Chapter 6: Econometric Findings
6. Econometric findings REGRESSION ANALYSIS FOR GUCI INDICATOR Based on detailed analysis on determining mechanisms and influencing factors of urban competitiveness, this chapter examines the different importance of each indicator to urban comprehensive competitiveness and GDP per capita from the perspective of econometric analysis. A better understanding of positions and roles of these influencing factors can help us set up more targeted strategies for urban development and competition. In this chapter, regression analysis is conducted on the basis of URCI indicator tests, using indicators (including level-I indicators and key level-II indicators) in input factor competitiveness indicators system as independent variables, and GUCI as a dependent variable. In the meantime, regression analysis is also conducted using GDP (Green) per capita as an independent variable and urban competitiveness factor indicators as dependent variables. Please refer to Table 6.1 for general results of this analysis. Comprehensive Competitiveness and Level-I Factor Indicator: Global Connection is the Most Important Regression analysis on the 6 level-I explanatory indicators show that: Global Connection has the biggest influence on GUCI, with a regression coefficient of 1.0849 and a goodness of fit (R2) of 0.7436. Please see Table 6.1 for regression coefficient, goodness of fit and correlation coefficient of the indicators. General regression coefficients show the extents of impact of the indicators. Figure 6.1 shows the impact of the 6 indicators on the competitiveness of the cities in order of their significance: Global Connection > Local Supply > Enterprise Quality > Public Institution > Inner Structure > Local Demand. In an era of economic...
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