Given the increasing use of technology and the digitalization of international trade through electronic documents, there is a need for a globally harmonized standard that caters for the legal aspects of digitalization. The Model Law on Electronic Transferable Records (MLETR) is one such law. Yet, it has not been adopted in Nigeria or several other jurisdictions. This article considers the possibility of Nigeria adopting the MLETR. To do this, the article considers the meaning of electronic transferable documents and the legal implications of digitalizing them. The article also examines the barriers and challenges to digitalizing electronic transferable records. It then considers some of the laws in Nigeria that would support electronic and digital trade transactions. Subsequently, the article highlights the benefits, challenges and hindrances to the adoption of the MLETR in Nigeria. It recommends an approach to adopting the MLETR, drawing from jurisdictions that have adopted it.

This article examines the nationwide campaign by financial reformers in the 1910s to convince businesses across the United States to abandon established commercial credit practices and use trade acceptances—the quintessential “real bill”—in their stead. The creation of the Federal Reserve System (Fed) and the outbreak of World War I offered a powerful coalition of campaigners the opportunity to forcefully argue that by capitalizing open account credit, trade acceptances fostered good business practices and stabilized the banking and financial systems. These campaigners relied on trade associations to disseminate, and the federal government to legitimize, their message. While some firms obliged, many businesses and banks criticized the campaigners’ arguments, casting trade acceptances as a means of financial centralization and as being contrary to the American culture of credit. Trade acceptances did not supplant promissory notes or trade in the open market and were rarely used by banks to access Fed liquidity. Instead, their legacy lies in their adoption by finance companies in the hope of securing financing for the distribution and mass consumption of consumer durables.

In this contribution, we exploit machine learning techniques to evaluate whether and how close firms are to become successful exporters. First, we train various algorithms using financial information on both exporters and non-exporters in France in 2010–2018. Thus, we show that it is possible to predict the distance non-exporters are from export status. In particular, we find that a Bayesian Additive Regression Tree with Missingness In Attributes (BART-MIA) performs better than other techniques with an accuracy of up to 0.90. Predictions are robust to changes in definitions of exporters and in the presence of discontinuous exporting activity. Eventually, we discuss how our exporting scores can be helpful for trade promotion, trade credit, and assessing aggregate trade potential. For example, back-of-the-envelope estimates show that a representative firm with just below-average exporting scores needs up to 44% more cash resources and up to 2.5 times more capital to get to foreign markets.

The reduction in international trade has been more than the reduction in economic activity during the 2008 financial crisis, against the one-to-one relationship between them implied by standard trade models. This so-called the Great Trade Collapse (GTC) has been investigated extensively in the literature resulting in alternative competing stories as potential explanations. By introducing and estimating a dynamic stochastic general equilibrium model using 18 quarterly series from the USA, including those that represent the competing stories, this paper evaluates the contribution of each story to GTC. The results show that retail inventories have contributed the most to the collapse and the corresponding recovery, followed by protectionist policies, intermediate-input trade, and trade finance. Productivity and demand shocks have played negligible roles.