| Abstract [eng] |
The thesis consists of three main parts. In the first part, the concept of a cluster is widely discussed in different theoretical approaches, mainly focusing on two main streams: new economic geography and competitiveness. Despite different cluster definitions, both theoretical streams assume that clusters are the result of the scale of the economy in a region. Even though the cluster term is widely accepted by research and political community, it has uncertainty while applying in different theoretical regional economics concepts. This was proved by analyzing different cluster identification methods in different countries in the second part of the thesis. In addition, the methods of clusters’ impact on the economy and innovation in the regions were analyzed and compared in this part of the thesis. Models for impact on economy assessment mainly exploit various employment variables such as employment itself and its growth, average annual wage and its growth using regression models. Models for clusters’ impact on innovation mainly exploit patents as dependent variables using the knowledge production approach. Despite some proves of productivity growth as the main result of clustering business’ economic activities, productivity according to the best of thesis author’s knowledge was not used to identify clusters in a region. Thus, the cluster identification algorithm was constructed for three the Baltic States (Estonia, Latvia, Lithuania) done by exploiting employment and productivity variables and tested in the third part of the thesis. Identified clusters’ impact on the economy was explored by using panel data and regression analysis. |
