In general terms, key sectors analysis aims at identifying and quantifying the economic impact of a sector in a given economy. For a sector, we mean here either an industry or a region, or even a cluster of them. Quite a few measures and methodologies of varied complexity have been proposed in the literature, from multiplier sums to extraction methods, but not without debate about their properties and information content. All of them, to our knowledge, focus exclusively on the interdependence effects that result from an input–output structure of the economy. By so doing the approach misses critical links beyond the interindustry ones. A productive sector's role is that of producing but also that of generating and distributing income among primary factors and households as a result of production. Thus, when measuring a sector's role, the income generating process should not be omitted if we want to elucidate the sector's true economic impact. A simple way to make the missing income links explicit is to use the SAM (Social Accounting Matrix) facility. Extending an extraction methodology to the SAM we compare lost output with and without the missing links. We observe that substantial differences in sectoral lost gross output arise but, even more important, we capture the implied shifting in the rank ordering of sectors.

We present an extension of the generalized RAS (GRAS) technique to a multi-regional (MR) or multi-national setting. The framework is applicable to updating/regionalizing/balancing any partitioned matrix that needs to conform to new row sums, column sums and additional non-overlapping aggregation constraints. The technique, which we refer to as MR-GRAS, also handles non-exhaustive constraints, in which case the missing values are endogenously generated in the updating process. We derive the closed-form solution of MR-GRAS, propose a simple iterative algorithm for its computation, and discuss the main analytical properties of the method as well as the normalization and interpretation of MR-GRAS multipliers. From a wide range of possible MR-GRAS applications, several updating frameworks of national and interregional supply and use tables are examined.

About one third of food produced for human consumption is lost or wasted. For this reason, food losses and waste has become a key priority within worldwide policy circles. This is a major global issue that not only threatens the viability of a sustainable food system but also generates negative externalities in environmental terms. The avoidance of this forbidding wastage would have a positive economic impact on national economies in terms of resource savings. In this paper we look beyond this somewhat traditional resource savings angle and we shift the focus to explore the distributional consequences of food losses and waste reduction using a resource constrained modeling perspective. The impact due to the behavioral shift of each household is therefore explained by two factors. One is the amount of resources saved when the behavioral shift takes place, whereas the other one has to do with the position of households in the food supply chain. By considering the whole supply chain, instead of the common approach based only in reducing waste by consumers, we enrich the empirical knowledge of this issue and improve the quantification of its economic impact. We examine data for three EU countries that present different economic structures (Germany, Spain and Poland) so as to have a broader and more robust viewpoint of the potential results. We find that distributional effects are different for consumers and producers and also across countries. Our results could be useful for policymakers since they indicate that policies should not be driven merely by the size waste but rather on its position within the food supply chain.