Trade impacts of the New Silk Road in Africa: Insight from Neural Networks Analysis

Abstract

The Belt and Road Initiative (BRI) is aimed to strengthen the preferential reciprocal trade between China and the Belt-Road nations. Quantitative evaluations of BRI to determine whether it can explicitly provide more insight into China’s bilateral trade among its partners are needed. Hence, improving prediction accuracy while using more superior algorithms for sustainable decision-making remains essential since decision-makers have been interested in predicting the future. Machine learning algorithms, such as supervised artificial neural networks (ANN), outperform several econometric procedures in predictions; therefore, they are potentially powerful techniques to evaluate BRI. This study uses detailed China’s bilateral export data from 1990 to 2017 to analyze and evaluate the impact of BRI on bilateral trade using gravity model estimations and ANN analysis techniques. The finding suggests that China’s bilateral export flow among the BRI countries results in a slight increase in inter-regional trade. The study provides a comparison view on the different estimation procedures of the gravity model – ordinary least squares (OLS) and Poisson pseudo-maximum likelihood (PPML) with the ANN. The ANN associated with fixed country effects reveals a more accurate estimation compared to a baseline model and with country-year fixed effects. Contrarily, the OLS estimator and PPML showed mixed results. Grounded on the study dataset, the ANN estimation of the gravity equation was superior over the other procedures to explain the variability of the dependent variable (export) regarding the prediction accuracy using root mean squared error (RMSE) and R-square.