A Study on the Development of Global Competitiveness Index for Local Governments in Korea

Abstract

Purpose - Since 2008, A.T. Kearney has published a Global Cities Index (GCI), which has been evaluated in five areas and 27 detailed evaluation indicators, in 60 major cities in 40 countries. In this study, we selected various indicators necessary for the construction of global competitiveness index of local governments in Korea based on the evaluation system of A.T. Kearney. Through the process of indicator selection, normalization, and aggregation using various weights the global competitiveness index of 16 local governments were calculated, compared and analyzed. Research, design, data and methodology - The study selected 13 indicators of 16 local governments to construct the global competitiveness index. In normalization of transforming individual indicators the parametric method of z-score standardization and nonparametric method which is free from the distribution types are used. Nonparametric methods used in this study include rescale with respect to the maximum value, range standardization, and inter-decile range standardization. In aggregation the global competitiveness index was calculated using weights derived from Analytic Hierarchy Process(AHP), Principal Component Analysis(PCA) and Unobserved Components Model(UCM). Results - The rankings of global competitiveness index using various combinations of normalization method and weight-determination method were calculated and compared. The correlation between the ranks was statistically significant, which implies that all measurement methods are meaningful and useful. It was also found that the method of standardization does not affect the ranking of the composite index while the method of determining the weights affects the ranking of the composite index. Although both the PCA and UCM methods have their advantages and disadvantages in determining weights and making composite index, it is judged that individual indicators can be selected by PCA and then the composite index are developed by UCM in terms of the usefulness of the study. Conclusions - The method for aggregating indicators used in this study is designed to calculate the level, ranking, and confidence interval of a global competitive index. Therefore, it enables us to overcome the limitations of simply calculating the level an ranking of a composite index by arbitrary weight. This method is expected to be very useful in other areas of study.