SECTORAL GROWTH DYNAMICS OF COUNTRY GROUPS: A COUNTRY GROUPING SUGGESTION

Abstract

Purpose- In the study, the effects of sectors on the growth of OECD member countries were determined by using the Fuzzy Goal Programming method. These findings may help policymakers see sector impacts that help countries in their growth targets. The study aims to contribute to the literature in two ways. The first of these analyses are based on long-term economic growth and primary sector analysis. The second contribution is to propose an alternative empirical methodology with clustering analysis which is not used to obtain the basic assumption of homogeneity in the application of panel data analysis. Methodology- The effects of sectors on the growth of OECD member countries were determined by using the Fuzzy Goal Programming method. In the second step, countries were divided into groups using K-means clustering analysis according to these impact values. With the help of these weights, the growth dynamics of similar countries and the contributions of sectors to this dynamic were obtained. Findings- Countries analyzed in terms of the contribution of sectoral growth rates to the growth rate of the country were divided into groups by cluster analysis. It is determined that the countries grouped in terms of the contribution of sectors to growth are divided into 5 groups. The first group has 10 member countries. The second group has 12 countries and the third group it has 7 countries, the fourth group has 4 countries and only 1 country belongs to the fifth group. The countries in group 1 are Estonia, Turkey, Greece, Italy, Poland, Portugal, Lithuania, Latvia, Slovakia, and Slovenia. The countries in group 2 are Australia, Belgium, Czech Republic, Germany, Denmark, Hungary, Ireland, Mexico, Netherlands, Norway, Sweden, and New Zealand. The countries in group 3 are Austria, Spain, Finland, France, the Republic of Korea, Luxembourg, Switzerland, the USA, Israel, Costa Rica, the United Kingdom, and Japan. Conclusion- Countries that have similar sectoral structures can analyze growth with panel data analysis, but it is important to form homogeneous groups while doing this analysis. For this reason, another critical suggestion it is offered based on the study is the use of FGP methodology in the analysis method.

Keywords

• Economic growth
• sectoral growth
• Fuzzy Goal Programming
• Cluster Analysis
• Panel VAR

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