Application of economic distance for the purposes of a spatial analysis of the unemployment rate for Poland


  • Michał Bernard Pietrzak Nicolaus Copernicus University in Torun



spatial econometrics, unemployment rate, spatial weight matrix, SAR model, economic distance


The article presents the problem of the application of the spatial weigh matrix based on economic distance in spatial analysis of the unemployment rate. The spatial weight matrix expresses potential spatial interactions between the researched areas and forms a basis for the instruments applied in spatial econometrics. While identifying the neighbourhood, the following criteria are used: a common border, distance, and the k number of the nearest neighbours. The potential force of impact is identified by means of the standardisation of the matrix by rows to unity, or by means of the distance based on the physical properties of the areas. The disadvantage of the matrix standardisation is the fact of accepting the same force of impact for all the areas. It seems natural is the differentiation of the force of the impact dependent on the selected areas which should result from the differences and similarities of the areas in the scope of the researched phenomenon and its determinants. The use of the distance based on physical properties of the areas allows considering the diverse force of impact of neighbouring areas, which, in turn, allows to obtain a more precise outcome of analyses. Unfortunately, physical properties do not constitute the determinants of economic phenomena covered by a spatial analysis which means that they are not related directly to the scrutinised phenomenon. The application of economic distance for building spatial weight matrix shown in the present paper constitutes a way of determining of the force of impact for the economic spatial processes that is alternative to the distance based on physical properties of the researched areas and to the proposal of the standardisation by rows to unity.


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Abreu M., de Groot H. L. F., Florax R. J. G. M. (2004), Space and Growth: A Survey of Empirical Evidence and Methods, Tinbergen Insititute Working Paper No. Ti 04-129/3
Anselin L. (1988), Spatial Econometrics: Method and Models, Kluwer Academic Publishers, Netherlands.
Anselin L., Florax R. J. G. M., Rey, S. J. (2004), Advances in Spatial Econometrics. Methodology, Tools and Applications, Springer-Verlag, Berlin.
Arbia G. (2006), Spatial Econometrics, Springer-Verlag GmbH.
Bivand R. (1981), Modelowanie geograficznych układów czasoprzestrzennych, PWN, Warszawa-Poznań
Bivand, R., S., Pebesma, E. J., Gómez-Rubio, V. (2008), Applied Spatial Data Analysies with R, Springer, New York.
Clif A., Ord J. (1973), Apatial Autocorrelation, Pion, London.
Clif A., Ord J. (1981), Apatial Processes, Models and Applications, Pion, London.
Klaassen J. H. P., Paelinck L. H., Wagenaar S. (1982), Systemy przestrzenne, PWN, Warszawa
Klaassen J. H. P., Paelinck L. H. (1983), Ekonometria przestrzenna, PWN, Warszawa
Kopczewska K. (2006), Ekonometria i statystyka przestrzenna, CeDeWu, Warszawa.
LeSage J. P., Pace R. K. (red) (2004), Advances in Econometrics: Spatial and Spatiotemporal Econometrics, Elsevier, Amsterdam.
LeSage J.P., Pace R. K. (2009), Introduction to Spatial Econometrics, CRC Press.
Schabenberger, O., Gotway, C. A. (2005), Statistical Methods for Spatial Data Analysis, Texts in Statistical Science, Chapman & Hall/CRC, Taylor &Francis Group, Boca Raton, London.
Suchecki B. (2010), Ekonometria przestrzenna. Metody i modele analizy danych przestrznnych, Wydawnictwo C.H.Beck, Warszawa
Szulc E. (2007), Ekonometryczna analiza wielowymiarowych procesów gospodarczych, Wydawnictwo UMK, Toruń 2007.
Zeliaś A. (red) (1991), Ekonometria przestrzenna, PWE, Warszawa.




How to Cite

Pietrzak, M. B. (2010). Application of economic distance for the purposes of a spatial analysis of the unemployment rate for Poland. Oeconomia Copernicana, 1(1), 79–98.