What Explains German Export Performances? Spatial Econometric Evidences: 1995 to 2014

Abstract

German exports achieved outstanding performances, yet there is lack of research utilizing spatial econometric evidences. This paper explores four explanations and evaluates their empirical contributions: (i) German exports were highly correlated to its imports. Thus, its exports built upon bilateral trade flows. (ii) German exported to countries with high GDP per capita with the capability and the demand of high-quality and less price-elastic goods. (iii) It exported to countries with economic integration with other countries such as free trade agreements. (iv) Its exports broadened from Europe to other countries in America and the Asia Pacific regio n with increasing total export-volume growth. Thus, German exports benefited from the free trade flow to a few EU member countries, those are close geographically and culturally to Germany. The empirical evidence also points out that the changing geospatial distribution of German exports is another key factors to its export success. The spatial Durbin model was identified to be the best fit model of all after a series of tests. Decisive determinants of its exports performance were found through the estimation besides geospatial analyses of its exports by employing Moran's I.Keywords: spatial econometrics, spatial effects, German exports distribution, exports determinants IntroductionGerman exports have long been well documented, and their performances and competitiveness widely studied. However, there is a significant lack of research on how Germany achieved outstanding export performances using spatial econometric evidences. This paper aims to fill the gap in the existing literature by investigating the changing geospatial distribution of its exports and identifying determinants to its outstanding performances over decades by applying spatial econometric models.We first employ Moran's I which is a useful indicator of the spatial association. The statistic of Moran's I can illustrate and show the decomposition of the association into spatial clusters (it is the "country" in the case of this study) and, therefore, it is more effectively observe and assess changing geospatial distribution of German exports during the period of this study.Then we examined the explanatory power of the spatial lag model (SLM) or the spatial error model (SEM), it is much better off than the conventional Pooled Ordinary Least Squares (OLS) by comparing the R-squared of each. The log-likelihood test, the LR test, and the robust LR test were employed to determine if the SLM is more favored than the SEM for the panel data of this study. The Hausman test was also utilized to decide that the fixed effects model is more appropriate than the random effects model. Wald test was used to determine that the spatial Durbin model (SDM) is more favored than the SLM.The SDM was finally identified to be the best fit model of all after the series of aforementioned tests. Decisive factors of German exports performances were found through this study besides geospatial analyses of the exports by emplo...