Machine learning tools are well known for their success in prediction. But prediction is not causation, and causal discovery is at the core of most questions concerning economic policy. Recently, however, the literature has focused more on issues of causality. This paper gently introduces some leading work in this area, using a concrete example—assessing the impact of a hypothetical banking crisis on a country’s growth. By enabling consideration of a rich set of potential nonlinearities, and by allowing individually-tailored policy assessments, machine learning can provide an invaluable complement to the skill set of economists within the Fund and beyond.

Machine learning tools are well known for their success in prediction. But prediction is not causation, and causal discovery is at the core of most questions concerning economic policy. Recently, however, the literature has focused more on issues of causality. This paper gently introduces some leading work in this area, using a concrete example—assessing the impact of a hypothetical banking crisis on a country’s growth. By enabling consideration of a rich set of potential nonlinearities, and by allowing individually-tailored policy assessments, machine learning can provide an invaluable complement to the skill set of economists within the Fund and beyond.

The Financial Action Task Force’s gray list publicly identifies countries with strategic deficiencies in their AML/CFT regimes (i.e., in their policies to prevent money laundering and the financing of terrorism). How much gray-listing affects a country’s capital flows is of interest to policy makers, investors, and the Fund. This paper estimates the magnitude of the effect using an inferential machine learning technique. It finds that gray-listing results in a large and statistically significant reduction in capital inflows.

Kingdom of Lesotho: Selected Issues

In this paper, we study systemic non-financial corporate sector distress using firm-level probabilities of default (PD), covering 55 economies, and spanning the last three decades. Systemic corporate distress is identified by elevated PDs across a large portion of the firms in an economy. A machine-learning based early warning system is constructed to predict the onset of distress in one year’s time. Our results show that credit expansion, monetary policy tightening, overvalued stock prices, and debt-linked balance-sheet weaknesses predict corporate distress. We also find that systemic corporate distress events are associated with contractions in GDP and credit growth in advanced and emerging markets at different degrees and milder than financial crises.

In a highly competitive global market, companies need to equip themselves with best practices and strategies to survive. Strategic management, innovative managerial thinking, and a clear decision-making process must be utilized to boost company performance and ultimately drive the company’s success. The Handbook of Research on Managerial Thinking in Global Business Economics identifies the importance of strategic decision making in competitive environments and analyzes the impacts of managerial thinking on global financial economics. The content within this publication examines globalization, consumer behavior, and risk management. It is designed for researchers, academicians, policymakers, government officials, and managers, and covers topics centered on innovation and development within organizations.

Economic volatility has come into its own after being treated for decades as a secondary phenomenon in the business cycle literature. This evolution has been driven by the recognition that non-linearities, long buried by the economist's penchant for linearity, magnify the negative effects of volatility on long-run growth and inequality, especially in poor countries. This collection organizes empirical and policy results for economists and development policy practitioners into four parts: basic features, including the impact of volatility on growth and poverty; commodity price volatility; the financial sector's dual role as an absorber and amplifier of shocks; and the management and prevention of macroeconomic crises. The latter section includes a cross-country study, case studies on Argentina and Russia, and lessons from the debt default episodes of the 1980s and 1990s.

The growth of manufacturing industries is one of the key sectors in helping to mitigate global recessions. Productivity Growth in the Manufacturing Sector thoroughly discusses issues and potential remedies of this sector for a range of international countries.

This book considers and assesses essential financial issues by utilizing data science and fuzzy multiple criteria decision making (MCDM) methods. It introduces readers to a range of data science methods, and demonstrates their application in the fields of business, health, economics, finance and engineering. In addition, it provides suggestions based on the assessment results on each topic, which can help to enhance the efficiency of the financial system and the sustainability of economic development. Given its scope, the book will help readers broaden their perspective on the assessment and evaluation of financial issues using data science and MCDM approaches.