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We leverage insights from machine learning to optimize the tradeoff between bias and variance when estimating economic models using pooled datasets. Specifically, we develop a simple algorithm that estimates the similarity of economic structures across countries and selects the optimal pool of countries to maximize out-of-sample prediction accuracy of a model. We apply the new alogrithm by nowcasting output growth with a panel of 102 countries and are able to significantly improve forecast accuracy relative to alternative pools. The algortihm improves nowcast performance for advanced economies, as well as emerging market and developing economies, suggesting that machine learning techniques using pooled data could be an important macro tool for many countries.
Macroeconomics --- Intelligence (AI) & Semantics --- Forecasting and Other Model Applications --- Neural Networks and Related Topics --- Technological Change: Choices and Consequences --- Diffusion Processes --- Macroeconomics: Production --- Machine learning --- Production growth --- Technology --- Production --- Economic theory --- Costa Rica
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This paper presents a set of collaborative filtering algorithms that produce product recommendations to diversify and optimize a country's export structure in support of sustainable long-term growth. The recommendation system is able to accurately predict the historical trends in export content and structure for high-growth countries, such as China, India, Poland, and Chile, over 20-year spans. As a contemporary case study, the system is applied to Paraguay, to create recommendations for the country's export diversification strategy.
Exports and Imports --- Macroeconomics --- Intelligence (AI) & Semantics --- Economic Development, Innovation, Technological Change, and Growth --- Trade: General --- Neoclassical Models of Trade --- Technological Change: Choices and Consequences --- Diffusion Processes --- Personal Income, Wealth, and Their Distributions --- International economics --- Machine learning --- Exports --- Export diversification --- Comparative advantage --- Personal income --- International trade --- Technology --- National accounts --- Income --- Paraguay
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We develop a framework to nowcast (and forecast) economic variables with machine learning techniques. We explain how machine learning methods can address common shortcomings of traditional OLS-based models and use several machine learning models to predict real output growth with lower forecast errors than traditional models. By combining multiple machine learning models into ensembles, we lower forecast errors even further. We also identify measures of variable importance to help improve the transparency of machine learning-based forecasts. Applying the framework to Turkey reduces forecast errors by at least 30 percent relative to traditional models. The framework also better predicts economic volatility, suggesting that machine learning techniques could be an important part of the macro forecasting toolkit of many countries.
Econometrics --- Forecasting --- Intelligence (AI) & Semantics --- Forecasting and Other Model Applications --- Neural Networks and Related Topics --- Classification Methods --- Cluster Analysis --- Principal Components --- Factor Models --- Technological Change: Choices and Consequences --- Diffusion Processes --- Econometrics & economic statistics --- Machine learning --- Economic Forecasting --- Factor models --- Economic forecasting --- Econometric analysis --- Technology --- Econometric models --- Turkey
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Interrogates the development of the world's first international courts of humanitarian justice and the subsequent "liberation" of nearly two hundred thousand Africans in the nineteenth century.
Slavery --- Freedmen --- Ex-slaves --- Freed slaves --- Slaves --- Abolition of slavery --- Antislavery --- Enslavement --- Mui tsai --- Ownership of slaves --- Servitude --- Slave keeping --- Slave system --- Slaveholding --- Thralldom --- Crimes against humanity --- Serfdom --- Slaveholders --- History --- Law and legislation --- Freedpersons --- Freed persons --- Ex-enslaved persons --- Freed enslaved persons --- Enslaved persons --- Abolition. --- African Consequences. --- British Colonies. --- Comparative Study. --- Emancipation. --- Global Legacy. --- Humanitarian Justice. --- International Courts. --- Liberated Africans. --- Nineteenth Century. --- Slave Trade. --- Slave-Plantation Economies. --- Slavery.
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With public debt soaring across the world, a growing concern is whether current debt levels are a harbinger of fiscal crises, thereby restricting the policy space in a downturn. The empirical evidence to date is however inconclusive, and the true cost of debt may be overstated if interest rates remain low. To shed light into this debate, this paper re-examines the importance of public debt as a leading indicator of fiscal crises using machine learning techniques to account for complex interactions previously ignored in the literature. We find that public debt is the most important predictor of crises, showing strong non-linearities. Moreover, beyond certain debt levels, the likelihood of crises increases sharply regardless of the interest-growth differential. Our analysis also reveals that the interactions of public debt with inflation and external imbalances can be as important as debt levels. These results, while not necessarily implying causality, show governments should be wary of high public debt even when borrowing costs seem low.
Exports and Imports --- Financial Risk Management --- Macroeconomics --- Public Finance --- Intelligence (AI) & Semantics --- Interest Rates: Determination, Term Structure, and Effects --- Fiscal Policy --- International Lending and Debt Problems --- International Finance Forecasting and Simulation --- Debt --- Debt Management --- Sovereign Debt --- Financial Crises --- Technological Change: Choices and Consequences --- Diffusion Processes --- Personal Income, Wealth, and Their Distributions --- Public finance & taxation --- International economics --- Economic & financial crises & disasters --- Machine learning --- Public debt --- External debt --- Financial crises --- Personal income --- Debts, Public --- Debts, External --- Income --- United States
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This paper applies state-of-the-art deep learning techniques to develop the first sentiment index measuring member countries’ reception of IMF policy advice at the time of Article IV Consultations. This paper finds that while authorities of member countries largely agree with Fund advice, there is variation across country size, external openness, policy sectors and their assessed riskiness, political systems, and commodity export intensity. The paper also looks at how sentiment changes during and after a financial arrangement or program with the Fund, as well as when a country receives IMF technical assistance. The results shed light on key aspects on Fund surveillance while redefining how the IMF can view its relevance, value added, and traction with its member countries.
Finance: General --- Macroeconomics --- Industries: Financial Services --- Intelligence (AI) & Semantics --- International Monetary Arrangements and Institutions --- International Policy Coordination and Transmission --- Data Collection and Data Estimation Methodology --- Computer Programs: Other Computer Software --- Commodity Markets --- General Financial Markets: Government Policy and Regulation --- Technological Change: Choices and Consequences --- Diffusion Processes --- Financial Institutions and Services: General --- Finance --- Machine learning --- Commodity price fluctuations --- Financial Sector Assessment Program --- Financial sector --- Commodity prices --- Prices --- Technology --- Economic sectors --- Financial sector policy and analysis --- Financial services industry --- Canada
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This paper studies the effect of digitalization on the perception of corruption and trust in tax officials in Africa. Using individual-level data from Afrobarometer surveys and several indices of digitalization, we find that an increase in digital adoption is associated with a reduction in the perception of corruption and an increase in trust in tax officials. Exploiting the exogeneous deployment of submarine cables at the local level, the paper provides evidence of a negative impact of the use of Internet on the perception of corruption. Yet, the paper shows that the dampening effect of digitalization on corruption is hindered in countries where the government has a pattern of intentionally shutting down the Internet, while countries that successfully promote information and communication technology (ICT) enjoy a more amplified effect.
Labor --- Public Finance --- Industries: Information Technololgy --- Criminology --- Bureaucracy --- Administrative Processes in Public Organizations --- Corruption --- Taxation, Subsidies, and Revenue: General --- Information and Internet Services --- Computer Software --- Technological Change: Choices and Consequences --- Diffusion Processes --- Employment --- Unemployment --- Wages --- Intergenerational Income Distribution --- Aggregate Human Capital --- Aggregate Labor Productivity --- National Government Expenditures and Related Policies: General --- Education: General --- Corporate crime --- white-collar crime --- Information technology industries --- Labour --- income economics --- Public finance & taxation --- Education --- Digitalization --- Public employment --- Public financial management (PFM) --- Information technology --- Economic theory --- Finance, Public --- United States
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The COVID-19 pandemic has accelerated the shift toward digital services. Meanwhile, the race for technological and economic leadership has heated up, with risks of decoupling that could set back trade and growth and hinder the recovery from the worst global recession since the Great Depression. This paper studies the conditions under which a country may seek to erect barriers—banning imports or exports of cyber technologies—and in effect promote decoupling or deglobalization. A well-known result is that banning imports may be optimal in monopolistic sectors, such as the digital sector. The novel result of this paper is that banning exports can also be optimal, and in some cases superior, as it prevents technological diffusion to a challenger that may eventually become the global supplier, capturing monopoly rents and posing cybersecurity risks. However, export or import bans would come at a deleterious cost to the global economy. The paper concludes that fostering international cooperation, including in the cyber domain, could be key to avoiding technological and economic decoupling and securing better livelihoods.
Business and Economics --- Exports and Imports --- Models of Trade with Imperfect Competition and Scale Economies --- Trade Policy --- International Trade Organizations --- Technological Change: Choices and Consequences --- Diffusion Processes --- Trade: General --- Innovation --- Research and Development --- Technological Change --- Intellectual Property Rights: General --- Empirical Studies of Trade --- International economics --- Technology --- general issues --- Imports --- Exports --- Trade liberalization --- Trade balance --- International trade --- Commercial policy --- Balance of trade --- China, People's Republic of
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This paper considers the implications for developing countries of a new wave of technological change that substitutes pervasively for labor. It makes simple and plausible assumptions: the AI revolution can be modeled as an increase in productivity of a distinct type of capital that substitutes closely with labor; and the only fundamental difference between the advanced and developing country is the level of TFP. This set-up is minimalist, but the resulting conclusions are powerful: improvements in the productivity of “robots” drive divergence, as advanced countries differentially benefit from their initially higher robot intensity, driven by their endogenously higher wages and stock of complementary traditional capital. In addition, capital—if internationally mobile—is pulled “uphill”, resulting in a transitional GDP decline in the developing country. In an extended model where robots substitute only for unskilled labor, the terms of trade, and hence GDP, may decline permanently for the country relatively well-endowed in unskilled labor.
Labor --- Macroeconomics --- Automation --- Macroeconomics: Production --- Macroeconomic Analyses of Economic Development --- Innovation --- Research and Development --- Technological Change --- Intellectual Property Rights: General --- One, Two, and Multisector Growth Models --- Technological Change: Choices and Consequences --- Diffusion Processes --- Professional Labor Markets --- Occupational Licensing --- Labor Economics: General --- Wages, Compensation, and Labor Costs: General --- Robotics --- Labour --- income economics --- Unskilled labor --- Labor share --- Skilled labor --- Technology --- Labor market --- Labor economics --- Wages --- United States
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This paper analyzes the legal foundations of central bank digital currency (CBDC) under central bank and monetary law. Absent strong legal foundations, the issuance of CBDC poses legal, financial and reputational risks for central banks. While the appropriate design of the legal framework will up to a degree depend on the design features of the CBDC, some general conclusions can be made. First, most central bank laws do not currently authorize the issuance of CBDC to the general public. Second, from a monetary law perspective, it is not evident that “currency” status can be attributed to CBDC. While the central bank law issue can be solved through rather straithforward law reform, the monetary law issue poses fundmental legal policy challenges.
Business and Economics --- Banks and Banking --- Finance: General --- Money and Monetary Policy --- Public Finance --- Industries: Financial Services --- Monetary Systems --- Standards --- Regimes --- Government and the Monetary System --- Payment Systems --- Monetary Policy --- Central Banks and Their Policies --- Remittances --- International Monetary Arrangements and Institutions --- Development Planning and Policy: Trade Policy --- Factor Movement --- Foreign Exchange Policy --- Technological Change: Choices and Consequences --- Diffusion Processes --- Taxation, Subsidies, and Revenue: General --- Distributed ledgers --- Monetary economics --- Banking --- Public finance & taxation --- Finance --- Central Bank digital currencies --- Currencies --- Central bank legislation --- Legal support in revenue administration --- Payment systems --- Technology --- Money --- Central banks --- Revenue administration --- Financial markets --- Financial services industry --- Technological innovations --- Revenue --- Clearinghouses --- Canada --- Construction industry.
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