| Summary: | How Important Are Sectoral Shocks: I quantify the contribution of sectoral shocks to business cycle fluctuations in aggregate output. I develop a multi-industry general equilibrium model in which each industry employs the material and capital goods produced by other sectors, and then estimate this model using data on U.S. industries' sales, output prices, and input choices. Maximum likelihood estimates indicate that industry-specific shocks account for nearly two-thirds of the volatility of aggregate output, substantially larger than previously assessed. Identification of the relative importance of industry-specific shocks comes primarily from data on industries' intermediate input purchases, data that earlier estimations of multi-industry models have ignored.
Materials Prices and Productivity: There is substantial within-industry variation in the prices that plants pay for their material inputs. Using plant-level data from the U.S. Census Bureau, I explore the consequences and sources of this variation in materials prices. For a sample of industries with relatively homogeneous products, the standard deviation of plant-level productivity would be 7% smaller if all plants faced the same materials prices. Moreover, plant-level materials prices are persistent, spatially correlated, and positively associated with the probability of exit. The contribution of entry and exit to aggregate productivity growth is smaller for productivity measures that are purged of materials price variation. After documenting these patterns, I discuss three potential sources of materials price variation: geography, differences in suppliers' marginal costs, and within-supplier markup differences. Together, these variables explain 15% of the variation of materials prices.
Sources of Variation in Social Networks: What explains the large variation in the number of contacts (degree) that different participants of social networks have: age, randomness, or some unobservable fitness measure? To answer this question, I extend the model presented in Jackson and Rogers (2007) to allow individuals to vary in their ability to attract contacts. I estimate the parameters of the extended model, using a social network of citations among high-energy physics papers, and find that the extended Jackson-Rogers model can parsimoniously fit the degree distribution of each age cohort. Moreover, both the length of time spent in the network and the unobservable fitness measure are important in explaining the observed variation in participants' degrees.
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