Visa trial of international trade: evidence from support vector machines and neural networks
Computer Science and Information Systems
Arthur J. Bauernfeind College of Business
International trade depends on networking, interaction and in-person meetings which stimulate cross-border travels. The countries are seeking policies to encourage inbound mobility to support bilateral trade, tourism, and foreign direct investments. Some nations have been implementing liberal visa regimes as an important part of facilitating policies in view of security concerns. Turkey has been among the nations introducing liberal visa policies to support trade in the last decade and recorded significant increases in the volumes of exports. In this paper, we employed machine learning methodologies, Support vector machines (SVM) and Neural networks (NN), to investigate the facilitating impact of liberal visa policies on bilateral trade, using the export data from Turkey for the period of 2000–2014. The research disentangled the variables that have the strongest impact on trade utilizing SVM and NN models and exhibited that visa policies have significant impacts on the bilateral trade. More relaxed visa policies are recommended for the countries in the pursuit of increasing exports.
This is an Accepted Manuscript of an article published by Taylor & Francis in Journal of Management Analytics on March 4, 2020, available online: https://doi.org/10.1080/23270012.2020.1731719