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Adding space to the international business cycle
Institution:1. CEMFI, 28014 Madrid, Spain;2. The World Bank Group, Washington, DC, 20433 USA;1. Vienna University of Economics and Business, Austria;2. Wittgenstein Center for Demography and Global Human Capital (IIASA,VID/OEAW,WU), Austria;3. International Institute of Applied Systems Analysis (IIASA), Austria;4. Austrian Institute of Economic Research (WIFO), Austria;5. Halle Institute for Economic Research (IWH), Germany;6. University of Leipzig, Germany;1. Federal Reserve Bank of St. Louis, United States;2. School of Business, East China University of Science and Technology, China;3. Institute of Public Finance and Taxation, School of Finance, Renmin University of China, China
Abstract:Growth fluctuations exhibit substantial synchronization across countries, which has been viewed as reflecting a global business cycle driven by shocks with worldwide reach, or spillovers resulting from local real and/or financial linkages between countries. This paper brings these two perspectives together by analyzing international growth fluctuations in a setting that allows for both global shocks and spatial dependence. Using annual data for 117 countries over 1970–2016, the paper finds that the cross-country dependence of aggregate growth is the combined result of global shocks summarized by a latent common factor and spatial effects accruing through the growth of nearby countries – with proximity measured by bilateral trade linkages or geographic distance. The latent global factor shows a strong positive correlation with worldwide TFP growth. Countries’ exposure to global shocks is positively related to their openness to trade and the degree of commodity specialization of their economies, and negatively to their financial depth. Despite its simplicity, the empirical model fits the data well. Ignoring the cross-country dependence of growth, by omitting spatial effects or common shocks (or both) from the analysis, leads to a marked deterioration of the empirical model’s in-sample explanatory power and out-of-sample forecasting performance.
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