Accounting foundations for interregional commodity-by-industry input-output models

Jackson, Randall; Schwarm, Walter

doi:10.1007/s12076-011-0066-8

Cited by 17 publications

(3 citation statements)

References 11 publications

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“…Since then, a number of other research teams have attempted to estimate intraregional and interregional trade, (c.f. Celik, 2004; Jackson, Schwarm, & Okuyama, 2006; Jackson, Schwarm, Okuyama, & Islam, 2006; Lindall, Olson, & Alward, 2006; Robinson & Liu, 2006; Llano, Esteban, Pérez, & Pulido, 2010; Jackson & Schwarm, 2011; Chun, Kim, & Kim, 2012; Hwang et al, 2016; LeSage & Llano, 2016; Sun, 2018; Zhang, Cheng, & Jin, 2019).…”

Section: Rpc Estimatesmentioning

confidence: 99%

Intraregional trade shares for goods‐producing industries: RPC estimates using EU data

Lahr

Ferreira

Többen

2020

Papers in Regional Science

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The lack of subnational trade data has dampened the development of reliable regional and multiregional models for regional policy development. So, most researchers and vendors of regional and interregional economic models continue to rely on location quotients, supply-demand pool techniques, or minor modifications of them, despite knowing that they underestimate interregional trade. In this piece, we analyse the relative viability of estimates of intraregional trade-so called "regional purchase coefficients" (RPCs). We do so for manufacturing sectors in 28 EU countries using the World Input-Output Database. We introduce an RPC-estimating technique using a quasi-binomial regression approach for goods-producing industries; we apply standard supply/demand ratios as RPCs for service-based industries. We then apply the estimates to an aggregate EU input-output (I-O) table and measure how closely the results approximate the I-O tables (direct requirements matrices) for each of the 28 EU nations. We compare these findings to those obtained by other conventional approaches. We also evaluate their ability to replicate the country Leontief inverses and output multipliers. We find quasi-binomial regression approaches superior across the board.

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Section: Rpc Estimatesmentioning

confidence: 99%

Intraregional trade shares for goods‐producing industries: RPC estimates using EU data

Lahr

Ferreira

Többen

2020

Papers in Regional Science

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“…Other regionalisation approaches exist and could have been used in principle (e.g. Jackson and Schwarm, 2011;Oosterhaven et al, 1986;Junius and Oosterhaven, 2003;Wenz et al, 2014), but implementing these approaches in practice would have been outside of the scope of work for most studies, in particular where researchers are not familiar with regional IOA and would have first had to learn it as a new field of research.…”

Section: Ielab As a Research Enablermentioning

confidence: 99%

An input–output virtual laboratory in practice – survey of uptake, usage and applications of the first operational IELab

Wiedmann

2017

Economic Systems Research

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The Industrial Ecology Virtual Laboratory (IELab) is a collaborative cloud-computing platform for compiling large-scale, high-resolution, enviro-socio-economic accounts based on multi-region input-output (MRIO) tables and for conducting integrated sustainability assessment projects. These include, for example, assessments of biofuels and low-carbon construction materials or high-resolution waste modelling. This contribution provides a structured review of IELab applications that were published in either peer-reviewed journal papers or in the form of conference proceedings. The main research question posed is "What are the specific features of IELab that were used in the research and could the research have happened without them?" It is investigated whether the IELab has actually and truly enabled new research. A detailed analysis of IELab characteristics and their usage is presented. The results can help with the design of new research projects and inform existing and prospective users of the IELab about the options for academic research and practical applications.

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“…Moreover, since national IOTs are becoming rare, regional interindustry analysts increasingly are estimating interregional SuTs, which means that a choice has to be made from a whole family of possible interregional SuTs, not all of which have an accompanying interregional IO model (see Oosterhaven, 1984). The main choice is between estimating an interregional make table (products by producing industry) with single-region use tables, or an interregional use table (products by purchasing industry) with single-region make tables (see Jackson and Schwarm, 2011, for a further discussion). Eding et al (1999) started this development with a full set of bi-regional SuTs for the Netherlands, while Lindall et al (2006) and Schwarm et al (2006) developed alternative methodologies to estimate consistent commodity flow matrices for interregional social accounting models (SAMs) for the uSA.…”

Section: On the Construction Of Interregional Input-mentioning

confidence: 99%

Interregional input–output modeling: spillover effects, feedback loops and intra-industry trade

Hewings¹,

Oosterhaven²

2015

Handbook of Research Methods and Applications in Economic Geography

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Accounting foundations for interregional commodity-by-industry input-output models

Abstract: Interregional, Input-output, Commodity-by-industry, C67, D57, R15,

Cited by 17 publications

References 11 publications

Intraregional trade shares for goods‐producing industries: RPC estimates using EU data

Intraregional trade shares for goods‐producing industries: RPC estimates using EU data

An input–output virtual laboratory in practice – survey of uptake, usage and applications of the first operational IELab

Interregional input–output modeling: spillover effects, feedback loops and intra-industry trade

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