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.
Timely data availability is a long-standing challenge in policy-making and analysis for low-income developing countries. This paper explores the use of Google Trends’ data to narrow such information gaps and finds that online search frequencies about a country significantly correlate with macroeconomic variables (e.g., real GDP, inflation, capital flows), conditional on other covariates. The correlation with real GDP is stronger than that of nighttime lights, whereas the opposite is found for emerging market economies. The search frequencies also improve out-of-sample forecasting performance albeit slightly, demonstrating their potential to facilitate timely assessments of economic conditions in low-income developing countries.
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.
Timely data availability is a long-standing challenge in policy-making and analysis for low-income developing countries. This paper explores the use of Google Trends’ data to narrow such information gaps and finds that online search frequencies about a country significantly correlate with macroeconomic variables (e.g., real GDP, inflation, capital flows), conditional on other covariates. The correlation with real GDP is stronger than that of nighttime lights, whereas the opposite is found for emerging market economies. The search frequencies also improve out-of-sample forecasting performance albeit slightly, demonstrating their potential to facilitate timely assessments of economic conditions in low-income developing countries.
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