The use of network theory in the input-output field supposes an interesting alternative that allows structural complexity, weakness and strength to be shown. To this end, we analyse the relative position of each industry via core-periphery models to offer an approach to fundamental economy structure. These models are very flexible and can be applied on Boolean or valued graphs. In order to overcome the usual criticisms, we extend previous works and develop core-periphery valued models. This novel proposal is applied to the analysis of the European and Spanish economies in 1995.
JEL classification: C67
There is a long tradition of studies in the input-output field for determining key sectors (Perroux, 1955;Hirschman, 1958). Their analysis allows the identification of those sectors with great effect on the demand and supply system and therefore, they constitute the basis for the growth and development of a territory.In order to pinpoint, those sectors whose position is more relevant in the economy, we propose from the network theory a definition of centrality measures that we consider to be new in the input-output field. The definition is based on the consideration of three complementary characteristics: total effects, mediative effects and immediate effects. These measures we call multilevel indicators and they have the enormous advantage of allowing different-sized structures to be compared and the key sector concept to be approached from a relational and global viewpoint.
The compilation of the information required to construct survey-based input-output (I-O) tables consumes resources and time to statistical agencies. Consequently, a number of non-survey techniques have been developed in the last decades to estimate I-O tables. These techniques usually depart from observable information on the row and column margins, and then the cells of the matrix are adjusted using as a priori information a matrix from a past period (updating) or an I-O table from the same time period (regionalization). This paper proposes the use of a composite cross-entropy approach that allows for introducing both types of a priori information. The suggested methodology is suitable to be applied only to matrices with semi-positive interior cells and margins. Numerical simulations and an empirical application are carried out, where an I-O table for the Euro Area is estimated with this method and the result is compared with the traditional projection techniques.
In competitive electricity markets, the growth of electricity generated by renewable sources will reduce the market price of electricity assuming marginal cost pricing. However, small renewable distributed generation (RDG) alone cannot modify the formation of electricity prices. By aggregating small RDG units into a Virtual Power Plants (as a single unit market) they are capable of dealing at the wholesale electricity market analogous to large-scale producer following in changes in wholesale prices. This paper investigates the socioeconomic impacts of different type of RDG technologies on Spanish economic sectors and households. To this end, we applied an input-output price model to detail the activities more sensitive to changes in electricity price due to RDG technologies deployment and the associated modifications in income and total output associated with the households’ consumption variation. Detailed Spanish electricity generation disaggregation of the latest available Spanish Input-Output table, which refers to 2015, was considered. It was found that the integration of RDG units in the electricity market project a better situation for the economy and Spanish households. This paper’s scope and information can be used to benefit decision-making with respect to electricity pricing policies.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.