Estimating China’s Trade with Its Partner Countries within the Belt and Road Initiative Using Neural Network Analysis

Abstract

The Belt and Road Initiative (BRI) under the auspices of the Chinese government was created as a regional integration and development model between China and her trade partners. Arguments have been raised as to whether this initiative will be beneficial to participating countries in the long run. We set to examine how to estimate this trade initiative by comparing the relative estimation powers of the traditional gravity model with the neural network analysis using detailed bilateral trade exports data from 1990 to 2017. The results show that neural networks are better than the gravity model approach in learning and clarifying international trade estimation. The neural networks with fixed country effects showed a more accurate estimation compared to a baseline model with country-year fixed effects, as in the OLS estimator and Poisson pseudo-maximum likelihood. On the other hand, the analysis indicated that more than 50% of the 6 participating East African countries in the BRI were able to attain their predicted targets. Kenya achieved an 80% (4 of 5) target. Drawing from the lessons of the BRI and the use of neural network model, it will serve as an important reference point by which other international trade interventions could be measured and compared.

The United States’ Clothing Imports from Asian Countries along the Belt and Road: An Extended Gravity Trade Model with Application of Artificial Neural Network

“…The ANN with the best predictive ability is identified by comparison of RMSE of the testing dataset with unseen data across different networks. Similar to Dumor and Yao [4], this study uses Rectified Linear Units (ReLU) as the activation function. The ANNs are trained using the stochastic gradient descent optimizer with mean squared error (MSE) as the loss function.…”

“…The ANNs are trained using the stochastic gradient descent optimizer with mean squared error (MSE) as the loss function. Instead of dividing the dataset into training and validation sets in one go (e.g., [4,7]), this study applies K-fold cross-validation for training and validation of each ANN. This method provides more robust models and combats over-fitting the model [43].…”

“…First, it addresses an important yet under-researched area of bilateral trade under the BRI. Although more empirical studies have focused on trade along the B&R, they tend to examine trade flows between China and its trading partners at the country level (e.g., [3][4][5]) and not trade between B&R and non-B&R countries at the sectoral level. As the BRI aims to promote unimpeded trade through better connectivity of infrastructure and facilities across geographical boundaries, developing countries that have joined the BRI would have a chance to build stronger links to global value chains that connect to high-profit markets that do not necessarily have to be part of the B&R region (e.g., the US).…”

“…More recently, advances in big data availability and affordable high computing power and online platforms have made ANN more accessible for researchers. The use of ANN in this study is relevant and useful not only because of its higher predictive power but also because of its ability to estimate complex trade relationships [4,7]. Although more Asian developing countries have joined the BRI, India is an exception as it has concerns about the expansion of Chinese political influence and interests across South Asia through the BRI [8].…”

An Exploratory Analysis of Networked and Spatial Characteristics of International Natural Resource Trades (2000–2016)

“…To date, a few studies on natural resource trade have focused on a limited number of countries and products [23][24][25][26][27][28][29][30], but a systematic exploration of global scope is lacking. For example, the research into trade networks has been confined to local areas and organizations, such as the Belt and Road trade network [29][30][31][32][33][34] and North American Free Trade Area (NAFTA) [35][36][37]. International trade issues of specific resources, namely, coal [38][39][40], oil [41,42], scrap metal [6], and natural gas [28], have been investigated in detail.…”