## 4 Discussion  
Understanding the difference in economic development among countries or regions is a long-standing issue in economics. A crucial perspective to shed light on the issue is to evaluate competitiveness of a country in international trade as characterized by the RCA index. Although it is desirable to have a normally distributed RCA, empirical studies have often found the opposite. This discrepancy has stimulated a lot of researchers to develop alternative indices of the RCA index so that their distributions would be closer to Gaussian distributions. Yet, those efforts are not very successful. This calls for a deeper understanding of the significance of a normally distributed RCA index. To gain insights into this issue, we have defined a quantity, DfG, based on the KS test, which quantifies the deviation of the distribution of a country's RCA index from normality. We have found that the variations of DfG are characterized by spatiotemporal heterogeneity and regional spatial agglomeration. The spatiotemporal heterogeneity of the DfG refers to the significant differences in many countries' DfG and their dynamic evolution. Regional spatial agglomeration of the DfG refers to that countries with smaller DfG are mainly concentrated in North America (represented by USA), Western Europe (represented by Germany), and Eastern Asia (represented by China, Japan and South Korea). Interestingly, these three areas are very consistent with the description of \"The world seems to have three interconnected production hubs for the extensive trade in parts and components\" in the \"Global Value Chain Development Report 2017--Measuring and Analyzing the Impact of GVCs on Economic Development\". It suggests that the DfG has some connections with the development of GVCs. On the other hand, countries with larger DfG are mainly concentrated in Africa, South America, Western and Southern Asia. The pattern of DfG's spatial variation suggests that the DfG can act as a good indicator of a country's economic performance. This is indeed so, as DfG is found to be strongly negatively related with both the logarithm of GDP and the ECI. Therefore, the closer the distribution of a country's RCA index to a normal distribution, the higher degree of economic complexity and better economic performance of the country. This highlights the optimality of a country's export when its RCA index follows a normal distribution, and provides a new perspective to understand the difference in economic development among countries or regions. Furthermore, we have found that the correlation coefficients between DfG and the logarithm of GDP are persistently larger than those between ECI and the logarithm of GDP after the 2008 global financial crisis. This is further corroborated by regression analysis which shows that DfG better explains the variance in GDP than ECI. Further Granger causality analysis shows that DfG and ECI are the Granger cause of \(\ln\) GDP, but not the vice versa. It is worth emphasizing that Gaussianity is not a cause, it is more a consequence indicating economic development. The last feature, that DfG is more strongly correlated with GDP than ECI, suggests an interesting way to improve characterization of economic complexity of a country. For this purpose, we need to first understand the meaning of the correlation between ECI and the logarithm of GDP. This is due to the strong correlation between export and GDP--ECI amounts to retaining only products with RCA equal to or greater than 1 and approximating