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The aim of this chapter is to develop a strong research base for the academia and the industry to understand the importance of data analytics in International Trade. This chapter focuses on the case of cotton trade from India and explores different methodologies developed by the World Bank and International Trade Center to analyze the Big Data available on export and import. Through Big Data analysis, this chapter attempts to find out the export performance, market demand, export potential, and attractive markets for Indian cotton. This chapter also explores the trade competitiveness of Indian cotton over the years. The data through appropriate analysis can address some simple yet complicated questions in trade like what export potential the commodity holds, if the commodity is competitive or not in international market, what are new markets to look up to, and other similar questions. In other words, this information could make huge difference in decision-making of traders and policymakers directly, and farmers indirectly.

This paper modifies the “standard” methodology for calculating the economic opportunity cost of foreign exchange (EOCFX), so as to incorporate into its calculation the distortions involved in the act of “sourcing” in the capital market the funds that will be spent by the project. Once we take these “sourcing” distortions into account, we are logically forced to pursue two parallel calculations. The first, EOCFX traces the results of sourcing money in the capital market and spending it on tradables. The second, the shadow price of nontradables outlays (SPNTO) traces the results of sourcing money in the capital market and spending it on nontradables. Supporting arguments and illustrative calculations are presented in the paper.

Trade in food and other agricultural products is increasingly important across East and Southeast Asia, where high-income Asian economies have driven significant agricultural expansion, and the People's Republic of China's (PRC) momentous growth promises more stimulus to agro-food activity in the region. The PRC is expected to become a net importer of agro-food in the coming decades, which will have significant implications within the region. As its middle class continues to emerge, the resource intensity of food consumption (e.g., meat and dairy) will lead to net imports and require expansion of agricultural capacity elsewhere. Because low-income Southeast Asia is generally seen to be well below its agro-food potential, this situation suggests a significant opportunity for self-directed poverty reduction through regional agro-food market expansion. This chapter reviews the history of high-income Asia and the PRC's emergence in the region's agro-food markets. Finally, the Greater Mekong Subregion's role is analyzed for the potential of Asian agro-food trade to contribute to poverty reduction.

The analysis of the main stages of the Belarusian economy's development from 2000 to 2018 has been carried out, the main factors and limitations of economic growth considered. The known models of economic growth applied to the Belarusian economy are shown. It is grounded that the correct use of endogenous growth models based on production functions is hampered by the significant influence of exogenous factors on the Belarusian economy, and it is more preferable to use models based on the balance of payments under these conditions. The methodology for modeling the balanced economic growth based on Input–Output tables' data is proposed, the results of its use are shown. Three scenarios for the growth of the Belarusian economy until 2025 are analyzed: baseline, adverse, and target; the desired structural changes are identified for the implementation of the target scenario.