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We identify key factors, from large set of potential determinants, that explain the variation in export diversification across countries and over time using Bayesian Model Averaging (BMA), which addresses model uncertainty and ranks factors in order of importance vis-a-vis their explanatory power. Our analysis suggests, in order to diversify, policy makers should prioritize human capital accumulation and reduce barriers to trade. Other policy areas include improving quality of institutions and developing the financial sector. For commodity exporters reducing barriers to trade is the most important driver of diversification, followed by improving education outcomes at the secondary level and financial sector development.

Commercial policy. --- Foreign trade policy --- International trade --- International trade policy --- Trade policy --- Economic policy --- International economic relations --- Government policy --- Investments: Commodities --- Exports and Imports --- Labor --- Natural Resources --- Multiple or Simultaneous Equation Models: Models with Panel Data --- Globalization: Economic Development --- Trade: General --- Agricultural and Natural Resource Economics --- Environmental and Ecological Economics: General --- Commodity Markets --- Human Capital --- Skills --- Occupational Choice --- Labor Productivity --- International economics --- Environmental management --- Investment & securities --- Labour --- income economics --- Export diversification --- Natural resources --- Commodities --- Exports --- Human capital --- Environment --- Commercial products --- China, People's Republic of

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This paper assesses the quantitative importance of including sectoral heterogeneity in computing the gains from trade. Our framework draws from Caliendo and Parro (2015) and has sectoral heterogeneity along five dimensions, including the elasticity of trade to trade costs. We estimate the sectoral trade elasticity with the Simonovska and Waugh (2014) simulated method of moments estimator and micro price data. Our estimates range from 2.97 to 8.94. Our benchmark model is calibrated to 21 OECD countries and 20 sectors. We remove one or two sources of sectoral heterogeneity at a time, and compare the gains from trade to the benchmark model. We also compare an aggregate model with a single elasticity to the benchmark model. Our main result from these counterfactual exercises is that sectoral heterogeneity does not always lead to an increase in the gains from trade, which is consistent with the theory.

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In recent years, Fund staff has prepared cross-country analyses of macroeconomic vulnerabilities in low-income countries, focusing on the risk of sharp declines in economic growth and of debt distress. We discuss routes to broadening this focus by adding several macroeconomic and macrofinancial vulnerability concepts. The associated early warning systems draw on advances in predictive modeling.

Central African Republic --- Business and Economics --- Macroeconomics --- Economics: General --- International Economics --- Financial Risk Management --- Inflation --- Banks and Banking --- Forecasting and Other Model Applications --- Monetary Policy --- Financial Crises --- Macroeconomic Analyses of Economic Development --- Business Fluctuations --- Cycles --- Price Level --- Deflation --- Financing Policy --- Financial Risk and Risk Management --- Capital and Ownership Structure --- Value of Firms --- Goodwill --- Commodity Markets --- Economic & financial crises & disasters --- Economics of specific sectors --- Financial crises --- Economic sectors --- Prices --- Banking crises --- Early warning systems --- Commodity prices --- Currency crises --- Informal sector --- Economics --- Crisis management