,  Jan Christoph
Potsdam Institute for Climate Impact Research;

/persons/resource/Levermann

Levermann,  Anders
Potsdam Institute for Climate Impact Research;

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Citation

Wenz, L., Willner, S., Radebach, A., Bierkandt, R., Steckel, J. C., Levermann, A. (2015): Regional and sectoral disaggregation of multi-regional input-output tables: A flexible algorithm. - Economic Systems Research, 27, 2, 194-212.
https://doi.org/10.1080/09535314.2014.987731


Cite as: https://publications.pik-potsdam.de/pubman/item/item_19997
Abstract
A common shortcoming of available multi-regional input–output (MRIO) data sets is their lack of regional and sectoral detail required for many research questions (e.g. in the field of disaster impact analysis). We present a simple algorithm to refine MRIO tables regionally and/or sectorally. By the use of proxy data, each MRIO flow in question is disaggregated into the corresponding sub-flows. This downscaling procedure is complemented by an adjustment rule ensuring that the sub-flows match the superordinate flow in sum. The approximation improves along several iteration steps. The algorithm unfolds its strength through the flexible combination of multiple, possibly incomplete proxy data sources. It is also flexible in a sense that any target sector and region resolution can be chosen. As an exemplary case we apply the algorithm to a regional and sectoral refinement of the Eora MRIO database.