Input-Output Table Based Method of Cluster Identification

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Henryk Gurgul
Paweł Majdosz


Keywords : Input-output model, clusters, triangulization method
Abstract
An idea beyond the triangulization method is that an intermediate demand should be categorized as an intra-cluster if it is large enough not only from economy-wide perspective. Also it should be considered as a significant one by both buyer and seller. Doing so, the triangulization method provides a better insight into the actual structure of economy by distinguishing between three types of links, namely intra-cluster flows, flows which take place between two sectors belonging to different clusters, and flows from sector within cluster to sector outside of clusters. It turned out that the triangulization method leads to the solution which is, at least, as good as those of the diagonalization method. In addition, the solution obtained by using this method is irrespective of applying the intermediate demand matrix, input coefficient matrix, or output coefficient matrix. On the other hand, when applying the Leontief inverse, the identified clusters are different compared to those in the case where we used one of the above-mentioned matrices. It is worth noting that clusters seem to be a quite important in the Polish economy. The sectors belonging to the clusters created approximately a 70 per cent of gross output in 2000, and a 60 per cent of value added, final consumption expenditures, import and export in the same year. However, this conclusion should be drawn with care due to the fact that the high aggregation (55 x 55 sectors) was used in this study. Therefore, this part of our investigation should be repeated in the future at low level of data aggregation as far as possible.

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How to Cite
Gurgul, H., & Majdosz, P. (2006). Input-Output Table Based Method of Cluster Identification. Zeszyty Naukowe SGGW - Ekonomika I Organizacja Gospodarki Żywnościowej, (60), 103–112. https://doi.org/10.22630/EIOGZ.2006.60.31
References

Borkowski B., Dudek H., Szczesny W.(2003): Ekonometria. Wybrane zagadnienia. Wydawnictwo Naukowe PWN, Warszawa.

Brown R.G. (1959). Statistical Forecasting for Inventory Control, McGraw-Hill, New York Analysis Modelling Simulation, 42, 1361-76.

Antonelli, C. (1999). The Microdynamics of Technological Change (London, Routledge).

DeBresson, C. (1996). Economic Interdependence and Innovative Activity: An InputOutput Analysis (Cheltenham, Edward Elgar).

Eding, G. J., Oosterhaven, J., Stelder, D. (2001). Clusters, Linkages and Regional Spillovers: Methodology and Policy Implications for the two Dutch Mainports and the Rural North, Regional Studies, 35 (9), 809-22. (Crossref)

Gurgul, H., Majdosz, P. (2005). Key Sector Analysis: A Case of the Transited Polish Economy, Managing Global Transitions, 3 (1), 95-111.

Hauknes, J. (1998). Norwegian Input-Output Clusters and Innovation Patterns, STEP Report Series, No. R-15

Hoen, A. (2002). Identifying Linkages with a Cluster-based Methodology, Economic Systems Research, 14 (2), 131-45. (Crossref)

Krugman, P. (1991). Geography and Trade (Cambridge MA, MIT Press).

Krugman, P., Venables, A.J. (1996). Integration, Specialization, Adjustment, European Economic Review, 40, 959-68. (Crossref)

Porter, M. E. (1998). On Competition (Boston, Harvard Business Review).

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