Spatial and Sectoral Aggregation in the Commodity-Industry Multiregional Input-Output Model

In this paper, some experiments on the effect of aggregation in multiregional input-output (MRIO) models are reported. Sectoral aggregation and spatial aggregation are examined, separately and jointly, with use of hypothetical MRIO accounts and also with use of the 1977 US MRIO data. The spatial aggregation experiments constitute an extension and update of similar kinds of research reported on in a previous paper by Blair and Miller, in which hypothetical accounts and the 1963 US MRIO data were used. An important difference between the 1963 and 1977 data sets is that the latter are in the commodity-by-industry format. In the experiments reported here, it has been possible to deal with larger data sets for the least aggregated base cases, and the amount of data that is 'covered up' in the aggregations is therefore much larger. Nevertheless, it is still possible to conclude that spatial aggregation in MRIO models need not generate unacceptable error. The term error suggests that the most disaggregated data are always most accurate; because this position is not embraced by all input-output observers, we can alternatively describe the work in this paper as an investigation into the sensitivity of MRIO model results to sectoral and/or spatial aggregation

Commodity · Data aggregator · Econometrics · Economics · Computer Science · Environmental Impact and Sustainability · Global trade and economics · Regional Economics and Spatial Analysis

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• Spatial Aggregation in Multiregional Input-Output Models

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• Frontiers of Input-Output Analysis

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