Using the world input-output tables available from the WIOD project (www.wiod.org), we quantify production line positions of 35 industries for 40 countries and the rest of the world region over 1996-2009. In contrast to the previous related literature we do not focus only on the output supply chain, but also consider sectors' input demand chain. This distinction is important because both these chains jointly constitute the entire production process, and the output sales structure of each sector is generally different from the structure of its inputs purchases. We use the (output) upstreamness measure of Antràs et al. (2012) and our proposed input downstreamness measure to quantify industry relative position, respectively, along the global output supply chain and the global input demand chain. The results are examined in detail at the levels of the world, six aggregate economic branches, sectors and countries.

Analytical and empirical comparison of policy-relevant key sector measures Temurshoev, U.; Oosterhaven, Jan IMPORTANT NOTE: You are advised to consult the publisher's version (publisher's PDF) if you wish to cite from it. Please check the document version below. Document VersionPublisher's PDF, also known as Version of record Take-down policy If you believe that this document breaches copyright please contact us providing details, and we will remove access to the work immediately and investigate your claim.Downloaded from the University of Groningen/UMCG research database (Pure): http://www.rug.nl/research/portal. For technical reasons the number of authors shown on this cover page is limited to 10 maximum. AbstractWe consider ten widely used key sector measures (linkages) and identify groups of the most similar indicators on both analytical and empirical grounds. We derive new closed-form formulas for the generalized complete and incomplete hypothetical extraction linkages and add the up till now undefined forward counterpart of the net backward linkage. The analytical relations and some stylized facts enable us to formulate hypotheses about the direction and strength of the relationships between various linkages. To study policyrelevant measures, our empirical tests are based on sectoral income (GDP) linkages, CO 2 -emission linkages and employment linkages for 34 industries and 33 countries. They show that the information on the ten key sector measures may be summarized by four measures.

The GRAS method as presented by Junius and Oosterhaven [Junius, T. and J. Oosterhaven (2003) The Solution of Updating or Regionalizing a Matrix with Both Positive and Negative Elements. Economic Systems Research, 15,[87][88][89][90][91][92][93][94][95][96] assumes that every row and every column of a matrix to be balanced has at least one positive element. This might not necessarily be true in practice, in particular, when dealing with large-scale input-ouput tables, supply and use tables, social accounting matrices, or, for that matter, any other matrix. In this short note we relax this assumption and make available our MATLAB program for anyone interested in matrix GRASing. The same issue arises in the presentations of the KRAS method [Lenzen, M., B. Gallego and R. Wood (2009) Matrix Balancing Under Conflicting Information. Economic Systems Research, 21, and the SUT-RAS method [Temurshoev, U. and M.P. Timmer (2011) Joint Estimation of Supply and Use Tables. Papers in Regional Science, 90, 863-882], which should be accordingly accounted for in their empirical applications.

We propose a new biproportional method specifically designed for joint projection of Supply and Use tables (SUTs). In contrast to standard inputoutput techniques, this method does not require the availability of total outputs by product for the projection year(s), a condition which is not often met in practice. The algorithm, called the SUT-RAS method, jointly estimates SUTs that are immediately consistent. It is applicable to different settings of SUTs, such as the frameworks with basic prices and purchasers' prices, and a setting in which Use tables are separated into domestic and imported uses. Our empirical evaluations show that the SUT-RAS method performs quite well compared to widely used short-cut methods.

We formulate the problems of finding a key sector and a key group of sectors in the economy by the hypothetical extraction method (HEM), and derive their analytic solutions that are termed industries' factor worths. It is shown that the key group of k ≥ 2 sectors is, in general, different from the set of top k industries selected on the basis of the key sector problem, the issue which is totally ignored in the input-output (IO) linkage literature. Further, the related problems of finding a key region and a key group of regions in an interregional IO setting are discussed. We also examine how a change in an input coefficient affects the factor worth of an industry. The key group problem is applied to the Australian economy for factors of water use, CO 2 emissions, and generation of profits and wages.