The Determinants of Structural Change in the European Union: A New Application of RAS

Abstract

IntroductionExplaining economic growth has been a topic of interest for a long time. (1) In a multisectoral input^output framework, technological developments interact for all sectors simultaneously and induce changes in the entire matrix of input coefficients. A common method for analysing the contribution of changes in the technical coefficients to economic growth is the structural decomposition approach (Carter, 1970). (2) This method decomposes output or value-added changes, for example, into a number of key determinants, one of which is technological change, as reflected by changes in the input^output structure of the economy. Typically, however, this determinant is not decomposed further. In the present paper we aim at quantifying the underlying sources of technological change. To this end, the changes in the input^output structure are decomposed into (1) the fabrication effects in each sector, reflecting the change in its use of the intermediate input mix, (2) the average substitution of each of the products, and (3) sector-specific substitutions. It is shown that the RAS method may be used for this purpose.

An Extended Input Output Table Compiled for Analyzing Water Demand and Consumption at County Level in China

“…By introducing water resource consumption into the local IO table, empirical results shows hybrid method makes some improvement in presenting the regional economic structure [37]. Moreover, regional ongoing industrialization is depending on utilization of natural resource and relies on unique geographical location, especially in a far rural area.…”

Measuring structural change in the I‐O production function by biproportional methods: A theorem of price invariance

“…As a tool for measuring structural change, defined as the change over time or space of the production structure, from Z to Z * or from A to A *, the biproportional filter will now be compared with technical coefficients for its sensitivity to prices. The biproportional filtering method (de Mesnard 1988, 1990, 2004; van der Linden and Dietzenbacher 1995, 2000; Dietzenbacher and Hoekstra 2002) was initially created to dispense any assumptions about the orientation (demand‐driven versus supply‐driven) of the economy, which logically implies working with flow matrices 6 . The letter K , denoting the biproportional operator,…”

“…The aim of this research note is not to go back to the discussion of the advantages of using biproportional methods in measuring structural change (de Mesnard 1988, 1990, 2004; van der Linden and Dietzenbacher 1995, 2000; Dietzenbacher and Hoekstra 2002) but instead, to demonstrate that the biproportional filter provides an additional benefit: it is insensitive to price effects, which is not the case for the simple comparison of coefficients. This means that the biproportional filter remains fully functional even when price information is lacking.…”

The Solution of Updating or Regionalizing a Matrix with both Positive and Negative Entries

“…General discussions concerning the economic background of the information loss problem, applied to tables with only non-negative entries, can be found in, for example, Bacharach (1970), Miller & Blair (1985), Toh (1998), and Van der Linden & Dietzenbacher (2000). In the original RAS-problem, the requirements are added that zero entries keep their zero value and positive entries keep their positive signs.…”

“…The reason for this choice is that, in this case, the equilibrium row multipliers r may be interpreted economically as 'substitution effects' and the column multipliers ŝ as 'fabrication effects' (see Stone, 1961;Toh, 1998;and Van der Linden & Dietzenbacher, 2000).…”