Impacts of Aggregation on Relative Performances of Nonsurvey Updating Techniques And Intertemporal Stability of Input–Output Coefficients

Abstract

In many instances, and for variety of reasons, input–output researchers are compelled to both employ mechanical techniques to update older survey-based tables as well as using more aggregated ones. This combination, however, gives rise to several concerns. The present paper is an attempt to investigate two such questions. First, the effects of aggregation on the accuracy ranking of selected updating methods, and second, the effects of aggregation on intertemporal stability of the input–output coefficients. To probe these issues, three updating methods were selected. These methods are NAÏVE or constant coefficient hypothesis, RAS or biproportional method, and LaGrangian optimization technique. Two survey-based tables from the former Soviet Union along with the selected updating techniques are used to generate updated target year’s direct and inverse transaction matrices at four aggregation levels. Comparison of the resultant estimates at these four levels of aggregation with their counterparts in the actual benchmark table reveals that a higher level of aggregation neither affects the rankings of the updating methods nor does it universally and unequivocally leads to a higher degree of intertemporal stability of input–output coefficients. Copyright Springer Science+Business Media, Inc. 2005aggregation, input–output coefficients, intertemporal stability of coefficients, LaGrangian, RAS, updating methods,

Water Footprint Calculation on the Basis of Input–Output Analysis and a Biproportional Algorithm: A Case Study for the Yellow River Basin, China

“…Tables for interim years are updated via RAS, commonly used as a revision method [42]. RAS assumes that the technology coefficient matrix is affected by a structural impact and a manufacturing impact, captured as matrices R and S, respectively [34]. The technology coefficient matrix for the target year t is estimated as follows:…”

“…The latter can evaluate the total amount of water required for a product throughout its life cycle, however, the method is computationally such as constant coefficient hypothesis (or NAÏVE) [32], the biproportional method by Richard Stone (abbreviated as RAS to commemorate the inventor) [33], and the LaGrangian optimization technique [34]. Jalili [34] compared the RAS method to the others and found that the RAS method is the most efficient updating technique across various levels of aggregations. The RAS method considers that the change of the direct consumption coefficient is produced under the influence of "substitution effect" and "fabrication effect".…”

“…The RAS method considers that the change of the direct consumption coefficient is produced under the influence of "substitution effect" and "fabrication effect". The RAS pre calculates the direct consumption coefficient of the input-output table in the base year, and then constructs the substitution matrix R and fabrication matrix S for calculating the direct consumption coefficient in the target year, in order to update the input-output table [34]. Dobrescu and Gaftea [35] checked the applicability and accuracy of the RAS based on the statistical series of emerging economy in Romanian.…”

“…The results provided the superiority of the RAS for updating the input-output coefficients, especially in the short term. The RAS method is effective in revising input-output tables [33][34][35][36][37], and with its help, dynamic changes in the WF can be examined via IOA. Numerous socio-economic factors affect water demand and can be identified through dynamic change analysis [38].…”