REGIONAL AND SECTORAL DISAGGREGATION OF MULTI-REGIONAL INPUT–OUTPUT TABLES – A FLEXIBLE ALGORITHM

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.

Keywords:

• Disaggregation
• Disaster impact analysis
• Global supply chains
• Life cycle assessment
• Regionalization

Acknowledgments

We thank Christian Otto and Julius Zwirner for fruitful discussions.

FUNDING

This research was supported by the German Environmental Foundation and the Heinrich-Böll Foundation. It has received funding from the European Union Seventh Framework Programme FP7/2007-2013 under grant agreement n° 603864. Funding from Deutsche Forschungsgemeinschaft (DFG, CRC 1026 ‘Sustainable Manfacturing’) is gratefully acknowledged.

SUPPLEMENTAL DATA

Supplemental material for this article is available via the supplemental tab on the article's online page at http://dx.doi.org/10.1080/09535314.2014.987731

Notes

¹In some cases, the MRIO tables are replaced by more precise supply-use tables, which are capable of capturing the full diversity of different commodities that different industries may consume and produce instead of assuming a one-to-one relationship of commodities and industries.

²Even beyond standard I–O analysis there is a need for MRIO data sets with increased sectoral resolution, for example, in the field of studies of structural change and growth processes (Radebach et al., 2014).

³Approaches to deal with this problem are, among others, the RAS (Stone and Brown, 1962), the cRAS (constrained RAS, Lenzen et al., 2006) and the GRIT (Generation of Regional Input-Output Tables) algorithm (Bayne and West, 1989).

⁴Available as open source at http://github.com/swillner/disaggregation.