Algorithm for Defining Clusters based on Input–Output Tables: Case of Construction Cluster of Russia

Abstract

This research presents an algorithm for cluster identification based on input–output matrixes. Authors present an algorithm for downstream and upstream analysis of the symmetrical input–output matrix, which allows definition of the top input and output suppliers and consumers for each industry. As a result of the algorithm, related industries and clusters can be defined. The program, which implements the proposed algorithm, was written using Python. In this paper, the algorithm is applied to the analysis of «Construction» industry of Russia. We used the latest input–output matrix available for Russia for 2016, which contained information on 98 industries. We defined clusters and industries that are the top suppliers and consumers of the «Construction» industry. Among the top suppliers for the «Construction» industry are «Metal manufacturing», «Automotive cluster», and «Chemical products cluster», which account for 15.01%, 9.63%, and 5.95% of overall consumption, respectively. Top consumers of the «Construction» industry are «Public administration and defense; compulsory social security», «Real estate activities», and «Human health and social work activities», which account for 23.3%, 12.19%, and 6.26%, respectively, in the volume of output. The proposed algorithm can be used for analyzing input–output matrixes and cluster identification. Using the results of its application, the decision-makers can elaborate on policy for supporting the cluster-based development of the regions.

Keywords

• construction cluster
• input–output matrixes
• regional specialization