The Identification of Industrial Complexes
Bibliographic Data
| ID | 8378444 |
|---|---|
| Authors | Breandan O Huallachain (0000-0002-0621-0530, Northwestern University, corresponding author) |
| Year | 1984 |
| Volume | 74 |
| Issue | 3 |
| Pages | 420-436 |
| Publication date | 1984-03-01 |
| Peer Reviewed | Yes |
| Open Access | No |
| Type | ARTICLE |
| Venue | Annals of the Association of American Geographers (JOURNAL) |
| Journal identifiers | ISSN: 0004-5608 • E-ISSN: 1467-8306 |
| Publisher | Informa UK Limited (PUBLISHER • GB) |
| DOI | 10.1111/j.1467-8306.1984.tb01464.x |
| OpenAlex | W1974154168 |
| Language | EN |
| Citations received | 14 |
| References cited | 26 |
The objective of this paper is to reassess the value of principal components analysis as a technique for identifying regional industrial complexes, taking explicit account of the different kinds of relationships that are possible among industries. The empirical evidence offered here tends to confirm recent criticism of principal components analysis as a means for detecting vertical relationships, but it demonstrates the usefulness of this method with complementary relationships. In this study two principal components analyses were performed on a single regional input-output table for the state of Washington. Although initial inspection of component loadings did not always disclose industrial complexes, it was found that tracing the network of interactions, as indicated by the profiles of purchases and sales, revealed many critical links in a variety of industrial subsystems. A comparison of the results of this study with other analyses of Washington data suggests that the technique used here provides more information on the industrial structure than did the multivariate analysis conducted by Czamanski (1971) and the graph-theoretic approach of Campbell (1974). Also explored here are the links between principal components analysis and the general issue of aggregation in input-output analysis. Key Words: industrial complexinput-outputprincipal components analysisaggregation bias
Data mining · Econometrics · Economics · Graph · Identification (biology) · Industrial production · Multivariate statistics · Principal (computer security) · Principal component analysis · Statistics · Table (database) · Tracing · Artificial Intelligence · Computer Science · Environmental Impact and Sustainability · Global Trade and Competitiveness · Mathematics · Regional Economic and Spatial Analysis · Theoretical Computer Science
Industrial Agglomeration
Industrial Complex
The spatial dimension of knowledge flows
Simple industrial complexes
Testing the static and dynamic performance of statistical methods for the detection of national industrial clusters
Identifying industrial clusters from a multidimensional perspective
Industrial Geography
Sectoral Clustering and Growth in American Metropolitan Areas
The Rural Role in National Value Chains
Regional economic modelling through an embedded econometric–inter-industry framework
Spatial representations and policy implications of industrial co-agglomerations, a case study of Beijing
Linkages in High Technology Industries
Statistical Approaches to Structural Change in Regional Interindustry Models
The Development of a Measure of Intersectoral Connectedness by Using Structural Path Analysis
An introduction to regional economics.
Modern factor analysis
The location of economic activity
International Comparisons of the Structure of Production
Hierarchical Grouping to Optimize an Objective Function
Identification of Industrial Clusters and Complexes
Technological Similarity and Aggregation in Input-Output Systems
Economies of Scale and Regional Development
Some Empirical Evidence of the Strengths of Linkages Between Groups of Related Industries in Urban‐regional Complexes
The Essentials of Factor Analysis
Growth Pole Theory, Digraph Analysis and Interindustry Relationships
Local Industrial Complexes in Ontario
Selected Aspects of the Interindustry Structure of the State of Washington, 1967
| Unique citing works | 14 |
|---|---|
| Citations per year | 0,35 |
| Citation span | 1986 - 2020 (35) |
| Citation velocity | historical |
| Highly cited | No |
| Citation types | Neutral: 14 |