Nonsurvey techniques for constructing regional interindustry models

Schaffer, William A.; Chu, Kong

doi:10.1007/bf01941876

Cited by 118 publications

(34 citation statements)

References 1 publication

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“…x, -~ x d:, x;f : 5 0 J Unless such conditions hold, it is impossible to expect that the amount x: is realized. For more detailed discussion, see Schaffer and Chu (1969). We should note that a similar criticism can be applied to Round's work (1978) too.…”

Section: = a X + F + Ementioning

confidence: 88%

Nonsurvey Methods for Projecting the Input‐output System at a Small‐region Level: Two Alternative Approaches

Sasaki¹,

Shibata²

1984

Journal of Regional Science

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Section: = a X + F + Ementioning

confidence: 88%

Nonsurvey Methods for Projecting the Input‐output System at a Small‐region Level: Two Alternative Approaches

Sasaki¹,

Shibata²

1984

Journal of Regional Science

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“…220 the cells of the coefficient matrix to regional production specificities, and determined the scale of interregional export and import and then we ensured the fit to the regional data by using the RAS method. More technical details on these techniques can be found in [14][15][16][17][18][19][20][21][22][23][24][25][26].…”

Section: The Methods Of Accounting For the Blue Economy Innovation In mentioning

confidence: 99%

Blue Economy Innovation Impact Assessment with the GMR-Europe Model

Varga¹,

Hau-Horváth²,

Szabó³

et al. 2013

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This paper introduces and applies a model system that is suitable for the impact assessment of Blue Economy innovations. Our contribution to the literature is threefold. First, we build a multi-sector computable general equilibrium (CGE) model, which provides the theoretical frame for studying the economic impacts of using waste as a production input. Second, we create an empirical methodology through which new technologies of Blue Economy can be concretely accounted for in regional input-output tables. Since Blue Economy innovations are largely built on local inputs, their effects are primarily local. Given that interregional spillovers of local impacts might also be significant, through interregional trade or migration, we applied a modelling approach that is able to follow complex spatial processes. The broader model framework chosen is the GMR-Europe model.

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“…Thus, even if the disaggregate regional technology and industrial mix were identical to the national, the aggregate regional direct requirements matrix will still differ from the national to the extent that the region provides itself with smaller (or larger) percentages of any of its inputs. Czamanski and Malizia (1969) were probably the first to emphasize the potential importance of regionalizing the national technology before aggregating it; nevertheless, like Schaffer and Chu (1969) and others of that time, they found it much more practical to aggregate first and regionalize afterward.…”

Section: Interdependence Of Aggregation and Regionalizationmentioning

confidence: 95%

A Study of the Role of Regionalization in the Generation of Aggregation Error in Regional Input –Output Models

Lahr

Stevens

2002

Journal of Regional Science

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ABSTRACT.Although the need for aggregation in input-output modeling has diminished with the increases in computing power, an alarming number of regional studies continue to use the procedure. The rationales for doing so typically are grounded in data problems at the regional level. As a result many regional analysts use aggregated national input-output models and trade-adjust them at this aggregated level. In this paper, we point out why this approach can be inappropriate. We do so by noting that it creates a possible source of model misapplication (i.e., a direct effect could appear for a sector where one does not exist) and also by finding that a large amount of error (on the order of 100 percent) can be induced into the impact results as a result of improper aggregation. In simulations, we find that average aggregation error tends to peak at 81 sectors after rising from 492 to 365 sectors. Perversely, error then diminishes somewhat as the model size decreases further to 11 and 6 sectors. We also find that while region-and sector-specific attributes influence aggregation error in a statistically significantly manner, their influence on the amount of error generally does not appear to be large.

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Nonsurvey techniques for constructing regional interindustry models

Cited by 118 publications

References 1 publication

Nonsurvey Methods for Projecting the Input‐output System at a Small‐region Level: Two Alternative Approaches

Nonsurvey Methods for Projecting the Input‐output System at a Small‐region Level: Two Alternative Approaches

Blue Economy Innovation Impact Assessment with the GMR-Europe Model

A Study of the Role of Regionalization in the Generation of Aggregation Error in Regional Input –Output Models

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