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Recent evidence identiÖes second-moment shocks ñ often simply called “uncertainty” ñ as a key determinant of the business cycle. However, the key sources and propagation mechanisms of uncertainty remain a topic of debate. Resolving this debate is key for identifying the empirically relevant class of model for understanding the macroeconomic impact of uncertainty.
This paper explores the key sources and propagation mechanisms that lead uncertainty to a§ect the business cycle. We do so by exploring which technological characteristics lead industries to grow asymmetrically in times of higher uncertainty. Our aim is to explore the link between industry growth and aggregate uncertainty in a systematic way so as to identify the empirically relevant class of theory for modeling the macroeconomic impact of uncertainty
The motivation behind our exercise is as follows. The technology of production varies across industries based on factors such as the intensity with which di§erent inputs are used, the properties of those inputs, and so on. If a given technological characteristic interacts systematically with the factors that lead to changes in uncertainty, or if it is one that becomes harder to adjust in times of uncertainty, then growth in industries with that technological characteristic will be more sensitive to changes in measured uncertainty. This indicates that the characteristic in question tells us about the key sources or mechanisms linking uncertainty with growth. On the other hand, if this is not a factor that interacts with uncertainty, or if this is an easily adjusted factor in the production function, no such sensitivity will be detected, indicating this characteristic is not empirically important for uncertainty. As a result, any measurable interaction between an industry technological characteristic and a measure of uncertainty is a diagnostic as to where to look to understand the sources or macroeconomic impact of uncertainty.
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