Economic development quality evaluation of underdeveloped regions based on principal component analysis and grey correlation analysis: Empirical evidence from guizhou of China

Underdeveloped regions widely exist in China, a developing country with unbalanced economic development, which makes high-quality development indispensable for these regions. Therefore, the accurate measurement of economic development of these regions and the identification of factors affecting high-quality economic development are crucial. Guizhou in China has developed a distinctive high-quality development approach under the guidance of new development concepts. This study utilizes principal component analysis (PCA) to reduce the dimensionality of the indicators to avoid impacts of subjective weighting on empirical results so as to comprehensively and objectively reflect original information of the indicators. After the non-dimensionalization, a quality evaluation model was established including 6 primary indicators (i.e. quality of economic performance, investment quality, quality of driving forces, industrial quality, service quality and environmental quality) and 27 basic indicators, which completely measures economic development quality of Guizhou. Furthermore, the grey correlation analysis (GCA) model was applied to verify the feasibility of aforesaid evaluation model. The results indicate that: (1) The quality of driving forces and service quality are important indicators measuring the improvement of economic development quality; (2) PCA model can objectively measure economic development quality of underdeveloped regions; (3) Economic development quality of underdeveloped regions is generally increasing despite fluctuations; (4) Basic indicators are significantly correlated with economic development quality indexes in general. PCA and GCA model can be combined to objectively and comprehensively reflect original information of the indicators and completely measure economic development quality of underdeveloped regions. This approach compensates the shortcomings of subjective evaluation methods and may provide insights on new research methods and ideas concerning said research topic.