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 rate of youth unemployment has been a major challenge for preceding and consecutive Ghanaian governments. There is evidence to suggest how different economic interventions to bridge this gap have failed to yield the expected results. The intended beneficiaries of these interventions are caught in a quagmire of what they perceive as opportunities within the environment which are sometimes divergent from that of government. This is due to the influencing role of two main factors: their knowledge and perception. We carry out a study to reveal the relationship that exist between entrepreneurial environment and entrepreneurial intentions and how knowledge and perception of the youth mediate this relationship. A sample survey of 187 youth using questionnaires were administered and the results analyzed using the partial least squares–structural equation modeling (PLS-SEM). Overall, entrepreneurial knowledge and perception variables showed a partial mediation in their relationship with entrepreneurial environment and entrepreneurial intention. Entrepreneurial perception showed a higher mediating effect of the two mediators on the target construct, entrepreneurial intention. Entrepreneurial perception showed the highest Cronbach’s alpha of .90 of all the variables. We advance the theory of entrepreneurship intention to show how the dual mediating interactivities are influenced by the entrepreneurship environment. The need to build a supportive entrepreneurial environment is critical in transforming the youth perception to boost their knowledge in entrepreneurship to engage in business enterprises. These are critical for policy makers, implementers, and entrepreneurial trainers.

Recent research suggests that China’s Belt and Road Initiative (BRI) would improve the bilateral trade between China and its partners. This article uses detailed bilateral export data from 1990 to 2017 to investigate the impact of China’s BRI on its trade partners using neural network analysis techniques and structural gravity model estimations. Our main findings suggest that the BRI countries would raise exports by a modest 5.053%. This indicates that export and network upgrades should be considered from economic and policy perspectives. The results also show that neural networks is more robust compared with structural gravity framework.